1. Introduction
In the current context, it is important to conserve and optimally use energy as it is wasted in large amounts every day due to the increased demand for energy and an inability to efficiently use it. Thus arises a need for the efficient management of energy. Energy can be generated in various ways and purchased from national grids. Usage of energy varies from day to day, depending on the requirements and weather. In order to acquire a clear picture of day-to-day energy requirements, energy usage needs to be monitored and analyzed. This analysis could be a tedious process due to the varying patterns of energy usage and the large amounts of data that need to be analyzed. In this review, several journals and papers are grouped according to their respective energy grid applications, namely, consumption, production, demand response, energy management, design, and vehicle-to-grid transfer. Energy consumption in buildings is an important energy grid application and is reviewed as follows. The paper from Bagnasco et al., 2015 [1], gives us the forecasting results of energy consumption for a hospital facility and discusses the use case in a hospital with reference to energy forecasting and energy consumption. Abdullatif E., 2004 [2], discusses energy conversion and management for the usage of load in buildings. An artificial intelligence approach can be used to analyze algorithms and come to conclusions since it can deal with large datasets efficiently [3]. Alberto Hernandez Neto et al., 2008 [4], discussed a comparative study to anticipate the energy consumption in an administration building using an ANN model with a thorough building HVAC design and simulation software. In the paper by Alejandro et al., 2016 [5], an integrated architecture is proposed that uses the existing available data and unstructured information for better energy consumption prediction. Betul et al., 2009 [6], predicted a building’s energy forecast using three-layered backpropagation artificial neural networks. Joaquim Massana et al., 2016 [7], proposed a real case study in Girona’s university by applying the forecasting method to non-residential buildings to realize the performance discrimination between the artificial occupancy attributes. Carlos Roldán-Blay et al., 2013 [8], explored various techniques in artificial neural networks to predict electrical power load in buildings to forecast the time–temperature curve, energy usage, and performance data. N. Scarlat et al., 2014 [9], constructed an ANN model to predict the existing building heat load and its energy consumption; the obtained results were compared with an energy simulation tool called KEP-IYTE-ESS. D. Hawkins et al., 2012 [10], analyzed the distribution of energy usage in university buildings focusing on internal building environment and activity. The paper from Dandan Liu et al., 2013 [11], developed a support vector regression approach with a radial function; this was applied to predict the amount of energy consumption and the obtained results were compared using non-linear data regression. The developed prediction model used support vector regression with a radial function to forecast the energy required and assess the energy management plan. Although there are various options for managing energy, the need for more accurate prediction prevails in order to handle energy requirements and distribution. A loss in revenue results from unused energy and this can be avoided by predicting the amount of energy required and purchasing only the required amount of energy. In the given scenario, solar panels, cogeneration, consumption from buildings, and the national grid are connected to the smart grid. Through accurate prediction of unused energy, the wastage of energy can be controlled, and loss of revenue can also be prevented at a high level. Energy consumption prediction leads to many options in choosing a machine learning algorithm and produces a data analysis tool that can be used to address the following concerns.
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Reviewing various machine learning algorithms with some key parameters for each energy grid application, which is essential in finding the ideal algorithm;
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Identification of patterns in the energy consumption of buildings in a given scenario;
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Correlation analysis between the various features in the scenario;
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Usage of the best algorithm is decided according to the consumption prediction results for energy management applications.
Various machine learning algorithms are used for prediction analysis of energy grid applications. The major work carried out in this manuscript is a review analysis of various possible machine learning algorithms to filter them and obtain the ideal algorithm for implementation in the respective energy grid applications. The requirement in any given technical framework is the implementation of better energy management programs where automation and user discomfort minimization are needed. Machine learning algorithms provide a great contribution to energy management programs through prediction analysis.
David Solomon et al. [12] forecast the peak demand in a larger building to counterbalance the smart grid scenario. In the paper by Li Xuemei et al., 2010 [13], a load forecasting approach and its experimental result are presented; they used fuzzy support vector machines and a fuzzy C-mean clustering algorithm to indicate that the technique can be used as an effective approach to achieve short-term cooling. Deyin Zhao et al., 2016 [14], proposed a black box model using multivariant regression in VRV systems in office buildings to predict the total energy consumption. Faran et al., 2018 [15], presents a summary of resource management in a cellular base station powered by renewable sources and a thorough analysis of power usage and optimization is carried out to reduce costs and greenhouse gas emissions. This contributes to green communication towards climate change. Guang Shi et al., 2016 [16], and Guillermo Escrivá et al., 2011 [17], discussed an ANN method for the short-term prediction of the consumption of total power in buildings with several independent processes, while considering the load and end users. H. X. Zhao et al., 2010 [18], propose an SVM model to forecast power usage in multiple buildings. Kangji Li et al., 2011 [19], forecast the load demand in buildings using a hybrid GA-ANFIS model. Ivan Korolija et al., 2013 [20,21], described the construction of an ANN and regression model to predict the annual energy required for heating and cooling in office buildings. Jin Yang et al., 2005 [22], described static ANN models to forecast energy consumption with all the independent parameters known at time t. In the paper by Tiberia et al., 2013 [23], an approach to determining heating energy demand for fast prediction using multiple regression model is presented.
Regression models also yield good results for forecasting energy demand in buildings; this study demonstrates the usage of a multiple regression model to achieve good accuracy. In the paper by V.A. Kamadev et al., 2010 [24], forecast of energy consumption in shopping malls is perceived using connectionist systems. In the paper by Yixuan et al., 2018 [25], data-driven statistics are demonstrated different buildings for energy-related applications, such as energy prediction, energy consumption, load forecasting, energy pattern profiling, benchmarking for building stocks, and guidelines drafting for global retrofit strategies. I the paper by Yoseba et al., 2011 [26], air-conditioned non-residential and commercial buildings for short-term load forecasting are presented; it is demonstrated and observed that short-term forecasting yields better results than long-term forecasting in the future. Young et al., 2016 [27], proposed adaptive training methods and a data-driven forecasting model for determining the day-ahead electricity usage of buildings at to a 15-minute precision level to predict electricity consumption. Yumiko Iwafune et al., 2014 [28], examined different forecasting methods to support energy management in a house on a day-ahead basis. Zilong et al., 2017 [29], collected a dataset from hospital buildings and analyzed the data to forecast the energy consumption in the building. Analyses and surveys are performed in hospital buildings to determine their energy consumption applications. Adriana Chis et al., 2016 [30], with a previous tariff as reference, simulated and observed various charging scenarios for day-to-day historical time frames; they determined that the long-term costs of individual plug-in electric vehicles were reduced. Guang Shi et al., 2017 [31] describe the implementation of an echo state network technique that uses the Q function to control and determine the status of battery charging and discharging in offices using renewable energy. Frederik et al., 2016 [32], present a Q-learning approach along with an online Markov chain; in the study, this is used to evaluate and display the best methodologies for tracking hybrid electric vehicles. José R. Vázquez Canteli et al., [33], present a brief review of various machine learning algorithms and modelling techniques. Deep reinforcement learning is a version of deep learning that is used for detailed analysis before obtaining results. An efficient energy management approach for hybrid electric vehicles is obtained using deep reinforcement learning. Nora El-Nohari et al. [34] examined the impact of demand response and load shifting in residential houses. The derived simulation results provided an overview of data analytics and a prediction of the energy consumption in buildings. Xiaoshun et al., 2017 [35], present a use of the Stackelberg game, which is utilized to evaluate the supply–demand in a smart grid that operates through deep transfer Q-learning. Rui Xiong et al., 2018 [36] demonstrate a power management methodology through a validation of a real-time dataset using a battery and an ultracapacitor in-the-loop approach. The designed model for any system must be ecofriendly and reliable. A survey of the most significant energy grid applications and their designs is presented in the following works. Chang-Hwan et al., 2017 [37], investigate a value-weighted classification approach using an informational and theoretical filter approach. Cong Chen et al., 2016 [38], assert that machine learning algorithms help us to analyze and avoid real-life damages. The Naïve Bayes algorithm assesses the severity of driver injuries in rear-end vehicle crashes. Ehab et al., 2018 [39], state that a smart grid involves the integration of various renewable energy sources and has to be regulated effectively. The manuscript implements a modified harmony search algorithm to show the efficiency of smart grids in terms of structure, operation, and combined economic emission dispatch. Kadir Amasyali et al. [40] present a thorough review of various energy consumption techniques and the results were discussed to predict building energy consumption.
