A Comprehensive Review of Artificial Intelligence (AI) Companies in the Power Sector

Abstract

There is an ongoing, revolutionary transformation occurring across the globe. This transformation is altering established processes, disrupting traditional business models and changing how people live their lives. The power sector is no exception and is going through a radical transformation of its own. Renewable energy, distributed energy sources, electric vehicles, advanced metering and communication infrastructure, management algorithms, energy efficiency programs and new digital solutions drive change in the power sector. These changes are fundamentally altering energy supply chains, shifting geopolitical powers and revising energy landscapes. Underlying infrastructural components are expected to generate enormous amounts of data to support these applications. Facilitating a flow of information coming from the system′s components is a prerequisite for applying Artificial Intelligence (AI) solutions in the power sector. New components, data flows and AI techniques will play a key role in demand forecasting, system optimisation, fault detection, predictive maintenance and a whole string of other areas. In this context, digitalisation is becoming one of the most important factors in the power sector′s transformation process. Digital solutions possess significant potential in resolving multiple issues across the power supply chain. Considering the growing importance of AI, this paper explores the current status of the technology’s adoption rate in the power sector. The review is conducted by analysing academic literature but also by analysing several hundred companies around the world that are developing and implementing AI solutions on the grid’s edge.

1. Introduction

Digitalisation is fundamentally transforming the world we live in. It has a growing impact on society, affecting people both at national and international levels [1]. Nationally, digitalisation and the accompanying process of automation are likely to cause a shift from low-skilled to high-skilled occupations [2]. Internationally, similar to the shift towards renewable energy, it may alter the delicate power balance among nations. Digitalisation has been affecting both our personal and professional lives for years and will continue to do so with even stronger implications in the future [3]. Similarly, the energy sector has been going through a transformation of its own [4]. After the dissolution of vertically integrated utility business models [5], the energy sector has been presented with a new set of challenges regarding environmental issues and the surge of renewable energy sources [6]. The phase of reaching a zero-carbon power system is still far from completion, yet we are now witnessing a dawn of a new era [7]. It is hard to define how this new period might be labelled, but what’s certain is that it will be shaped by the progress of digitally-enabled solutions [8]. During the following years, digitalisation will be a key facilitator of the new energy transformation [9]. The energy system is a complex behemoth consisting of countless elements, all working together to provide a reliable supply of energy in real-time. In the future, this system will become even more complex as the traditional centralised flow of energy is disrupted by the emergence of new distributed sources [10]. Electric vehicles (EVs), battery systems, smart appliances and the electrification of buildings will put additional strain on the distribution part of the power supply chain by adding new components, introducing new market players and facilitating bi-directional flows of energy [11].

The intricacies that arise when trying to maintain the delicate supply-demand balance will require the utilisation of complex, automated algorithms that are able to optimise the system in real-time [12]. In order to make informed decisions, these algorithms will need to process vast amounts of data. In this sense, the relatively recent rise of supercomputing and Big Data has empowered the development of various solutions based on Artificial Intelligence (AI) [13]. As the power supply chain gradually becomes more complex, the application of automated, advanced techniques in system management will become a necessity [14]. It is important to note that this will not be an unimaginable leap as techniques such as artificial neural networks (ANNs), reinforcement learning (RL), genetic algorithms (GAs) and multi-agent systems are all commonly explored as tools in forecasting, optimisation and control of the power system [15]. Currently, due to the lack of advanced automation infrastructure, a number of system operations are still performed at basic levels of automation [16]. According to a growing number of research papers, technical reports and case studies, AI will play a crucial role in the power system of the future, limiting manual interventions and introducing advanced techniques of system optimisation [17]. At present, most papers study the application of AI in the process of power flow optimisation. In this sense, AI will essentially be used to convert data gathered from the power system into useful information, which will then be used to formulate concrete steps in optimising system operation. However, as evidenced by various research papers and numerous start-ups, AI’s role in the power system will not be limited to power flow optimisation. Areas such as fault detection, predictive maintenance, demand response, forecasting and customer relations all have significant potential for AI implementation.

Motivation, Related Works and Scope

AI-based solutions are increasingly utilised across various segments of the power sector. The expanding interest in AI can also be evidenced by the surge of research interest in the field. The aforementioned trend necessitates the need to create a systematic overview of the application fields of AI in the power sector. In this context, there are a number of review papers that explore AI approaches to solving issues related to the power system. Ahmad et al. provide a comprehensive review of AI in energetics systems identifying seven key areas of its application [18]. In another paper, Ahmad et al. explore recent advances in AI technology and focus on a narrower application of AI in balancing supply and demand and generating power from solar and hydrogen [19]. Considering papers with slightly higher impact factors, we identified another two comprehensive reviews that look into the entire power sector. Ali et al. analysed AI techniques supporting distributed smart grids [16], while Kumbhar et al. focused on machine learning (ML) applications in the smart grid and the renewable energy sector [20]. Given the large number of areas in which AI can aid the power sector, the majority of reviews focus on a certain aspect of potential AI application rather than taking a holistic approach to the matter. Antonopoulos et al. focus on ML techniques used in demand response [21], Zhang et al. provide a review on the deep learning (DL) approach in frequency analysis and system operation management [22], while Hou et al. [23] and Yazici et al. [24] explore demand forecasting techniques. Other notable reviews look into AI applications regarding the performance of clean energy communities [25], integration of renewable energy sources (RES) in the existing power system [26,27,28], supply chain automation [29], smart buildings [30], implementation of smart meter infrastructure [31], regulatory issues that AI technologies face [32] and the data AI uses [33]. Security issues of AI in the power system are a standalone field that requires a focus of its own [34,35]. Figure 1 shows the surge of research papers focused on the topic of artificial intelligence. The figure has two parts (left and right). The left part is obtained by listing all Scopus-indexed research papers containing the keyword “artificial intelligence”. The right part is obtained by listing all research papers that contain the keywords “artificial intelligence” and “power system”.

Observing previously published research in the field, a significant number of papers offering reviews on AI-based techniques in the power sector can be found. Given a large number of possible areas of application, the majority of papers focus on a particular segment of AI implementation, such as predictive maintenance, demand response, forecasting energy demand, etc. Despite numerous reviews already published, there is a surprising lack of papers that aim to construct a systematic, high-level analysis of all the main fields of AI applications in the power sector. The main aim of the research presented is to establish what are the key areas of research focus regarding the implementation of AI-based solutions in the power sector. Taking it a step further, the paper aims to integrate the analysis of existing academic knowledge with real-life business applications. Naturally, there is a significant number of large, energy-based companies, such as Enel, Engie, ABB, Schneider, General Electric, Honeywell, etc., that have, in recent years, developed strong AI competencies. However, the research presented in the paper focuses only on companies whose core business is artificial intelligence. Within this framework, over 450 companies across the world were observed during the market analysis part of the research. From these companies, a list of promising start-ups and well-established companies is compiled, giving the answer to which fields of application are getting the most traction. To sum up, and against the aforementioned background, the major contribution of the paper is a systematic review of the main AI-based solutions in the power sector that integrates academic knowledge with practical applications. To the best of our knowledge, this is the most comprehensive review of AI-based companies in the power sector to this date (as far as academic papers are concerned). The following paragraphs have a two-fold goal:

Provide a comprehensive, systematic literature review of the current research in the field of AI solutions in the power sector and identify key areas of the potential application of AI technology in the field.

Taking the scientific literature review further, the paper provides a structured insight into the adoption rate of AI technologies in the power sector by observing the current efforts of established companies and start-ups.

With regards to the power sector, in this paper, we provide (1) a systematic review of the main fields of research regarding AI-based solutions and (2) a comprehensive analysis of current AI applications in real life. The introduction segment opens the topic of AI in the power sector and reveals key aspects of the paper. Paragraph 2 reveals the method used during the analysis. Paragraph 3 offers a literature review and a systematic analysis of the main field of research regarding AI solutions in the power sector. Paragraph 4 analyses the current AI adoption rate in the power sector by observing AI companies. Finally, Paragraph 5 gives a final conclusion to the topic.

2. Method

The first part of the paper presents an academic literature review. Its aim is to outline the most researched topics regarding the application of AI-based solutions in the power sector. The process of identifying relevant literature follows a four-stage progression:

• -. Identification by keywords and titles. The key tool used for identifying relevant literature has been the Scopus database. There were three main criteria during the paper selection process of stage one: journal impact factor, citations and year of publication.