Demand response in buildings can be analyzed using reinforcement learning. Nima Shiri et al., 2018 [41], utilize a Naïve Bayes-supervised machine learning algorithm in a smart grid design that strategically controls and manages its resources based on conditional probability. Abdorreza et al., 2018 [42], discuss the usage of modified imperialist competitive algorithms for efficiently managing the energy resources in a smart grid. The data mining methods are broadly divided into the following categories: database, statistical, machine learning methods, and neural network. Gengyuan Liu et al., 2018 [43], highlights the developments in research in the fields of big data analytics and industrial energy efficiency assessments, with a focus on the numerous energy efficiency techniques which are based on process analysis of energy usage and big data mining. Muhammad et al., 2018 [44], examine the usage of multi-agent systems in distributed smart grids to strategically manage and control the energy resources which are involved. Panayiotis et al., 2015 [45], present a novel internal energy balancing method using a decision tree machine learning algorithm to address the increase and decrease in substantial load. Sook-Chin et al., 2017 [46], assert that energy production in any sector must be generated at a good level and maintained efficiently. Mark Landry et al., 2016 [47], utilize a Varrichio probabilistic gradient boosting machine, asserting that it is an important asset in forecasting wind energy production. Mehmet et al., 2017 [48], utilized real-time meteorological data streaming to predict the power generated by wind using a KNN classifier algorithm. Raik Becker et al., 2017 [49], utilized a hybrid approach, combining the k-nearest neighbors’ algorithm and the random forest algorithm, for forecasting the generation of wind power. Simon Martinez et al., 2017 [50], propose that energy production can be carried out using combined heat and power applications. This approach employs various machine learning techniques to assess the energy output. Vehicle-to-grid transfer applications for electric cars consists of distributed storage units, and their bidirectional features are surveyed and analyzed. M. Pihlatie et al., 2014 [51], demonstrate a viable option using machine learning techniques to study the practicality of running Nylund Fully Electric City Buses. N. Shaukat et al., 2018 [52], propose a smart grid system that consists of the integration of various renewable energy sources; they additionally provide a survey on electric vehicle transportation using machine learning techniques. Jian Tang et al., 2023 [53], proposed novel cooling techniques for a hybrid solar system, providing improved power control and energy management techniques.
The performances of various machine learning algorithms for various energy grid applications are recorded in the references mentioned above. There are different types of algorithms that can be implemented in various energy grid applications [34]. A brief explanation of algorithm types and the energy grid applications considered in this research work are seen in Section 2.2 and Section 2.3. The parameters considered and the implementations of the machine learning algorithms differ in every paper; thus, an overall comparison of all the available machine learning algorithms, using standard parameters, is essential. The comparison study (Table 1) for all the machine learning algorithms accounted for here was carried out considering standard parameters such as the energy grid application used, the reasons for utilization, the pre-training requirements, and their objectives.
The majority of currently used research methods are data-driven approaches; this is considered a rapid advancement in information technology. Information assets are considered as important to analyze, monitor, manage, improve, and predict energy in industrial sectors. The energy management procedures must be improved because a significant amount of revenue needs to be invested, and the investment that is carried out must be profitable; all this means that it is important to choose a good and efficient algorithm. The energy consumption patterns can be detected, operations can be optimized, and maintenance of costs and energy demand prediction is observed in buildings [114]. Electricity is transmitted from generation end to consumers using an energy grid. In industries energy grids are commonly used for various applications. Energy consumption by buildings, production, vehicle-to-grid transfer, design, demand response, and energy management are the energy grid applications considered in this research work. A thorough analysis of the above-mentioned parameters was carried out using various algorithms. Later, a comparative study is carried out and that gives us insights on the algorithms that should be employed to meet our requirements. Ranking of the algorithms is carried out to provide an overview of the parameters for which the machine learning algorithms are highly efficient. Every algorithm has advantages of its own; the only concern is whether it suits our chosen scenario, data type, and system requirements. Prediction performance is an important indicator that is used to save energy and to minimize the revenue spent on energy purchase and management. The wastage of energy can also be controlled through this indicator.
The algorithms are ranked effectively which leads to various new queries, such as the following: what data analysis tool should be used to evaluate the simulation results of the top performing algorithms? There are numerous tools available for data analysis, but it is important to understand and work on the tool that best suits a given scenario and its system requirements. R-tool, Rapid Miner, and Weka are some of the leading data analysis tools. A survey must be conducted to draw a conclusion on which data analysis tool must be used for the performance evaluation. A scenario-oriented approach was carried out using real-time data; the top-ranked algorithms were evaluated using the simulation results. The obtained results are discussed here, and the ideal algorithm for energy management application is determined [80].
In the industrial sector, energy-grid-based applications have remarkable potential is not adequately valued. Recognizing novelty of prediction analysis frameworks is essential in understanding energy consumption patterns, enabling one to control energy wastage and energy costs efficiently. Furthermore, the implementation of the top-performing machine learning algorithm may be the solution to managing and controlling energy flexibility, demand, wastage, and cost in energy grid applications [34].
The proposed work in this article compares several machine learning algorithms by taking essential parameters like consumption, production, vehicle-to-grid transfer, storage, design, energy management, and demand response. The review is carried out through a comparison among some key parameters, namely reasons for usage, objectives, and pre-training. Following the comparison study, the various algorithms are further graded with respect to the degree of efficiency of the parameters, namely large dataset, speed, numeric prediction, dimension reduction, and the simplicity. Real-time data are used to carry out a performance evaluation of the top-three-ranked algorithms for energy management applications [100]. Results are concluded with the best-performing algorithm being determined as the one with the highest accuracy and lowest error rates. This paper highlights the comparative study of different machine learning algorithms for energy grid parameters: consumption, production, energy management, demand response, design, and vehicle-to-grid transfer. These analyses identify the gaps in smart grid implementation and suggest future research guidelines for sustainability and energy optimization [34].
A wide range of machine learning algorithms for different energy grid applications are available. The energy grid applications seen in our paper are consumption, production, vehicle-to-grid transfer, storage, design, demand response, and energy management. All possible algorithms for every application have different efficiencies for each parameter, namely accuracy, linearity, self-training, and others. Here arises the need for a thorough comparison analysis to rank the algorithms by considering deeper parameters and performance evaluation in order to obtain the ideal algorithm amongst the various algorithms for implementation in energy grid applications [34]. Numerous papers are taken as references and insights are obtained; however, in the extensive comparison study, ranking and performance evaluation of the machine learning algorithms were implemented in smart grid systems; this fact is the driving factor of the work. Figure 1 provides an overview diagram consisting of the relevant procedures, which involves reviewing, ranking, and evaluating the algorithms for energy grid applications.
2. Algorithm Types
Based on labeled or unlabeled data types, the broad classification categories of machine learning algorithms are supervised, unsupervised, and reinforcement learning. Some top-performing and commonly used machine learning algorithms that fall under their respective algorithm types are briefly explained along with references. Irrespective of the type of algorithm, they seem to be used in numerous energy grid applications.
2.1. Supervised Learning
The presence of a supervisor as a guide is a basic explanation of supervised learning. Inferring an output from the trained labeled dataset that comprises a set of trained examples is used in supervised learning. An input object in supervised learning is the learning dataset and the response obtained for the given raw data is the output value and is called a supervisory signal. The analysis of trained labeled dataset and production of an inferred function is observed using supervised learning algorithms and is used for mapping new examples with experiences obtained [118]. The class labels for the unseen instances are correctly determined for every algorithm in each optimal scenario. The learning algorithms identify a reasonable method to address unforeseen circumstances from the generalized data. Concept learning also falls under supervised learning; it is a parallel task in animal and human psychology. Linear regression is among the most commonly defined machine learning algorithms in supervised learning and can be used extensively in most hands-on/real-time applications; this is because it is flexible and can create linear relations among unknown parameters to fit models which are non-linearly related to their parameters. The statistical properties of subsequent estimators are used for identification of Naïve Bayes classifiers in [46,67]. They were highly scalable and required a number of linear parameters; this is because the number of variables in a learning algorithm and in the training proves is determined by assessing the data to obtain its related output in a finite number of operations. Assessing and training in closed-form expression takes time; rather, iterative approximation as used for many other types of classifiers [37,38,41]. Support vector machine classifiers perform data group classification and regression; these are mapped into that same space and are predicted to belong to a given category based on the side of the gap which they fall on [55,62,68]. The ANN techniques used in [22,59,62,79] are based on a collection of connected units or nodes called artificial neurons, which loosely models the neurons in a biological brain.
2.2. Unsupervised Learning
A machine is trained with data that are neither labeled nor classified, allowing the machine learning algorithm to function without any instruction. Unlike the supervised learning algorithm, no training is provided to the machine in unsupervised learning. Grouping of unsorted data, recognizing its similarities and differences without any prior instructions, is carried out by the machines. This learning is associated with learning without a teacher; Hebbian learning is an example of unsupervised learning. The probability densities of inputs are modeled, and self-organization is also seen in unsupervised learning. There are various branches in machine learning; cluster analysis is one of the important branches [55]. Grouping of data that do not have any categorization, classification, or labeling is carried out in cluster analysis. Based on common properties, data were clustered in [3,32,58] and the responses are based on the presence or absence of such commonalities. Unsupervised learning encompasses sub-domains involving summarizing and explaining data features, but the main central application is in the field of estimation of density. The most common machine learning algorithms that are used in unsupervised learning are as follows: K-means clustering is a method of vector quantization, and it is a commonly used cluster analysis algorithm in data analysis. Numerous observations are partitioned into some clusters, where each cluster split belongs to the nearest mean cluster [43,118]. Deep belief network is a framework that can be implemented in machine learning algorithms to process complex data inputs [8,11,19]. Principal-component-analysis-based variance detection occurs when a vast majority of the data fall into a conventional distribution and are sensitive to the relative scaling of the original variables [49,55,59].
2.3. Reinforcement Learning
The concept of reinforcement learning diverges from supervised learning; here, the model is trained with the data that have the key answer within them. In reinforcement learning, an agent takes the decision of the action that needs to be performed; when the training set is found to be absent, the model learns by itself from previous experiences. In [90,93], the utilized reinforcement learning approaches use software agents to take necessary actions in a given environment to increase the notion of cumulative reward. Statistics, information theory, simulation-based optimization, swarm intelligence, multi-agent systems, control theory, and numerous problems from various fields are resolved due to its generality. The utilization of dynamic programming techniques is seen in the Markov decision process for the formulation of an environment [96,101]. In game theory and economics, the equilibrium under bounded rationality is explained using reinforcement learning. There are three machine learning paradigms mentioned in [104,106]: supervised, unsupervised, and reinforcement learning. The focus on reinforcement learning is intended to attain the performance which helps in finding the balance between current knowledge (exploitation) and uncharted territory (exploration).
3. Energy Grid Applications
Vehicle-to-grid transfer, consumption, production, energy management, demand response, and design are the energy grid applications considered in this study. A detailed description of the usages, problems, and challenges faced during the processes involved in energy grid applications are discussed below. A thorough comparative analysis of various machine learning algorithms used in energy grid applications for different key parameters shown in Figure 2 is carried out in Table 1.
3.1. Demand Response
Demand response applications are widely used in energy grids and many studies are conducted and organized based on them. Demand response research and study of general information is discussed and summarized in this paper. In grid-connected systems, the dissemination of power is a result of the due control on the demand side of electrical consumption. Demand response research is reviewed [25] for the study of demand response effects in residential houses; load shifting is determined, and its simulation results are then obtained. Therefore, load shifting is the ultimate use among demand response applications [93,102]. Dynamic costs, incentives, and time-based demand responses comprise the challenges faced in grid-connected system implementation scenarios. Simulation analyses were carried out in [104,107] on the tariffs paid in controlling peak demands from electrical appliances in houses; it was identified that the tariffs paid are high. Internet of Things applications and smart appliances present new views on energy consumption, production, and management using demand response.
3.2. Energy Management
The behavior of energy storage and transportation of energy loads influences the quality and quantity of energy used in buildings on an everyday basis; this can be seen in energy management systems. In the present context, energy usage in commercial and residential buildings has significantly increased due to increasing populations [110,113]. Energy management systems collect, store, and monitor the amount of data that is available about energy use. Analysis and exploitation of data in efficient ways are seen in this application. Data analytics techniques are presently being used to rapidly increase energy efficiency; such research is receiving significant interest and attention [114]. Energy storage and controlling energy resources for energy balancing are problems seen in energy management applications. The communication network architectures used in smart grids are considered in [46]; here, the intention was to identify energy theft and metering defects, aiming to decrease the non-technical losses that occur in smart grids. A novel real-time energy management strategy is presented in [115]; this is proposed to improve fuel consumption in hybrid vehicles through the utilization of different driving strategies. Opportunities and challenges also arise in these techniques, leading to further improvement requirements in computational technologies.