• -. Inclusion of related work. Additional documents were included based on key references in papers from stage one.

• -. Selection by abstract. The abstracts of each paper selected during phase one were examined.

• -. Selection by full text. Each selected paper has been examined. Key methods and focus areas were identified.

During the process, a number of queries were utilised. Search queries were formed by combining several of the key search terms from Figure 2. For example: “Artificial Intelligence” (or “Machine Learning” or “Deep Learning”) AND “Forecasting” AND “Demand”. The results obtained have been carefully reviewed and filtered. Our research confirmed the existence of numerous research papers analysing potential AI solutions in the power sector. As the paper focuses on the entire power sector, only a portion of them are referenced. However, the results obtained closely relate to actual research efforts and researchers’ focus areas.

The second part of the paper integrates academic literature findings with a market analysis of real-life applications by firms operating in the sector. The list of companies developing AI solutions was obtained via multiple sources. Some companies are listed in other research papers, such as [21] and [36]. Some companies are listed in various databases, such as ai-startups [37], tracxn [38], Omdena [39], startus-insights [40], and crunchbase [41]. A number of companies were identified via news segments and web pages. Overall, around 500 start-ups and companies were examined during the market analysis. After disregarding inactive companies and identifying the ones whose core business is AI, a list of 220 companies was formed. To this date, and to the best of our knowledge, the list presented in Appendix A is the most extensively published in any research paper.

3. Research on AI Approaches in the Power Sector

As said by one of the bibles of AI [42], “we call ourselves Homo sapiens—man the wise—because our intelligence is so important to us”. The study of AI does not simply aim to develop an understanding of how our cognitive reasoning works but to replicate its processes in order to automate solving complex issues we face on a daily basis. Despite being considered a relatively new field in science, AI has been present in our minds for quite some time before it was established as an academic discipline in the 1950s [43] when Alan Turing published a ground-breaking article in which he described an intelligent machine and proposed a method on how to test its intelligence [44]. However, it remained a somewhat obscure area with limited practical interest up until recent times when advances in the processing power of our computers and the rise of data collection started bringing AI closer to our lives. This is an expansive, ongoing process involving a multidisciplinary approach. The fundamental ideas of AI lie in philosophy and the study of how we perceive our environment, process information and make the “right decisions”, but in order to make a step forward towards standardisation of these concepts, the introduction of logic, probability and computation is needed.

AI is a multidisciplinary domain that utilises various knowledge bases, which range from information technology and engineering to neuroscience or finance. During decades of research, multiple approaches have been used in the AI process. These approaches include but are not limited to, symbolic reasoning [45,46], statistical learning [47,48], Bayesian optimisation [49,50,51], soft computing [52,53,54], logic-based [55,56] and knowledge-based systems [57,58]. Most common, modern AI approaches in tackling power system issues can be divided into four categories [19]: machine learning (ML) [20], multi-agent systems [59,60], nature-inspired intelligence [61] and artificial neural networks (ANN) [62,63,64]. The term artificial intelligence is often interchanged with two other popular concepts: machine learning and deep learning (DL). Although closely related, these techniques are not indistinguishable [65]. Machine learning is a subset of AI and is characterised by a learning process without explicit programming. In other words, ML describes AI methods that can automatically identify data patterns and then use these patterns to forecast future data in an uncertain environment [66,67]. ML can be classified into four categories: supervised, unsupervised, reinforced and evolved learning [68]. Deep learning, another familiar concept in the AI family, is a branch of machine learning methods [69]. DL typically involves the implementation of large neural networks (although this is not a prerequisite) [70]. DL is used to learn multiple levels of representation and abstraction and is developed to have the ability to process raw data [71]. DL gained recognition during the 2012 ImageNet Challenge, after which DNN/CNN/RNN algorithms started to get increasingly used by developers [72]. Figure 3 reveals which of the four main AI techniques are utilised in the papers reviewed in this paper.

As briefly mentioned in the introduction segment, the power sector is currently going through a radical transformation. This transformation is mainly driven by “3Ds”: decarbonisation, decentralisation and digitalisation. In essence, all three processes are heavily linked and interdependent. The power system of the future will be based on renewables and battery storage [73]. Renewable energy relies on distributed generation. In a power system composed of a large number of distributed sources, intelligent (smart) infrastructure plays a key role in optimising resource management. Digitally-enabled solutions are, thus, critical in sustaining our decarbonisation efforts. AI is seen as an emerging technological field, an innovation front and an enabling technology [74]. Advances in computational power and AItechniques enabled AI to play a notable role in global business arenas [75]. AI technology rapidly progresses and is accompanied by a growing research presence—the power sector is not an exception. In this context, AI-aided solutions can find their place in all four segments of the energy supply chain: generation, transmission, distribution and supply [76].

3.1. Forecasting

Reliability is one of the key aspects of a successful energy supply chain. To ensure grid stability, power consumption and generation need to be in constant equilibrium. Balancing countless components with time-dependent outputs and/or demands is an extremely difficult task to manage. This is particularly emphasised when techno-economic constraints of the generation portfolio are taken into consideration. What further complicates things is the surge of variable RES. These generation capacities have intermittent outputs that are oftentimes difficult to predict and challenging to incorporate in hourly dispatch curves. In addition, the traditional energy system is based on a centralised flow of energy from large producers (typically thermal power plants) to consumers. However, the emergence of distributed energy sources and prosumers is disrupting this paradigm and introducing bidirectional power flows. In this context, the transition from a traditional power system to a decentralised, smart system of the future will necessarily entail the implementation of advanced management software, as shown in Figure 4.

Uncertainty regarding the variability of both demand and generation significantly increases risks and costs associated with the reliable provision of energy. In the past, the main solution to the issue of variability was to install backup generation capacities. However, the rise of digitally-enabled solutions introduces algorithms that aim to forecast the potential supply-demand curve and optimise assets to achieve optimal dispatch. In this context, forecasting has been one of the first areas of AI application in the power sector. Short-term forecasting of electricity demand is already a well-established topic and has been researched for over two decades [77,78]. Studies in short-term demand forecasting have now started to shift towards exploring smart grids and smart buildings [79]. AI techniques are also being applied in forecasting medium and long-term demand levels using different approaches such as fuzzy logic [80], multivariate adaptive regression splines (MARS), artificial neural network (ANN) and linear regression (LR) methods [81].

In addition, the process of predicting generation outputs from variable renewables is receiving an increasing amount of attention. Successful prediction of wind or solar power plant output requires the installation of smart sensors and the integration of this data with GPS-based weather forecasting platforms. AI methods have been researched regarding solar units using the adaptive network-based fuzzy inference system (ANFIS) [82] and utilising ML [83] and ANN approaches [84]. ML [85] and ANN [86] approaches are also used in forecasting wind power production. Long-term forecasts on wind power are also a topic of strong researchers’ interest [87]. There are several studies that confirm that the application of AI-based algorithms is able to notably improve the accuracy of predicting wind [88,89] and solar generation [90,91]. In addition to forecasting demand or production outputs, AI techniques are also commonly explored in order to predict the prices of energy commodities [92] and electricity prices [93,94,95].

3.2. Optimisation

As evidenced by our research, optimisation applications in the power sector can be divided into asset optimisation and system-level optimisation. In this context, asset optimisation refers to the process of operational optimisation of a single unit, such as a power plant, a grid component, a storage system, a home energy management system (HEMS), and/or a building heating, ventilation and air conditioning (HVAC) system. There are numerous papers analysing how to augment the efficiency of variable renewables such as solar [96,97] and wind power plants [98,99]. Other papers focus on traditional power plants [100], grid assets [101] and energy storage systems [102]. Looking at the consumer side of the equation, papers focus on HVAC systems [103], energy efficiency in buildings [104] and HEMSs [105]. In addition to operational optimisation, AI-aided solutions also focus on predictive maintenance and fault detection.