3.3. Energy Consumption
Practical data-driven models are commonly used in energy consumption applications, especially for forecasting energy consumption [1,64,73]. In the past few years, with the use of conventional sources and increase in demand requirements, energy consumption and CO2 emissions have increased significantly. Energy is the most important part of all our lives in the current context [17,23]. Data pre-processing is the most significant method for energy prediction, and findings indicate that energy costs can be significantly reduced. Dynamicity is the main problem seen in energy consumption and this can be overcome using prediction analysis. The utilization of energy consumption prediction saves energy cost and avoid wastage of excess energy [78]. Various machine learning algorithms can be trained and tested to achieve the best results for energy consumption prediction. Performance measures can be analyzed and evaluated using various data mining tools. By developing and utilizing data-driven models, energy consumption prediction can be improved in the near future [86]. Data-driven models can be used to remedy the existing gaps in research fields and future for research.
3.4. Energy Production
Sources of primary energy are generated from electric power; this process is called energy production. Delivering to the end users is the first stage followed by storage of energy, recovery, transmission, and distribution. The significance of energy production is to generate energy for various purposes, but it is commonly generated for industries. Electric energy is not freely present in nature; therefore, it must be produced in remarkable amounts via energy production [117]. Power plants and power stations generally carry out energy production tasks. Electromechanical generators generate a huge amount of electricity using power plants. Energy can be primarily produced through combustion or nuclear fission methods and can also be produced through natural means; for example, kinetic energy can be generated through freely available resources, such as wind or flowing water. Geothermal and photovoltaic resources can also be used as energy sources. Various renewable and non-renewable energy forms can be converted into useful electric energy [48,118]. Batteries also provide a very small amount of utility in electric power. For utility-scale energy generation, electric generators are rotated or photovoltaic systems are used [50,121]. The main challenge seen in energy production applications is in smart grid scenarios, where the production completely relies on solar panels; this approach is entirely dependent on the weather and climate.
3.5. Design
Reduction in harmful gases and their emission must be controlled and, for this reason, efficient models must be designed which address practical necessities, fault detections, and feature weighting in designing any energy-related model. The main usage of design modeling is to create ecofriendly and reliable models. There are various advancements happening very rapidly in the energy field. Good designs are required which utilize renewables-based distributed energy resources. Some examples for renewables-based energy resources are wind and solar systems [38]. In smart grids, we see the concept of active distribution level for the requirement of resilient power networks, and this can be achieved using renewable-based distributed energy resources. Turbine technologies have rapidly increased in the current context and concepts characterized by minimal land requirements have also been formulated [38,39,109]. The main challenge involved in the design of smart grids is the provision of suitable and safe protection approaches that involve dynamic behavior with weather conditions. The other additional issues faced are mode detection, varying fault scenarios, and section identification. Wind-turbine-based smart grids are very commonly seen; these show the impacts in voltage–current characteristics and consequently provide high wind speed profiles [41]. Pre-specified threshold settings are not very sensitive for detecting the faults that could occur with varying wind speeds in cases of conventional over-current relay scenarios.
3.6. Vehicle-to-Grid Transfer
Plug-in electric vehicles run on batteries, hydrogen fuel, or hybrid sources. These electric vehicles communicate with the smart grid, relaying a supply–demand response to the systems either by regulating their charge rate or by returning energy to the smart grid. Distributed storage units are used in vehicle-to-grid technologies for electric cars. The state of charge in batteries, technical data, and statistical data are seen in the power transfer between vehicles and smart grids. Bidirectional power flow can be seen in vehicle-to-grid transfers [51]. Power generation through wind and solar resources is commonly seen in electric vehicles; in smart grids, under normal conditions, power is sent back to the vehicle. The effect of intermittent energy supply is reduced using the distributed storage units in electric vehicles. Efficient utilization of control schemes through optimal charging and discharging is made as cost-effective as possible. The main usage of vehicle-to-grid applications is to store and discharge energy. Intelligent scheduling for charging electric vehicles is an emerging idea for obtaining maximum profits. Computer software is used to analyze and find out the optimization in charging with and without vehicle-to-grid transfer. Peak demand reduction is carried out, and the results show that better performance is obtained through charging optimization with vehicle-to-grid than without vehicle-to-grid. Vehicle-to-grid aggregators are introduced for providing additional frequency regulation services due to rapid deployment of vehicle-to-grid technologies in electric vehicles. Demand from electric vehicle owners is fulfilled by using optimal dispatching strategies of vehicle-to-grid aggregators [52]. The challenges faced in vehicle-to-grid applications are battery degradation, investment costs, energy losses, and effects on distribution equipment.
4. Comparison Study
There are various algorithms used for each energy grid application and when it comes to real-time implementations, there are numerous choices; further research is required in this area. This research involves the comparison of the most frequently used algorithms for the energy grid applications that are considered. The important parameters of machine learning algorithms—reasons for usage, pre-training involved, and objectives—are compared for all the algorithms [58]. The reasons for usage of the machine learning algorithms are mostly found to be forecasting, analysis, and evaluation, as seen in Table 1. The pre-training parameter may or may not be mandatory for the machine learning algorithms and that is also mentioned in the comparison table. Many papers and journals are reviewed by taking into consideration the important parameters for every machine learning algorithm. The highlighted objectives for the energy grid applications are mentioned clearly in the table. Energy costs, storage, and demand are some examples seen among the objectives. This gives a clear idea to compare our requirements and analyze the existing algorithms [34]. The energy grid applications are namely consumption, production, and energy management, and vehicle-to-grid transfer, design, and demand response. This chapter comprises various sections that involve reviewing, comparing, and ranking of the various machine learning algorithms for each energy grid application. Comparison tables considering the learning algorithm, reasons for usage, pre-training requirements, and objectives for each paper reviewed is displayed in Table 1, and the key parameters—namely accuracy, speed, linearity, training time, response time, self-learning, prediction numeric, dimension reduction, simplicity and large datasets—are reviewed. Later, the compared machine learning algorithms are ranked and the algorithms that excel are evaluated to determine the ideal algorithm for energy management applications.
4.1. Comparison Table
A total of 71 algorithms are compared below. These algorithms were taken from 126 papers and reviewed carefully. All of these algorithms are used for energy grid applications, namely consumption, production, energy management, vehicle to grid applications, storage, design, and demand response. Table 1 details the attributes considered here, including application, learning algorithm, reasons for usage, pre-training, and objectives.