Another large AI research area refers to energy resource management. System-level optimisation focuses on a broader area optimising power flows and dispatch schedules. In this context, there is a considerable amount of papers that focus on demand response (DR) [19]. Some papers focus on the demand response of households [106], some explore buildings [107], while others analyse industrial facilities [108]. In addition, there are three other, relatively novel areas where AI optimisation is researched: virtual power plants (VPPs) [109], battery systems [110] and e-mobility [111]. With the emergence of prosumers and distributed energy sources (DERs), the virtual power plant concept is quickly gaining momentum [112]. AI regarding VPPs is explored in the context of schedule optimising [113] and forecasting flexibility [114]. Considering the surge of distributed sources and variable renewables, AI is increasingly becoming an important segment of system operation and control. Figure 5 reveals a typical fault occurrence in a power grid and distinguishes how primary, secondary and tertiary control help in bringing the system back in balance. It also outlines the main field of operation for an AI-aided system that can automate and optimise the recovery process. AI approaches show a distinct advantage in solving such complex problems in real-time [115].

Regarding battery storage, AI is used to explore digital twins in management systems [116], predict novel materials with designed properties [117] and facilitate the process of searching for novel electrochemical energy storage materials [118]. E-mobility is an important part of almost any sustainable development scenario. However, at present, it seems that the topic of e-mobility is not at the focus of the AI-based research community. In other words, there are not many papers that analyse how AI solutions can be applied to e-mobility. Research in this particular area is mostly focused on analysing how AI can help with electric vehicle (EV) mass adoption [119], how it can help with smart charging [120] and EV energy management and charging scheduling systems [121]. Taking it a step further from Figure 5 and Figure 6 reveals the key building blocks of an AI-aided energy management system that would help optimise the operation of the energy supply chain, taking into consideration the techno-economic aspects of power generation capacities, grid constraints and forecasts.

AI is also heavily researched regarding the topic of predictive maintenance in the power sector [122]. Predictive maintenance of smart grids [123], renewable energy systems [124], photovoltaic systems [125], wind farms [126,127] and distribution networks [128] is explored. In addition to the above-mentioned fields of research, there are numerous other AI-related topics regarding the power sector. AI-based solutions are explored regarding RES sizing options [129], fault detection [130,131], integration of RES [132] and energy efficiency [133].

3.3. Services

Observing literature regarding AI-aided services to consumers in the power sector, an evident gap can be noticed. Other sectors, particularly tech, media and telecom (TMT) and software have done two major things: (1) shifted their focus to being customer-centric companies that aim to provide a premium service [134] and (2) adopted an open innovation business model in which they create to facilitate the co-creation of value for their consumers [135]. Customer engagement via interactive AI platforms is a fast-growing field [136]. A recent study predicts that by 2025, 95% of consumer interactions might be powered by AI [137]. Although this could be hard to achieve, it is certainly an indication of where AI-aided customer engagement is heading. Businesses will have to adapt to the new, service-orientated reality or will risk becoming uncompetitive in the market. Incorporating AI-based customer support platforms is the first step towards creating an open ecosystem able to foster value co-creation. The key will be to engage customers by actively managing their expectations.

Electric utilities are having a difficult time switching their business models to being customer-centric [138] and are finding it even more difficult to create successful ecosystems. In other words, they keep offering a commodity instead of a service. Following suit, research on AI-aided services in the power sector is limited compared to other sectors [139]. Current research on the topic focuses on analysing active consumer participation in smart energy systems [140], assessing service quality and customer satisfaction [141] and analysing customer experience with chat boxes [142]. AI-aided customer relationship management (CRM) will help improve the ability to predict customer value and will improve the treatment of customers.

4. AI Applications in the Power Sector

Everything in AI is about prediction. In one way or another, AI is used to predict the future. It does so by comparing a string of predetermined conditions with two sets of data: past state and current conditions. The changes in data that have occurred during the time interval observed are used to determine the future state of the system. Given a specified set of parameters, AI generates actions to be taken to ensure optimal operation during this future interval. In the power sector, these actions are applied for various purposes, for power flow optimisation, predictive maintenance, security enhancement, customer support etc.

Applying AI-based solutions requires the use of a considerable amount of data. The term big data simply refers to extremely large datasets. Quality data has always been difficult to attain, but with the digitalisation of the energy system, an increasing amount of data is now being generated. According to a report by Cisco [143], in 2018, an average of 5 exabytes (10¹⁸ bytes) of data was generated each day. The power and scale of digitalisation are bringing our world closer together, and with increased interconnections and an exponentially larger number of smart devices, the generation of data will only become more significant. Some estimates predict that by 2025 over 460 exabytes of data will be generated each day [144]. When this abundance of information is paired with the increased processing power of today′s computers, an ideal foundation for the application of AI is created. As far as the energy sector goes, there will be several major new sources of data: smart meters installed at consumer, storage and producer locations, electric vehicles and numerous smart devices. The Internet of things (IoT) revolution is knocking on the doors of the energy industry, with estimates predicting growth from 25 billion IoT devices that exist today to 75 billion in just a five-year time frame [145].

Figure 7 presents a basic principle of the transactive energy management model where each physical asset has a corresponding virtual counterpart [135]. Digital twins such as the one presented transact in digital space while recording power flows that occur in the system. The key enabler of such a system is the existence of a digital platform. As shown in Figure 7, this platform would consist of three main layers: physical, infrastructure and business. These layers are further divided into six additional interoperability layers: business, functional, information, communication, integration and asset. The aforementioned layers are built upon the physical infrastructure of the power grid and enable a fully-digital peer-to-peer transaction network.

Power Sector’s AI Adoption Rate

As can be seen from the examples of early adopters, AI is destined to be one of the key enablers of the next wave of digital disruption. Facilitated by the rise of digital platforms and by tech giants such as Google, Facebook and Baidu, investments in AI are growing at a rapid pace. According to a recent report by McKinsey, in 2016, companies around the globe invested between USD 26 and 39 billion in AI [146]. The majority of the funding came from tech giants who invested between USD 20 and 30 billion in research, development and deployment of AI systems. Outside the tech world, the adoption of AI is still in its early stages. Energy companies basically shun away from it despite the myriad of advantages AI might bring to the table. The lethargic approach to developing and adopting new solutions is one of the reasons why most utilities have been underperforming in financial markets [147]. Further neglecting the digital reality will only increase the gap energy utilities face when compared to other businesses, such as software or the tech, media and telecom (TMT) industry [135]. However, despite certain headwinds and the slower rate of adoption, AI-aided solutions in the power sector are a rapidly growing field with different interest areas. Figure 8 reveal major areas of AI application in the power sector.

Currently, the biggest focus is directed towards four major fields of interest:

• (1). Optimisation of assets. According to our research, the majority of AI-based companies in the power sector focus on the application of various optimisation techniques. This field refers to the operational optimisation of particular assets within the system, such as renewable power plants, battery systems, buildings′ energy systems and/or home energy management systems. Companies developing applications in this area rely on business models that offer value propositions to residential and commercial users and power plant operators. It should be noted that this field of application does not only deal with power assets. A number of companies analysed use their solutions to integrate home or to build HVAC systems into their optimisation processes. In such a way, they offer an integrated solution to homeowners and companies.

• (2). Optimisation at the system level. This field refers to the management of power flows of the grid and tuning the supply-demand balance by adjusting the production of the generation portfolio, optimally utilising energy storage capacities and applying available demand response schemes. Companies that focus on system-level optimisation generally aim at offering their services to grid operators, utilities and power producers. Grid operators use AI-aided solutions to forecast renewable generation and energy demand and then use this data to optimise their dispatching schedules, and grid power flows. Power generation companies form virtual power plants through which they optimise their production schedules and minimise their exposure to market risk.

• (3). Data analytics. Data analysis and forecasts present the third largest field of AI application in the power sector. Naturally, all AI-based solutions require the use of some form of data analysis. However, companies listed as “optimisers” in the above two fields analyse data to form actions that automatically influence power assets. Companies in the segment of "Data analytics" form insights that are then used for further analysis and form a base for the decision-making process but do not autonomously optimise infrastructural assets. Companies focused on data analytics generally offer their services to utilities and the commercial sector via various monitoring platforms and to power generation companies and system operators that use data analytics in forecasting renewable generation and energy demand.

• (4). Operation and maintenance. O&M is a very common form of AI application in the power sector. Companies that deal with predictive maintenance generally offer their services to power generation companies and the industrial sector. The majority of use cases regarding this field refer to AI-aided analysis of drone or satellite imagery. This field of application also refers to the robotisation of the maintenance process, where (for instance) robots are used to clean PV panels or inspect wind turbines.

Apart from the applications listed in the four fields above, there are several other that have growth potential. The most prolific of these applications are the ones focused on the field of electric mobility. There are numerous companies that use AI-aided solutions regarding electric vehicles. However, in this research, we have considered the ones that focus on applications with a direct influence on the power grid. These generally include companies that develop platforms for the optimisation and/or integration of charging infrastructure and that analyse how to use renewable energy for the charging process. Companies that focus on autonomous driving, upgrading EV efficiency and/or improving battery life have been disregarded. Despite not making it into the top four applications, E-mobility is already on its way to widespread adoption. Looking at AI companies tied to the power sector, we believe that the future of the automotive industry is electric. However, this is a topic for another paper. Sixth on the list seems to be the application of customer service platforms. These help energy providers to understand and serve their customers, offering them a premium customer journey. In this context, the majority of companies use AI to unlock meter-level data, provide consumer insights and make business processes automated and smarter for energy retailers, utilities and grid operators.

Apart from the aforementioned fields, and as witnessed by our research, other popular areas of AI application in the power sector are cybersecurity and financial services. Cybersecurity is a key prerequisite for the enablement of AI. The increased data flows will inevitably raise associated security risks. As the future smart grid will become increasingly distributed, and as it will consist of countless IOT components and smart appliances, so will the entry points for malicious attacks increase. Applying AI-based architecture such as artificial neural networks (ANNs) is proving to mitigate the cybersecurity susceptibilities of the power system. Finally, the financial service segment of AI-aided applications in the power sector will ensure that distributed sources are more easily financeable. At present, around 30–40% of rooftop solar costs refer to soft costs. Aiding the design process and offering a financing solution can prove crucial in the more widespread adoption of solar rooftop solar panels, whether regarding households or commercial users. Figure 9 shows the spatial distribution of analysed companies.

5. Conclusions

The surge of distributed energy sources is causing fundamental changes to the traditional, centralised flow of energy in the power system. Variable renewable energy sources such as wind and solar are disrupting both the energy supply chain and the business models of the companies in the power sector. The increasing amount of energy being produced by these variable sources, paired with the gradual inclusion of electric vehicle charging stations and battery systems, significantly complicates the delicate supply-demand balance of power supply. These are the main reasons why we are witnessing a new era in the management of power. The colossal size and the grave importance of the power systems necessitate the need for the automation of energy management processes. This is where AI-aided technological solutions step in.

As evidenced by the work presented in this paper, the surge of variable renewables and distributed energy sources is accompanied by the surge of companies providing various AI solutions that aim to help solve the complex issues of the new power system. There are currently several major fields of AI application in the power sector. In this research, we have divided them into asset optimisation, system-level optimisation, data analytics and forecasts, predictive maintenance, e-mobility, customer support, cybersecurity and financial services. During our research, several hundred companies have been analysed. This resulted in a list of 220 AI-based companies operating in the power sector. To this date, and to the best of our knowledge, the research presented in the paper is the most comprehensive review of the actual, business-based application of AI in the power sector.

Research presented in the paper shows that the growing researcher interest in applying AI-based techniques in the power sector is accompanied by a growing number of companies following suit in real business applications. These companies are trying to implement numerous innovative solutions to all segments of the energy supply chain. In recent years, start-ups have been developing into successful companies. Many successful start-ups and companies have been acquired by incumbent companies in the sector. In such a way, incumbents combine their strengths with the agility of the start-up they acquired. Despite the lack of agility oftentimes seen in companies of the power sector, we are witnessing a new age in which the sector is becoming increasingly open towards newentrants and disruptors. This process is facilitated by structural changes in the power supply chain.

Business reports regarding other business sectors reveal that AI-based applications are most commonly used to support different types of service optimisations. In other words, they are focused on customers. Traditionally, power companies have always been focused on the process of providing a reliable source of energy. While other sectors, such as technology, media, and telecom (TMT), have shown more agility and switched to being customer-centric, most power companies have continued to maintain their traditional business model. This is the reason why many of them are failing to provide satisfactory financial returns—because they are still focused on providing a commodity, not a service. Research presented in this paper confirms that in the advent of the power sector′s digital age, the majority of digitally-equipped newentrants are still focused on augmenting the efficiency of the process rather than the service.

To provide a final conclusion, given the new energy paradigm driven by distributed energy sources, bidirectional flows and variable RES, it is essential to equip the distribution network with sensors that enable the application of data-driven AI techniques. AI will certainly play a major part in the future of the power sector. However, the successful implementation of these new solutions will take a considerable amount of time and significant effort. To follow the trends set by other industries, the power sector will have to shift its current focus from providing commodities to achieving an open innovation, customer-centric business model. Research presented in the paper shows that major steps have been taken, but further progress in this direction is yet to be made.

Author Contributions

Conceptualization, V.F. and A.V.; literature review, V.F. and D.M.; investigation and data acquisition V.F. and D.M.; paper writing and editing, V.F.; Supervision, A.V. All authors have read and agreed to the published version of the manuscript.

Data Availability Statement

Data obtained from numerous online sources.

Conflicts of Interest

The authors declare no conflict of interest.

Footnotes

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Figures

Figure 1. Research papers on AI (left) and AI in the power system (right).

Figure 2. Search methodology and key search terms.

Figure 3. AI techniques used in the reviewed literature.

Figure 4. The AI-aided new energy paradigm.

Figure 5. AI-aided fault recovery.

Figure 6. AI-aided Energy Management System.

Figure 7. Digitally-enabled transaction network as a basic layer of the smart grid platform.

Figure 8. (a) Main areas of application of AI companies in the power sector. (b) Main focus areas of AI companies in the power sector.

Figure 9. AI-based companies in the power sector across the Globe.

Appendix A

Table A1 lists 220 AI companies in the power sector. It categorizes them by name, country of origin, target market and field of interest according to Figure A1.

Figure A1. Explanation of Table A1.

Table A1

220 AI companies in the power sector in 2022. (websites last accessed on 5 January 2023).

1

Absolar

UK

https://www.absolar.co.uk/

R/C

Aids solar power plant design, scans sites to establish solar potential

DA

2

Accenta

France

https://www.accenta.ai/accueil

C

Optimizes low carbon heating and cooling needs of buildings

EE

HVAC

3

Actility

France

https://www.actility.com/

R

Carrier grade IoT connectivity platform

EM

AO

IOT

DR

HEMS

4

Adaptricity

Switzerland

https://www.adaptricity.com/en/

E

Cloud-based network analytics platform enables distribution system operators to better understand, operate, and plan their power grid infrastructure using data-driven network analytics

EM

DA

5

AIDI.solar

Ukraine

https://www.aidi.solar/

E

Developer and integrator of solutions for PV asset management and O&M

AO

DA

O&M

6

Ambyint

Canada

http://ambyint.com/

C/E

Ambyint developed an automation and analytics solution for artificial lift optimization that builds on traditional physics-based techniques

EE

DA

7

Amelia

USA

https://amelia.ai/

E

Creates personalized digital experiences for customers

8

Amog

Australia

https://amog.consulting/

E

Advanced hydrodynamic analysis, dynamic cable analysis and static cable stability as well as a range of solutions regarding RES

AO

O&M

9

Amperon

USA

https://amperon.co/

E

Provides AI-based smart meter analytics for utilities

DA

10

Annea

Germany

https://annea.ai/

E

Provides predictive maintenance solutions for renewable energy assets such as wind turbines, solar farms, and hydropower plants

O&M

11

Argentum

Canada

https://www.argentum.ai/

C

Real-time monitoring and control of buildings

EE

HVAC

12

Ari analytics

China

https://www.ari-analytics.com/

E

Solar power plant production forecast and optimization, data-driven EE optimization of buildings

AO

EE

DA

13

Arloid Automation

UK

https://arloid.com/

C

Helps cut energy costs with a solution that automatically adjusts HVAC settings in buildings based on changing environmental conditions

HVAC

14

Arundo Analytics

USA

https://www.arundo.com/

C

The company uses data and predictive solutions to reduce maintenance costs, improve revenue, and avoid outages for industrial companies

O&M

15

Aurora Solar

Canada

https://aurorasolar.com/

R/C

Develops cloud-based software that aids solar PV engineering design

DA

16

AutoGrid

USA

https://www.auto-grid.com/

R

AutoGrid integrates all distributed energy resources by the use of flexibility management

EM

VPP

DR

HEMS

17

BeeBryte

Singapore

https://www.beebryte.com/

C

EE for industrial cooling and HVAC

AO

EE

DR

HVAC

18

Beijing Rongxing Technology

China

https://www.mixislink.com/

C/E

Develops predictive maintenance solutions, and integrated EMS platforms

EM

VPP

O&M

19

Beyond Limits

USA

http://www.beyond.ai/

C/E

Beyond Limits is an Industrial and Enterprise grade AI technology company that covers the full range of Artificial Intelligence capabilities

EM

EE

VPP

O&M

20

Bidgely

USA

https://www.bidgely.com/

R/C

Customer service platform provider, energy analytics for distribution system management related programs

EM

AO

EE

DA

DR

HEMS

21

Blink Energy Inc.