The comparisons require key parameters, as discussed from Section 4.1.1 to Section 4.1.10. The weightage given to the parameters plays an important role in choosing the algorithm that best suits each energy grid application. The top five algorithms for all the energy grid applications—namely energy consumption, production, energy management, design, demand response, and vehicle-to-grid transfer—are obtained with the help of comparison carried out while considering deeper parameters. The top seven algorithms for energy grid applications are detailed below.
4.1.1. Accuracy
The most accurate results are not always required in performance evaluation when carried out using machine learning algorithms. Approximate answers are sufficient in most evaluation cases, depending on system requirements. Processing time is drastically reduced by using the most approximate method in machine learning algorithms [70,73]. The weightage given for the accuracy parameter must be high when compared to the other parameters, since accuracy is the most important parameter considered. Acquiring accuracy is the most challenging objective in the design of any algorithm [23,59]. Overfitting is avoided naturally by most of the approximate methods. Accuracy is usually compared and measured from many different sources among the collected data [26,60]. The algorithm with the lowest accuracy is rejected and the one with highest accuracy will be chosen as the optimal algorithm for any given energy grid application and scenario.
4.1.2. Speed
The time taken by the algorithm to run a complete analysis is defined as speed. It is calculated in minutes, hours, seconds, or sometimes even in milliseconds. The faster the algorithm runs, the better it is for our model [55]. We do not want to take a long time for our process to run. Therefore, the speed parameter is considered to be very important. The speed changes according to the density and size of a given dataset [60]. The machine learning algorithms that are very slow are rejected and the ones that are performing with high speed are used by the energy grid applications. The efficiency of any given machine learning algorithm is determined by the speed it takes to perform the implementation [86].
4.1.3. Linearity
Linearity in the time variant system is maintained by different machine learning algorithms. The classes are separated by straight lines in linear classification. Logistic regression and support vector machines are example algorithms when linearity parameters are considered. Regression algorithms presume that the datasets follow straight lines, and, because of that, accuracy might be reduced. A high dimensional analog is comparable to these desirable straight lines, and linearity is commonly represented in graphs [67,70].
4.1.4. Training Time
The time taken for training the data is considered to be very important; reduced training time is desirable in this context. The training time taken for each machine learning algorithm varies a great deal [60,65]. Accuracy is closely associated with the training time and the sensitivity of some algorithms to the data points vary to the other data points. Testing is carried out only after training the data. Most of the algorithms undergo a lot of training, as it is beneficial for the algorithm in ensuring that it obtains improved results [72,83]. This is also considered as a very important parameter when the algorithms are compared.
4.1.5. Time Response
The time response is the time taken by any model to respond to a given situation or any circumstance. The response time must be low so that a lot of time can be saved [60]. The faster the response time, the more likely the algorithm is to be used. Successful computing of any machine learning algorithm becomes critical if it has low response time [82]. The elapsed time between the query and response in any given system is called the response time [86]. System performance is measured using the total responses it gives based on service requests.
4.1.6. Self-Learning
The self-learning parameter is the most important feature in the current context because it allows the algorithm to perform tasks by itself from previous observations. Artificial intelligence decides on its own to perform a task, without an algorithm prompt. In order to decide and act by itself, the machine learning algorithm goes through an elaborate training process [118]. The training process involves significant human input along with observed values [8]. Specific situations are provided in advance and a lot of training is carried out [19]. The problem is defined accurately, and the correct and incorrect possible answers are also uploaded. The training data are then labeled, and the correct and incorrect answers are evaluated [22]. Now, the algorithm knows how to react to each of the situations. This is also known as a self-adaptive feature.
4.1.7. Predicting Numbers
The predicting numeric process usually happens in a stream and the next value must be predicted [17]. There are various examples seen for this parameter, namely artificial neural networks. This is also one of the important parameters that needs to be considered when comparing algorithms [25]. Numerical predictions are the foundation for designing any analytical approach to predict values. A predictive numeric is performed after refining and evaluating the models that were trained [102]. Numerical prediction is evaluated and assessed in all the machine learning algorithms.
4.1.8. Dimensions Reduction
The dimension reduction parameter is mainly seen in the statistics domain. Obtaining some principle variables through reducing the number of random variables is the process of dimension reduction [60]. The dataset taken can consist of many numbers of columns with a three-dimensional space [90]. The process of dimension reduction involves bringing down multiple columns to a very low number of columns within a two-dimensional space. This can be divided into feature extraction and selection [46].
4.1.9. Simplicity
The machine learning algorithm is always fed with dataset for analysis and evaluation. Some datasets will have numerous rows and columns while the other datasets will have fewer rows and columns. Numerous steps were involved in every machine learning algorithm to perform calculation and analysis; some being simple while the other algorithms are complex [38,46]. Simple algorithms are considered easy for use while complex algorithms seem to yield more accurate results [45].
4.1.10. Dataset
When the dataset is fed to every algorithm, it consists of various rows and columns, in which 80% of the dataset is utilized for training and 20% of the dataset is used for testing. Depending on the size of the dataset, it can be named large or small [60]. When a larger dataset is applied for training, more accurate results will be obtained [62,75]. A few thousand lines of rows are considered to be a large dataset, and the machine learning algorithm takes more time to produce the results, leading to increase in runtime [118]. Large datasets can be used for scenarios and system requirements where the runtime is less of a priority compared to the accuracy.
4.2. Energy Grid Applications and Machine Learning Algorithms
The reason for usage of every energy grid application is unique and serves different purposes. In Table 1, the reasons for the usage of energy grid applications are mentioned, and the majority are forecasting, analyzing, and evaluating. Most of the dataset needs pre-training, as mentioned in Table 1. The comparison table shows that pre-training helps us to attain better results. The default split between training and testing is seen to be 80:20. First, the dataset is trained for the algorithm with 80% of the dataset, and then the algorithm is tested with the remaining 20% of the dataset. The objective of every scenario is unique; a reduction in energy costs is the main goal of this research work [2,15,16,17,54,65,66]. Peak demands can be forecasted in advance to counterbalance the smart grid scenario [12,62,64,70,71,72,73]. Energy wastage can be highly controlled; revenue can be saved in a large margin through prediction [30,108,112,116]. These are the two main objectives considered here.
The algorithm we selected to use for the scenario plays a pivotal role in decision making. Survey, comparison, and performance evaluation of the algorithms will help us in determining which algorithm would suit the present smart grid scenario and the chosen requirements better. The ANN algorithm was used in most of the scenarios where the system’s learning and prediction was a priority [1,6,14,27,64,65,75,76]. SVM analyzes the data for classification and regression analysis [14,61,66,69]. The linear regression algorithm shows that the level of predictability seems to have a lower margin of error [25,29,46,99]. An interactive Q-learning algorithm learns the strategy and instructs the representative to take necessary action under specific conditions [30,31,89,93,97,100,101]. The decision tree algorithm is used for predicting multiple variables [46,58,85,99]. The essences of the machine learning algorithms presented in the comparison table are utilized for ranking them with respect to the energy grid application; the top-performing algorithms are noted for further performance evaluation. Ranking of the machine learning algorithms according to certain parameters—large dataset, speed, dimension reduction, predicting numeric, and simplicity—possess different efficiency levels, and this gives insights into which algorithm can be deployed for a given dataset, scenario, and requirement.