USA

https://blinkenergy.co/

E

Develops a platform that provides utilities with real-time data collection, fault indication, and helps prevents ice on power lines

O&M

22

BlueWave

USA

https://www.bluewaveailabs.com/

E

Blue Wave AI Labs is using predictive analytics to operate nuclear reactors across the United States and to help them operate as safely and efficiently as possible

DA

O&M

23

BluWave-ai

Canada

https://www.bluwave-ai.com/

E

BluWave-ai uses artificial intelligence to improve operations for energy grids and renewable energy sources such as wind and solar farms

AO

O&M

24

BluWave-ai

Canada

https://www.bluwave-ai.com/

C/E

Energy optimization for smart grids and fleet electrification

M

AO

25

Bolt

India

https://bolt.earth/

C

Offers electric vehicle charging management solutions

M

26

BrainBox AI

Canada

https://brainboxai.com/en

C

Developed a fully autonomous commercial HVAC solution, BrainBox AI, that uses predictive and self-adapting AI to optimise a building’s HVAC system for maximum energy reduction

HVAC

27

Bueno

Australia

https://www.buenosystems.com.au/

C

Analytics and optimization solutions for building management

AO

DA

HVAC

28

Buzz Solutions

USA

http://buzzsolutions.co/

E

Buzz Solutions is developing artificial intelligence technology to spot and analyze power line flaws so companies can repair them before a fire starts

O&M

29

C3 AI

USA

https://c3.ai/

C/E

Uses machine learning techniques to enable accurate forecasting, benchmarking, building optimization, demand response, and anomaly detection. The company’s digital platform for utilities offers application for performance optimization for gas power plants, efficiency optimization of heating and cooling plants, energy analysis and management for enterprise and consumer customers, and anomaly detection of wind turbines.

AO

EE

DA

VPP

IOT

DR

HVAC

O&M

30

Carbon Relay

USA

https://www.carbonrelay.com/

C

Carbon Relay tackles data centre cooling with AI

EE

HVAC

31

Centrica

Belgium

https://www.centricabusinesssolutions.com/energy-solutions/products/energy-optimisation-solutions?redirect=restoreeu

C

Centrica’s REstore provides cloud-based demand side management. The technology is currently used by more than 150 industrial energy consumers in Europe.

DR

32

ChargePoint

USA

https://www.chargepoint.com/en-gb

C

Kisensum develops software to control and optimize energy resources, including photovoltaics, energy storage and charging stations for electric vehicles

M

AO

33

CIM

Australia

https://www.cim.io/

C

Data-driven building operations software

AO

HVAC

34

Circunomics

Germany

https://www.circunomics.com/

C/E

Optimizing second life and recycling of batteries

AO

35

Cleandrone

Spain

http://www.cleandrone.com/

E

Develops autonomous drones for thermal imaging and cleaning of solar panels and glass surfaces

O&M

36

CLEAResult

USA

https://www.clearesult.com/

R

The largest provider of emission-reducing energy solutions across North America

EM

EE

DR

HEMS

37

Cleveron

Switzerland

https://cleveron.ch/

C

Building EMS

IOT

HVAC

38

ClimaCell

USA

https://www.climacell.co/

C/E

Delivers weather insights helping to manage weather related challenges

39

Climatik

Mexico

https://climatik.net/

C/E

Meteorological data analysis

DA

40

Clobotics

China

https://clobotics.com/

E

Provides drone-based wind turbine inspection and monitoring repair and maintenance

O&M

41

COI Energy Services

USA

https://www.coienergyservices.com/

C/E

COI Energy Solutions deals with energy waste issues through its EMS platform improving energy performance of buildings

EE

HVAC

O&M

42

Coulomb AI

USA

https://coulomb.ai/

C/E

Optimizes batteries’ operation

AO

43

cove.tool

USA

https://www.cove.tools/

C

Provides data, automation, and collaborative tools to design better buildings

AO

44

Crusoe

USA

https://www.crusoeenergy.com/

E

The company’s behind-the-meter load approach enables alternative revenue streams for renewable and clean energy projects

AO

45

cyberGRID

Austria

https://www.cyber-grid.com/

C/E

cyberGRID offers innovative ICT-based flexibility management technology, integration of renewable energies and storage devices

EM

AO

VPP

DR

46

Dabbel

Germany

https://www.dabbel.eu

C

DABBEL controls the energy system in buildings reducing energy consumption

EE

HVAC

O&M

47

dcbel

Canada

https://www.dcbel.energy/

C

Develops AI-driven sustainable technologies which enable people to leverage solar energy to power their cars

M

48

DCbrain

France

https://dcbrain.com/

C/E

Detects anomalies and anticipates incidents, and uses data to predict future requirements

DA

49

DCSix Technologies

Ireland

https://www.dcsixtechnologies.com/

R/C

Provides an energy monitoring platform that help reduce energy consumption

HEMS

50

Deepmind

UK

https://deepmind.com/

C/E

Google’s DeepMind is the world leader in artificial intelligence research and its application in different fields, such as games, medicine, energy efficiency.

AO

EE

VPP

IOT

HVAC

51

DeepVolt

Tunisia

https://deepvolt.io/

C

AI-powered software for the placement and sizing of electric vehicle charging stations

M

52

Detect Technologies

India

https://detecttechnologies.com/

C/E

Identifies fault differences, workforce safety hazards, monitors process automation and possible risks, generates analytics on data consumption

EE

IOT

53

Dexter Energy

Netherlands

https://dexterenergy.nl

C/E

Dexter provides forecasting and dispatching solutions based on AI and cloud-based technology that increases efficiency and reduces cost

EM

DA

54

Dynamhex

USA

https://dynmhx.io/

E

Customer service platform provider, energy analytics

DA

55

EasyMile

France

https://easymile.com/

C

Develops autonomous driving systems and smart mobility solutions

M

56

Ecolibrium Energy

India

https://www.ecolibrium.io/

C

The company’s platform provides a holistic view of buildings’ energy profile, develops customer engagement systems

EE

DA

IOT

57

Ecotropy

France

https://ecotropy.fr/

C

Provides analyses of buildings’ energy performance and optimizes energy retrofit of buildings

HVAC

58

Effenco

Canada

https://www.effenco.com/

C

Develops electric hybrid systems for electrification of transport and electric powertrains

M

59

ei

Portugal

https://www.galpsolar.com/pt/

E

Develops solar energy monitoring solutions

O&M

60

Elevate

North Macedonia

https://elevate-global.biz/index.html

E

Develops autonomous energy forecasting solutions for utilities

DA

61

eleXsys

Australia

https://elexsys.com/

E

Develops AI-driven smart grid solutions

EM

62

Emuron

India

https://www.emuron.com/

C

Develops IoT-enabled battery management systems

M

63

Enercast

Germany

https://www.enercast.de/

E

Forecasts energy production

DA

64

Energiency

France

https://www.energiency.com/

C

Provides a platform for enterprises to optimize their EE

EE

DA

65

Energy Pool

France

https://www.energy-pool.eu/en/

C/E

Builds and operates demand-side management solutions

EM

VPP

DR

66

Energy X

South Korea

http://www.energyx.co.kr/

C

Developed an AI-driven platform that allows corporate and individual users to invest in renewable energy projects worldwide