5. Results and Discussions
In this section, we discuss the seven top-performing algorithms based on ranking the machine learning algorithms designed for energy grid applications. The parameters used for ranking the algorithms are large dataset, speed, predicting numeric, dimension reduction, and simplicity.
The comparative study of various algorithms in the energy grid system on demand response, consumption, production, energy management, design, and vehicle-to-grid transfer provides the details of greater insight on top seven performing algorithms as seen in Figure 3. Figure 4 shows the overall top seven algorithms for the energy grid applications. The overall top seven best-performing algorithms are determined, irrespective of the energy grid applications. Deeper parameters considered for the ranking of the algorithms are size of dataset, speed, predicting numeric values, and dimension simplicity [87].
A clear understanding of the efficiency levels of the seven top-performing algorithms are seen (see Figure 4, overall top seven algorithms for the energy grid applications). Every algorithm has different efficiency levels under each parameter and the variances are seen in the graph that assists us in choosing the preferable algorithm for the given requirements and scenario. As the result of the ranking has been carried out, we see that the algorithms—namely Naïve Bayes, artificial neural networks, linear regression, support vector machine, Q-learning, Gaussian mixture model, and principle component analysis—were compared [58]. A brief explanation of the distinct features of these machine learning algorithms can be seen below. This explanation further guides us in understanding the algorithm that would best suit our dataset and requirements.
5.1. Comparison Ranking of Top Five Algorithms for Each Energy Grid Applications
Accuracy, training time, reaction time, linearity, and self-learning are the primary comparative metrics used to choose the top five algorithms for each energy grid application [6]. Now, a deeper comparison of the top five algorithms for each energy grid application is carried out with more intense parameters; they are then further ranked to obtain the best-performing algorithm for each energy grid application: demand response, energy management, consumption, production, design, and vehicle-to-grid.
5.1.1. Production
Delivering to the end users is the first stage followed by storage of energy, recovery, transmission, distribution, etc. Electric energy is not freely available in nature, and it has to be generated in remarkable amounts through energy production. Power plants and power stations carry out energy production tasks. Electromechanical generators generate huge amounts of electricity through the use of power plants [48,118]. Energy can be primarily produced using combustion or nuclear fission methods, and can also be produced by other means; for example, energy can be produced through kinetic energy, which is freely available in natural resources such as the wind or flowing water. Geothermal and photovoltaic resources can also be used as energy sources. Various renewable and non-renewable energy forms can be converted into useful electric energy. Figure 5 shows a comparison of the algorithms for production applications; the random decision forest was found to be correct—decision trees have a habit of overfitting to their training datasets [120,121]. The figure was obtained through a ranking of the efficiency levels of the parameters of the top five algorithms for the production applications.
5.1.2. Consumption
Figure 6 shows a comparison of various algorithms in order to predict the energy utilization and consumption application. The graph shown in Figure 6 shows the subset clustering as a function of consumption and is carried out based on the estimation index of a user’s energy consumption; the recorded index of various users’ power consumption patterns were obtained. Practical data-driven models are commonly seen in energy consumption applications, especially for energy consumption prediction [1,64,73]. Due to increases in populations, there is an increasing demand for energy, and usage of conventional sources increases CO2 emissions. Energy is the most essential resource in all our lives in the current context. Data pre-processing is an important method used in energy prediction; according to the prediction results, energy costs can be reduced. Various machine learning algorithms can be trained and tested to ensure the best energy consumption prediction results. The performance measures can be analyzed and evaluated using various data mining tools [10,13,14]. The figure was obtained by ranking the efficiency levels of the parameters for the top five algorithms for consumption applications.
5.1.3. Vehicle-to-Grid Transfer
Figure 7 shows the graph of algorithm comparison for a vehicle-to-grid application that shows greater insights into genetic algorithms that are commonly used to generate superior solutions for system optimization and other search problems. Bidirectional power flows can be seen in vehicle-to-grid transfers [51]. Efficient utilization of control schemes in optimal charging and discharging becomes as cost-effective as possible with advancements made in vehicle-to-grid technology. Intelligent scheduling for charging electric vehicles is an emerging approach to ensure maximum profits are obtained. Computer software is used for analyses to determine the optimization in charging with and without vehicle-to-grid transfer. Peak demand reduction is carried out, and the results show that better performance is obtained when charging optimization with vehicle-to-grid transfer than without vehicle-to-grid transfer [52]. The figure was obtained by ranking the efficiency levels of the parameters of the top five algorithms for vehicle-to-grid applications.
5.1.4. Design
The design model uses Bayesian methods as it prevents overfitting; it is ecofriendly and reliable. There are various advancements happening very rapidly in the energy field; their effective designs are urgently required for use in renewable-energy-based distributed systems. This can be achieved using renewable-energy-based distributed energy resources. Use of turbine technology has rapidly increased in the current context and concepts with minimal land requirements have also been proposed [38,39]. The main challenge involved in smart grid design is providing suitable and safe protection that involves dynamic behaviors in response to weather conditions. Figure 8 shows a comparison of the algorithms under design applications; the figure was obtained by ranking the efficiency levels of the parameters of the top five algorithms for design applications.
5.1.5. Energy Management
The decision forests algorithm and the linear regression algorithm are seen to excel in the parameters comparison when compared with the other top algorithms under energy management applications. The efficient analysis and exploitation of data are seen in this application [99,100]. In this paper, a smart grid scenario is taken into consideration for the energy management application to analyze usage patterns among users and employees in the building sector. Opportunities and challenges also arise in the techniques used, which lead to further improvement requirements in computational technologies. Figure 9 presents a comparison of algorithms under the energy management application; this figure was obtained by ranking the efficiency levels of the parameters for the top five algorithms under energy management applications.
5.1.6. Demand Response
Demand response research and study of general information is observed and summarized in this paper. Under the smart grid concept, the dissemination of demand response applications was influenced by the idea of controlling the demand side of electrical consumption. Demand response research is reviewed in [25]. Load shifting is used in residential houses for the study of demand response effects; then, simulation results are obtained. Controlling the peak demand of the electrical appliances in houses where high tariffs are paid must be evaluated and understood using simulation studies. Figure 10 presents a comparison of algorithms under demand response applications [31,32,33]. The figure was obtained by ranking the efficiency levels of the parameters of the top five algorithms for demand response applications.
5.2. Comparison Ranking of Top Three Algorithms for Each Energy Management Applications
The top three best-performing algorithms—linear regression, artificial neural networks, and decision tree—have been determined under energy management applications (Figure 9 presents a comparison of the algorithms for energy management applications). These are evaluated and the results are discussed to find the ideal algorithm for the given smart grid scenario. The machine learning algorithms chosen for use in a grid-connected system, along with their analyses using data analysis tools (Weka, R-Tool, and Rapid Miner), are shown in Table 2.