67

Enerlogix

Spain

https://enerlogix.es/

C

Developed a platform offering HVAC management solutions for commercial applications

HVAC

68

Enervalis

Belgium

https://enervalis.com/

R/C/E

Developed a platform optimizes homes, buildings and electric vehicle charging stations

M

EM

AO

DA

VPP

IOT

DR

HEMS

69

Eneryield

Sweden

https://www.eneryield.com/

C/E

Develops solutions to help with fault forecasting, identification and localization, life-time estimation, energy loss estimation and grid health assessment

O&M

70

Enfor

Denmark

https://enfor.dk/

E

Develops software solutions for power generation forecasting, optimizes district heating systems

M

DA

DR

71

EnPowered

Canada

https://enpowered.com/

R/C

Facilitates companies to invest in energy efficiency solutions

EM

EE

DA

VPP

DR

72

Entelios

Norway

https://www.entelios.com/

R/C

EMS and trading

EM

DR

73

EQuota Energy

China

https://equotaenergy.com/en/

C

AI & Big Data supported EMS Service provider

AO

DA

74

Eve

Ireland

https://evE-mob.io/

C

Analytics solutions and emissions reporting to empower corporate fleet electrification

M

E-M

75

Evio

Portugal

https://go-evio.com/

C

Develops e-mobility solutions

M

76

Evolve Energy

USA

https://www.evolvemyenergy.com/

E

Developed the Kraken platform for customer management now licensed to E.ON, Npower, Origin Energy and EDF Energy and providing support to millions of customers. The company is also focused on providing flexibility and market access.

M

IOT

DR

77

Exergenics

Australia

https://www.exergenics.com/

C

Analytics solutions for building management

AO

DA

78

Flexitricity

UK

https://www.flexitricity.com/

C/E

Largest demand response aggregator in UK

EM

DR

79

Flutura

India

http://www.flutura.com/

E

Developed a platform Cerebra focused on improving asset uptime and operational efficiency

AO

O&M

80

Foghorn Systems

USA

https://www.foghorn.io/

C/E

FogHorn is an IoT platform that provides a complete edge solution consisting of a highly miniaturized complex event processing engine that derives real-time insights

DA

IOT

81

Fresh Energy

Germany

http://getfresh.energy/

R/C

Develops online building EMS solutions

IOT

HVAC

HEMS

82

Fulcrum3D

Australia

https://www.fulcrum3d.com/

E

Remote sensing, forecasting, data capture, and reporting for solar and wind power

DA

83

Future Grid

Australia

https://future-grid.com/

E

Real-time smart meter data analytics

DA

84

Gaiascope

USA

https://gaia-scope.com/

E

Provides forecasting software for energy storage systems and empowers customers to achieve bid and trading optimization for their power assets

AO

DA

85

Gbatteries

Canada

https://www.gbatteries.com/

C/E

Optimizes battery charging solutions for electric vehicles

M

86

Generac Grid Services

Canada

https://www.generac.com/

E

Manages grid assets and balance supply and demand in real time

EM

VPP

DR

87

GetJenny

Finland

https://www.getjenny.com/

E

Develops self-service solutions

88

Gilytics AG

Switzerland

https://www.gilytics.com/

E

Automates infrastructure planning, routing, and monitoring

DA

89

Glint Solar

Norway

https://www.glintsolar.ai/

E

Identifies and analyses optimal solar sites using satellite imagery

DA

90

Green Running

UK

https://verv.energy/

R

Predictive maintenance technology for smart, sustainable appliances through Verv, a home energy assistant which monitors energy use in a home to help cut costs and save energy

91

Greenbird

Norway

https://www.greenbird.com/

E

Big Data integration for utilities

DA

92

GreenPocket

Germany

https://www.greenpocket.com/

E

Develops an energy management system and visualization software

93

GreenWhale

Germany

https://thegreenwhale.com/

C

Operates private and public vehicle-to-grid charging infrastructure

M

94

Grid AI

Japan

https://gridpredict.jp/

E

Grid optimization

EM

95

GridBeyond

UK

https://gridbeyond.com/

C/E

Uses AI-powered technology to optimize system operation to facilitate savings and manage price volatility

M

EM

VPP

DR

96

GridIMP

UK

https://gridimp.com/

C

AI driven fully automated technology takes care of day-to-day EMS decisions

EM

AO

VPP

IOT

DR

HEMS

97

Gridium

USA

http://www.gridium.com

C

The company’s data platform automatically aggregates energy interval data, billing data, weather history data, and weather forecast data to power a rich set of analytic services

DA

98

Hank

USA

https://www.hank.re/

C

Hank is enabling service providers and building owners to proactively manage HVAC costs and improve EE of commercial buildings

EE

HVAC

99

Hive Power

Switzerland

https://www.hivepower.tech/

C/E

The company’s platform monitors resource distribution and analyzes the usage behavior of consumers (from energy communities to EV charging), it also provides tools to optimize energy trading and prevent grid overloads

M

EM

AO

DA

VPP

100

Homeys

France

https://www.homeys.io/

C

Delivers a platform that connects sensors and provides real-time data and performs analysis of heating settings and energy consumption

HVAC

101

Homi

China

https://myhomi.io/

R/C

Provides home automation solutions for residential buildings

HEMS

102

inbenta

Spain

https://www.inbenta.com/en/

E

Automate customer service

103

IND Technology

Australia

https://ind-technology.com/

E

IND.T is uses DA and smart sensing to reduce unplanned outages

DA

O&M

104

Informetis

Japan

https://www.informetis.com/

C

Develops energy related services using Big Data and machine learning in order to improve EE

EE

105

Infra Solar

Netherlands

https://infrasolar.com.br/

E

Focuses on digitizing electricity consumption, optimising electric mobility, and energy consumption management

DA

106

Innowatts

USA

http://www.innowatts.com/

E

Innowatts’ platform transforms how energy providers understand and serve their customers. It uses AI to unlock meter-level data, provide consumer insights and make business processes automated and smarter for energy retailers, utilities and grid operators.

DA

107

Instylesolar

Australia

https://instylesolar.com/

R/C

Solar power analytics

DA

108

IntelliView

Canada

https://www.intelliviewtech.com/

C/E

Lleak detection and video analytics systems for surveillance

O&M

109

Invenia

Canada

https://www.invenia.ca/

E

Invenia’s Energy Intelligence System is a cloud-based machine learning platform that uses big, high frequency data to solve complex problems in real time. Invenia currently applies its platform to optimize electric utility operations as well as electricity markets themselves.

EM

110

Invenia

Canada

https://invenia.ca/

E

Forecasting and pattern recognition to manage and optimize operations

EM

AO

DA

111

Ion Energy Labs

India

https://www.ionenergy.co/

C/E

Develops battery management and mobility systems

M

AO

112

IONATE

UK

https://www.ionate.energy/

E

Real-time monitoring and control of the electricity grid-edge

EM

113

Itron

USA

https://www.itron.com/

C/E

Itron’s portfolio of smart networks, software, services, meters and sensors helps our customers better manage energy and water

EM

AO

DR

114

Jungle AI

Portugal

https://www.jungle.ai/

C/E

AI-based Canopy aims to increase production and prevent unplanned downtime and has industrial and power generation applications

O&M

115

Kagera AI

Serbia

https://www.kagera.ai/

E

Optimizes production and minimizes downtime

AO

EE

O&M

116

Kapacity.io

Finland

https://kapacity.io/

C

The company provides energy optimisation and demand response services for buildings by connecting to buildings through building management systems and adjusting HVAC energy consumption in real time

EE

DR

HVAC

117

Kayrros

France

http://www.kayrros.com/

C/E

Kayrros is an advanced DA company that helps global energy market participants make better investment decisions

DA

118

Kiwi Power (Engie)

UK

https://www.kiwipowered.com/

C/E

Provides asset optimisation using flexibility of generation capacity to earn revenue by accessing wholesale market opportunities and participating in grid demand-side services

EM

AO

VPP

119

kWIQly

Switzerland

https://kwiqly.com/

C

AI based search and analytics

DA

120

Leanheat

Finland

https://leanheat.com/

R

Leanheat aims to use artificial intelligence to improve climate control in multi-family buildings

EE

IOT

HVAC

HEMS

121

LEBO ROBOTICS

Japan

https://www.leborobotics.com/en

E

Providing smart maintenance for wind power plants

O&M

122

Levelise

UK

https://www.levelise.com/

R

Home EMS system that links high numbers of domestic battery systems to a suite of advanced algorithmic controls. By coordinating the actions of these batteries Levelise can provide balancing services to the electricity transmission operator and to suppliers

IOT

DR

HEMS

123

LifeSmart

China

https://cn.ilifesmart.com/

R/C

The company’s platform offers home automation services

HEMS

124

Limejump

UK

https://www.limejump.com/

C/E

Platform that delivers flexible energy in real time by an aggregation of flexible energy generation and storage assets.

EM

DR

125

Lition

Sweden

https://lition.io/

R

Lition and Watty provide a power usage monitoring system for commercial or personal purposes.

AO

IOT

HEMS

126

LiveEO

Germany

https://live-eo.com/

E

Provider of satellite-based power grid monitoring services

O&M

127

Loggma

Turkey

https://loggma.com.tr/en/

E

Data monitoring and analysis for solar power plants

DA

128

LogicLadder

India

https://www.logicladder.com/

C

Develops an EMS and monitoring platform

EM

DA

129

MAC

Ireland

http://www.mac.ie/Utilities/EarthFault.aspx

E

MAC created GridWatch - low-cost earth fault monitoring product solutions based on cloud back-end solution IOT

O&M

130

Mandulis Energy

Uganda

https://www.mandulisenergy.com/

E

Develops and operates renewable energy projects focused on the sustainable biomass sector

AO

IOT

HEMS

O&M

131

Meteo-Logic

Israel

http://meteo-logic.com/

E

They are a DA company focusing on weather prediction and how it impacts the energy commodity market. The company uses machine learning and big data to create self-learning algorithms that produce accurate predictions when it comes to weather and its impact on energy supply. The algorithm adapts and adjusts to the data that comes in to predict future behaviour as well.

O&M

132

METRON

France

https://www.metronlab.com/

C

Metron’s platform provides insight into energy usage and provides data on energy intelligence strategies

DA

133

Mindtitan

Estonia

https://mindtitan.com/

E

Customer service, demand forecast, optimization of energy production and scheduling, defect detection

EM

AO

O&M

134

Mobilyze

Slovakia

https://mobilyze.it/#/

C

Provides a location intelligence platform to identify EV charging hotspots

M

135

Moduly

Canada

https://moduly.io/

R/C

Moduly helps residential and commercial electricity users optimize their EE by allowing them to shift consumption peak hours, and reduce energy consumption

IOT

HVAC

HEMS

136

moixa

UK

https://moixa.com/

C

Focuses on smart battery systems and smart charging, and platform that allows households to minimize energy bills by optimizing battery and electric vehicle performance

M

AO

HEMS

137

Morgan Solar Inc.

Canada

https://morgansolar.com/

R/C

Solar power plant production forecast and optimization, data-driven EE optimization of buildings

AO

DA

HVAC

138

Myst AI

USA

https://www.myst.ai/

E

Myst AI is a developer of the AI-based data analysis platform intended for electricity demand and supply forecasting

DA

139

NanoLock Security

USA

https://nanolocksecurity.com/

C/E

NanoLock device-level protection and management secures IoT and connected devices

140

NEC

Japan

https://www.nec.com/

C/E

Focuses on plant failure prediction systems and building EMS systems

HVAC

O&M

141

Nest Labs

USA

https://nest.com/

R

Nest Labs is a home automation company manufacturing sensor-driven, Wi-Fi-enabled, self-learning thermostats and smoke detectors

EE

IOT

HVAC

HEMS

142

Neurons Lab

Ukraine

https://www.neurons-lab.com

C

Neurons Lab specializes in designing AI-based solutions to maximize energy outputs for small businesses and start-ups by forecasting renewable energy production (PV, Wind) and predicting energy consumption

AO

DA

143

Nnergix

Spain

https://www.nnergix.com/

C/E

Manages renewable energy and optimizes spinning reserves by AI-based algorithms and analytic models. Incorporates artificial intelligence to improve the accuracy of renewable energy forecasting

AO

DA

VPP

144

Notilo Plus

France

https://notiloplus.com/

E

Provides underwater analysis

O&M

145

NRGI.ai

Ireland

https://www.nrgi.ai/

C

Nrgi is developing an AI-based B2B energy marketplace with energy price benchmarks, using an AI-based forecasting engine

DA

146

Octopus Energy

UK

https://octopus.energy/

R/C

Developed a cloud-based smart grid platform that balances loads around the grid

M

EM

VPP

DR

147

Ogre AI

Romania

https://www.ogre.ai/

C/E

Ogre AI develops automated B2B platform which uses machine learning to offer financial and operational decision-making support to energy and utilities companies

DA

148

Open Energi

UK

https://openenergi.com/

R/C/E

The company’s platform, Dynamic Demand 2.0, automatically optimizes and trades power from low carbon technologies such as battery storage, EVs, demand-side response, hydrogen electrolysers and smart buildings

M

EM

AO

DR

HEMS

149

Origami Energy

UK

https://www.origamienergy.com/

E

Origami’s independent energy data platform enables real-time market access, physical control, scheduling & dispatch, contract & position management, and price & volume forecasting

EM

DA

150

Orison

USA

https://orison.com/

R/C

Orison offers residential and commercial self-installable energy storage systems paired with flexible fleet control tools that enable electric utilities and energy retailers to improve grid resilience and manage peak loads

EM

IOT

DR

151

Osperity

USA

https://osperity.com/

C

Osperity’s technology provides AI-driven intelligent visual monitoring for industrial operations that can result in improved safety, reduced carbon footprints, etc.

EE

DA

O&M

152

Palmetto Clean Technology

USA

https://palmetto.com/

R

Insights and design for rooftop solar installation, monitoring and predictive maintenance

DA

O&M

153

Piclo

UK

https://www.piclo.energy/

R/C

Operates a cloud-based platform and integrated service that supports the end-to-end process of procuring and operating flexibility

EM

AO

DR

154

Ping Services

Australia

https://ping.services/

E

Ping records changes in this signature to continuously monitor the health of wind turbine blades and use advanced acoustic analysis to detect damage

O&M

155

Ping Things

USA

https://www.pingthings.io/

E

The company uses big data software and science to detect events on the power grid. The platform records data from internal and external sources to utilities. This helps predict if an event, such as asset failure, will occur and alert the control room so they can avoid any outages.

AO

O&M

156

Plexflo

USA

https://www.plexflo.com/

C

Provides operational steps for EV charging station development, and simulates the electric grid to provide energy DA

M

157

PowerPeers

Netherlands

https://www.powerpeers.nl/

R/C

Aggregates smaller producers and consumers

AO

DR

158

Prescinto

India

https://prescinto.ai/

E

The company’s platform offers advanced DA, enhances the performance of renewable energy or energy storage assets, streamlines operations and maintenance

EE

DA

O&M

159

Proasistech

Spain

http://bd4bs.com/

E

Optimizes operation of generation assets

AO

160

Quadrical.Ai

Canada

https://www.quadrical.ai/

C/E

Quadrical develops AI-based solar plant monitoring and forecasting platform

DA

O&M

161

Quodus

Spain

https://quodus.ai/

E

Developed a platform for infrastructure monitoring, optimization of energy consumption, and analytics

AO

DA

162

R8tech

Estonia

https://r8tech.io/

C

R8 Digital Operator developed add-on software for existing building automation systems

EE

HVAC

O&M

163

Ramanujan

India

http://ramanujaninc.com/

R/C/E

Designs, develops and operates service-oriented solutions that enhance utility performance and consumer EMS

EMS

IOT

DR

HEMS

164

Raptor Maps

USA

https://raptormaps.com/

E

Advanced analytics and aerial thermal inspections for solar power plants

DA

O&M

165

Rated Power

Spain

https://ratedpower.com/

E

Their technology finds the smartest way to maximize the value of photovoltaic plants, scanning millions of iterations, finding the best configuration and immediately generating hundreds of pages of engineering documents

AO

DA

166

Raycatch

Israel

https://www.raycatch.com/

C/E

Raycatch developed DeepSolar, a digital asset management system that automates and optimizes solar PV assets

DA

O&M

167

Regalgrid

Italy

https://www.regalgrid.com/en/

R

Works as an aggregator connecting homes to one community where clean energy is produced, stored and distributed among a network of people