The three top-ranked algorithms for the energy management application need to be further evaluated to assess the simulation results and to find the ideal algorithm that would best suit the smart grid system. Various data mining tools are currently available and the top data mining tools and their features are compared. Each data mining tool has its own pros and cons.
The data mining tool that best suits the smart grid scenario and needs is chosen. The leading data mining tools—Weka, Rapid Miner, and R tool—are compared in detail [5]. Key features, like languages, are used to build the tool. The advantages of the specific data mining tools, the various limitations of each tool, the type of data mining tool that each one is, and the specialization of each data mining tool are discussed. The main features required for the scenario is predictive analysis; Rapid Miner is optimal for this in comparison with the other tools. R tool is the topmost data mining tool, but this type is mainly involved with statistical computing. The Rapid Miner tool is the best-performing data analysis tool for the given smart grid scenario and system requirements. Performance evaluation is carried out for the top three algorithms—linear regression, artificial neural networks, and decision tree—for the energy management application and the results are compared to find the ideal algorithm which has a high accuracy rate and low error rates.
5.3. Discussion on Performance Evaluation
The three different algorithms—decision tree, linear regression, and neural networks—do not have significant differences, and they all perform well. Python is used in the Rapid Miner tool to obtain the true computation of the performances of the three top-ranked algorithms for energy management applications. Linear regression works very well with consistent data. Decision tree and neural networks also seem to have a low margin of error, but we see that linear regression has the lowest occurrence of errors. Neural networks perform well for scenarios where they must learn and predict well; decision tree is good for predicting multiple variables, which is not required in this smart grid scenario. The analysis of the performance evaluation of the data shows linear correlation between every single event and is systematically aligned to a consumption range at any given point. Hence, linear regression works best in our given smart grid scenario with real-time data for energy management applications.
6. Conclusions
The goal of this study was to find out which algorithm would help us predict better outputs for consumption. A deep and complete comparative analysis of the possible algorithms used for different energy grid applications were reviewed. Comparison of the various machine learning algorithms that can be used to yield better results was conducted to narrow the pool, and further comparisons with deeper parameters were carried out. In this research work, a review of recent research developments regarding the use of machine learning algorithms for applications is carried out. Analyses of 126 research papers for energy-related applications, namely storage, vehicle-to-grid transfer, energy management, design, consumption, production, and demand response, were carried out. Overall, the top seven algorithms of all the applications were compared. A total of 71 algorithms were compared, taking a few parameters into consideration, namely training time, accuracy, response time, linearity, and self-learning.
Energy management systems play major roles in tariff reduction and in maintaining sustainability in grid-connected systems. This can be attained with the use of demand-in-response algorithms by integrating concepts of cyberattacks and smart meters. The main challenges in grid-connected systems are their vulnerability to energy theft and faults. Advanced smart control strategies can be incorporated to ensure better communication strategies and reliable integration. Among various machine learning techniques, the seven algorithms (Naïve Bayes, artificial neural networks, linear regression, support vector machine, Q-learning, Gaussian mixture model, and principle component analysis) were selected, as they provide various advantages for the development of intelligent power technologies. Forecasting energy utilization tends to increase gradually and the accumulated datasets from various buildings are large. The evaluation indexes for energy consumption characteristics are divided into groups and are assessed using mutual information matrices; total energy consumption is predicted based on multiple linear regression algorithms. Real-time energy theft is an issue both at the supply section and the consumer section; providing system sustainability can be attained using hybrid techniques using SVM and deep learning. The uses of this hybrid technique are as follows: (1) analyze and pre-process historical data on energy usage; (2) train and test datasets to prepare for energy theft; (3) extract valuable data from smart meters and classify the energy used with the application of Naïve Bayes; (4) validate the proposed model. The Gaussian mixture model was one of the seven algorithms chosen because it is flexible and can accomplish hard clustering for complex data. Based on the outcome of the review, a selection of the best algorithms that suit each application, in terms of their different characteristics, was made. This selection is presented in the Results and Discussion Sections. The research concludes with the results of the algorithms evaluated; our results determined that linear regression is the optimal choice for our scenario.
Methodology, R.K., S.N. and Y.T.; Software, R.K., S.N. and Y.T.; Formal analysis, R.K., S.N. and Y.T.; Investigation, R.K., S.N. and Y.T.; Resources, R.K., S.N. and Y.T.; Writing—original draft, R.K., S.N. and Y.T.; Writing—review and editing, R.K., S.N. and Y.T.; Supervision, R.K., S.N. and Y.T.; Project administration, R.K.; Funding acquisition, S.N. and Y.T. All authors have read and agreed to the published version of the manuscript.
Not applicable.
Not applicable.
The data presented in this study are available in the main text of the article.
The team of authors acknowledges anonymous reviewers for their feedback, which certainly improved the clarity and quality of this paper.
The authors declare no conflict of interest.
Footnotes
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Enlarge this image.Figure 1. Overview diagram consisting of the steps: reviewing, ranking, and evaluating the algorithms for energy grid applications.
Algorithm comparison.