DR

HEMS

168

Relectrify

Australia

https://www.relectrify.com/

R/C

Increases longevity of batteries

O&M

169

Rhythmos

USA

https://rhythmos.io/

C/E

Rhythmos assembles critical information from legacy systems to automatically identify, characterize, quantify and forecast EV charging needs and host utility grid constraints

M

AO

170

Sawatch Labs

USA

https://www.sawatchlabs.com/

C

EV analytics including tailpipe emissions calculations, vehicle specific EV recommendations, site specific supply equipment, energy demand projections, and operational analysis of EV

M

DA

171

Schneider Electric

USA

http://www.auto-grid.com/

C/E

AutoGrid Systems by Schneider Electric organizes energy data and employs big DA to generate real-time predictions, and offers flexibility solutions

EM

DA

VPP

172

Scopito

Denmark

http://scopito.com/

E

Predictive maintenance for power lines, and solar and wind power plants

DA

O&M

173

Senfal (Vattenfall)

Netherlands

https://group.vattenfall.com/

C/E

Senfal is a Dutch start-up that offers innovative software services to large industrial customers, wind and solar farms, as well as battery owners for unlocking value from flexibility

EM

AO

174

Sensgreen

Turkey

https://www.sensgreen.com/

C

Optimizes building management

AO

HVAC

O&M

175

Simerse AI

UK

https://www.simerse.com/

E

Helping electric utilities find defects on critical utility equipment

O&M

176

Skyqraft

Sweden

http://skyqraft.com/

E

Skyqraft conducts aerial inspection of power lines through unmanned airplanes and machine learning

O&M

177

SkyX

Canada

https://skyx.com/

E

Drone-aided predictive maintenance

DA

O&M

178

SmartCat

Serbia

https://smartcat.io

C

SmartCat develops software used to optimize heating and cooling devices, and to reduce electricity consumption

EE

HVAC

179

SmartHelio

Switzerland

https://smarthelio.com/

E

An end-to-end real-time analytics platform for solar plants

O&M

180

Smartive

Spain

https://smartive.eu/

E

Provides a cloud-based monitoring solution for wind farm assets

O&M

181

Sobolt

Netherlands

https://www.sobolt.com

C

Sobolt operates an AI solution HeatPuls that maps heat losses in buildings

HVAC

182

Social Energy

UK

https://social.energy/

R/C

Creates solutions flexibility and demand response for homeowners and businesses

AO

DR

HVAC

HEMS

183

Solar AI

Singapore

https://getsolar.ai/

C

Aids solar power plant desing, scans sites to establish solar potential

DA

184

Solar Captus

Australia

https://www.solarcaptus.com/

R/C

Their platforms aid designing rooftop solar units, and facilitate digital marketing

DA

185

Solar Inspectron AI

Greece

https://solarinspectron.ai/

E

Drone thermal inspections of PV assets

O&M

186

Solavio

India

https://www.solavio.com/

E

Autonomous solar panel cleaning

O&M

187

SOLshare

Bangladesh

https://solshare.com/

R

SOLshare created a peer-to-peer energy exchange network of rural households and small businesses with rooftop solar home systems

AO

VPP

IOT

188

Solstice

USA

http://solstice.us

R/C

The company operates a platform that helps integrate community-based solar power

VPP

189

Solytic

Germany

https://www.solytic.com/

E

Provides monitoring solutions of large-scale rooftop and outdoor systems and develops comprehensive databases for the provision of device benchmarks and analysis

DA

O&M

190

SparkCognition

USA

https://www.sparkcognition.com/

C/E

Developed cyber-physical software for the safety, security, and reliability

191

Stem

USA

https://www.stem.com/

C/E

The company’s platform Athena forms virtual power plants to maximize the value of energy resources through a combination of machine learning and predictive analytics

M

AO

DA

VPP

192

Sterblue

France

https://www.sterblue.com/

C

Designed a platform that manages infrastructure inspections through image recognition

AO

VPP

193

suena

Germany

https://suena.energy/

E

Optimizes battery storage

AO

194

sun2wheel

Germany

https://sun2wheel.com/en/home/

R

Develops V2H and V2B charging systems that ensure the availability of energy in EVs during the day and feeds back the excess to households at night

M

195

Sunai

Chile

https://sunai.cl/en/

E

The company developed a platform NEURAL that gives operational guidelines to O&M teams

DA

O&M

196

Suncast

Chile

https://www.suncast.cl/

E

Develops models that perform energy forecasts in photovoltaic power plants and applies AI and ML to estimate soiling in photovoltaic modules

DA

O&M

197

Sync Energy

USA

https://www.sync.energy/

E

Sync develops no-code AI-based predictive simulations and analytics, that streamline planning and operations for electrical utilities.

DA

198

The Solar Labs

India

http://thesolarlabs.com

C/E

Develops software that aids solar PV engineering design

DA

199

Thermelgy

India

https://www.thermelgy.ai/

C/E

Enables end users to manage their energy assets more efficiently

AO

EE

IOT

HVAC

200

ThermoVault

Belgium

https://www.thermovault.com/

R

Provides an all-in retrofit solution that transforms electrical space and water heaters into energy-efficient storage devices

EE

DR

HVAC

201

Tibber

Sweden

http://tibber.com

R

Tibber is a digital electricity supplier that uses AI to regulate power for houses based on their predicted levels of consumption

AO

IOT

HEMS

202

tiko

Switzerland

https://tiko.energy/

R/C

tiko’s Virtual Power Plant is designed to stabilize the grid and reduce these fluctuations

EM

VPP

203

TokWise

Bulgaria

https://www.tokwise.com/

E

Develops algorithms for energy forecasting

DA

204

Trendometric

Romania

https://trendometrics.com//

E

Develops energy forecast models

DA

205

Twaice

Germany

https://www.twaice.com/

C/E

Provider of digital twin-based battery DA and management platform

AO

DA

O&M

206

Unleash live

Australia

https://unleashlive.com/

E

Video analytics for predictive maintenance

O&M

207

Upside Energy

UK

https://www.krakenflex.com/

E

Software platform for managing, controlling and optimising all distributed energy resources

EM

DA

VPP

208

Urbint

USA

https://www.urbint.com

C/E

Urbint predicts threats to workers and critical infrastructure to stop incidents before they happen

O&M

209

Verdigris Technologies

USA

https://verdigris.co/

C

Verdigris Technologies is a SaaS-based platform that develops artificial intelligence in order to optimize energy consumption

AO

DR

210

VIA

USA

https://www.solvewithvia.com/

E

Predicts energy demand, grid power flows, possible outages, and renewable energy production by analysing data collected by smart meters, drones, and sensors

DA

IOT

211

Virtual Power Solutions

UK

https://www.vps-energy.com/

R/C/E

The company’s platforms focus on smart homes and smart cities. EMS system that combines demand management with building automation technology targeted for the business sector

EM

AO

VPP

DR

HEMS

212

Visor

Portugal

https://www.visor.ai/

E

Customer service automation with AI bots

213

visualAI

India

https://futr.energy/

C/E

Technology integrates data from drones, IoT sensors, and offline sources in predictive maintenance and asset management

AO

O&M

214

Voltalis

France

https://www.voltalis.com/

R

Demand response for home heating

DR

HVAC

215

VoltPost

USA

https://www.voltpost.com/

C

Offers hardware-as-a-service that transforms lampposts into smart electric vehicle charging stations

M

216

Wärtsilä

Finland

https://www.wartsila.com/

C/E

Greensmith Energy (Wärtsilä) uses software-based intelligence and machine learning to enhance grid systems and networks

EM

VPP

DR

217

wibeee

Spain

https://wibeee.com/

R

Offers a home EMS platform

HEMS

218

Willow

Australia

https://www.willowinc.com/

C

Willow empowers asset owners and operators to make proactive, data-led decisions in real time

AO

219

Wirewatt

Mexico

https://www.wirewatt.com/

R/C

The company provides solar leases to customers of any business that installs or sells solar panels

220

Worlds

USA

https://worlds.io/

C/E

Worlds’ technology combines deep learning and IoT inside of a 4D environment to give organizations the ability to observe their organization’s physical space and then, analyze and learn from their physical surroundings

DA

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