| Ref. | Year | Application Learning | Learning Algorithm | Reason for Usage | Pre-Training | Objectives |
|---|---|---|---|---|---|---|
| [ |
2014 | Consumption | ANN | Forecasting | Yes | Energy storage |
| [ |
2004 | Consumption | General regression neural networks | Cooling load prediction | Yes | Energy cost |
| [ |
2008 | Consumption | Simulation tools | Assessing simulation tools | Yes | Peak demand |
| [ |
2008 | Consumption | ANN, energy plus | Forecasting | Yes | Energy demand |
| [ |
2018 | Consumption | Multidimensional hybrid integrated architecture | Forecasting | Yes | Comfort |
| [ |
2008 | Consumption | ANN | Forecasting | Yes | Energy needs |
| [ |
2016 | Consumption | SVR | Forecasting | Yes | Occupancy |
| [ |
2013 | Consumption | ANN (upgraded) | Forecasting | Yes | Time temperature Curve |
| [ |
2014 | Consumption | ANN | Forecasting | Yes | Energy performance data |
| [ |
2012 | Consumption | ANN | Analyze | Yes | Energy use |
| [ |
2013 | Consumption | SVR | Forecasting | Yes | Energy conservation |
| [ |
2010 | Consumption | SVR | Forecasting | Yes | Peak demand |
| [ |
2010 | Consumption | Fuzzy SVM and fuzzy C-mean clustering | Forecasting | Yes | Energy cost |
| [ |
2016 | Consumption | ANN, SVM, and ARIMA | Forecasting | Yes | Energy consumption intensity |
| [ |
2018 | Consumption | Integrated BS and renewable energy sources | Resource management | No | Energy cost, GHGs reduction |
| [ |
2017 | Consumption | Classification, prediction, and reduction strategy | Critical assessment | No | Energy cost |
| [ |
2016 | Consumption | Echo state networks | Forecasting, analysis | No | Parameter sensitivity, energy cost |
| [ |
2011 | Consumption | New ANN | Forecasting, validation | Yes | Customer flexibility, energy cost |
| [ |
2011 | Consumption | Parallel support vector machines | Forecasting, analyzing | Yes | Parallel computing, energy data |
| [ |
2011 | Consumption | Hybrid genetic algorithm | Forecasting | Yes | Parameter optimization |
| [ |
2013 | Consumption | Regression model | Forecasting | Yes | Energy cost |
| [ |
2018 | Consumption | Pattern-recognition-based algorithms | Forecasting | No | Improved prediction performance |
| [ |
2005 | Consumption | Adaptive artificial neural networks | Energy prediction | Yes | Energy cost |
| [ |
2015 | Consumption | Multiple linear regression, SVR, and ANN | Forecasting | Yes | Low computational cost |
| [ |
2014 | Consumption | Neural network model | Forecasting | Yes | Site load behaviors, forecasting |
| [ |
2018 | Consumption | Gaussian process meta model | Forecasting | No | Ship fuel consumption |
| [ |
2018 | Consumption | Data-driven techniques | Forecasting | Yes | Identification of research gaps |
| [ |
2018 | Consumption | A hybrid artificial neural network | Forecasting | Yes | Convergence speed, optimization |
| [ |
2012 | Consumption | Indexed ARX model | Forecasting | Yes | Energy cost, peak demand |
| [ |
2010 | Consumption | KPCA and SVM | Forecasting | No | Energy cost, peak demand |
| [ |
2009 | Consumption | Least square support vector machine | Forecasting | Yes | Energy cost, peak demand |
| [ |
2010 | Consumption | PCA and SVM | Forecasting | Yes | Energy cost |
| [ |
2012 | Consumption | ANN | Forecasting | Yes | Energy cost, peak demand |
| [ |
2014 | Consumption | Artificial neural network | Forecasting | Yes | Energy cost |
| [ |
2018 | Consumption | SVM | Forecasting | Yes | Energy cost |
| [ |
2018 | Consumption | Time series forecast techniques | Forecasting | Yes | Quality analyzation |
| [ |
2009 | Consumption | Comparison SVM and ANN | Forecasting | Yes | Energy cost, peak demand |
| [ |
2009 | Consumption | SVM | Forecasting | Yes | Energy cost |
| [ |
2015 | Consumption | FFNN, RBFN, and ANFIS | Forecasting | No | Energy cost, peak demand |
| [ |
2012 | Consumption | LS-SVM | Forecasting | Yes | Energy cost, peak demand |
| [ |
2011 | Consumption | ANN-based MLP model | Forecasting | Yes | Building occupancy and demand |
| [ |
2014 | Consumption | SVR | Forecasting | Yes | Energy cost, peak demand |
| [ |
2009 | Consumption | Neural network (modal trimming method) | Forecasting | Yes | Energy cost, peak demand |
| [ |
2015 | Consumption | Artificial neural network (ANN) | Forecasting | Yes | Energy demand |
| [ |
2010 | Consumption | ANN | Analysis | Yes | Energy cost, peak demand |
| [ |
2016 | Consumption | Extreme learning machine | Estimation | Yes | Energy demand |
| [ |
2017 | Consumption | Online | Survey | No | Energy efficiency and |
| [ |
2014 | Consumption | Multi-model prediction and simulation | Forecasting | Yes | Energy demand |
| [ |
2018 | Consumption | Survey | Estimation | No | Energy cost, peak demand |
| [ |
2017 | Consumption | ARIMA | Forecasting | Yes | Energy demand |
| [ |
2011 | Consumption | Probabilistic entropy-based neural model | Forecasting | Yes | Building occupancy and demand |
| [ |
2015 | Consumption | Support vector machine prediction | Forecasting | Yes | Energy demand |
| [ |
2018 | Consumption | Research | Benchmarking | No | Energy performance |
| [ |
2013 | Consumption | Multiple regression model | Forecasting | No | Energy demand |
| [ |
2012 | Consumption | Using connectionist systems | Forecasting | No | Energy demand, energy cost |
| [ |
2018 | Consumption | Survey | Forecasting | No | Energy structure, energy usage |
| [ |
2018 | Consumption | Survey of data-driven techniques | Forecasting | No | Energy pattern profiling |
| [ |
2011 | Consumption | Short-term load forecasting | Forecasting | Yes | Energy demand, energy cost |
| [ |
2011 | Consumption | Short-term load forecasting | Forecasting | Yes | Demand flexibility, energy cost |
| [ |
2016 | Consumption | ANN | Forecasting | Yes | Peak demand, energy cost |
| [ |
2014 | Consumption | Short-term forecasting | Forecasting | Yes | Energy demand |
| [ |
2017 | Consumption | Survey | Analysis | No | Energy conservation, energy cost |
| [ |
2016 | Demand response | Reinforcement learning |
Forecasting | Yes | Cost reduction, smart charging |
| [ |
2015 | Demand response | Reinforcement learning algorithm | Evaluation | No | Numerical modeling, computational sustainability |
| [ |
2017 | Demand response | Prediction-based multi-agent reinforcement learning | Prediction | No | Pattern change detection |
| [ |
2016 | Demand response | Reinforcement learning |
Evaluation | No | Sequential decision making, energy storage |
| [ |
2018 | Demand response | Model-free control | Evaluation | Yes | System identification scalability |
| [ |
2017 | Demand response | Batch Reinforcement Learning (Fitted-Q iteration algorithm) | Evaluation | No | Battery storage, optimization |
| [ |
2018 | Demand response | Multiple agents and reinforcement learning | Evaluation | No | Convergence rate, reward performance |
| [ |
2017 | Demand response | Network-based Q-learning |
Analysis | Yes | Peak demand reduction, energy savings |
| [ |
2017 | Demand response | Echo state network-based Q-learning method | Analysis | Yes | Optimal battery control |
| [ |
2016 | Demand response | Batch reinforcement learning | Evaluation | Yes | Thermostatically controlled loads |
| [ |
2018 | Demand response | Mobility-aware vehicle-to-grid control algorithm (MACA) | Evaluation | No | Energy demand |
| [ |
2017 | Demand response | Extended joint action learning | Analysis | No | Demand flexibility |
| [ |
2018 | Demand response | Deep reinforcement learning | Analysis | No | Optimal control, energy efficiency |
| [ |
2018 | Demand response | Reinforcement learning | Evaluation | No | Thermal comfort |
| [ |
2017 | Demand response | Reinforcement learning (used an ANN to map the state–action) | Evaluation | Yes | Thermal comfort, energy storage |
| [ |
2018 | Demand response | Deep Q-learning-based approach | Analysis | Yes | Energy storage, energy cost |
| [ |
2018 | Demand response | Hierarchical reinforcement learning, upper confidence tree search | Prediction | Yes | Fuel cell performance, hydrogen consumption |
| [ |
2018 | Demand response | Survey | Analysis | No | Reliability; adaptability |
| [ |
2017 | Demand response | Bayesian-regularized neural networks with genetic algorithm, reinforcement-learning-based control logic using fitted Q-iteration | Evaluation | Yes | Optimization |
| [ |
2018 | Demand response | Fuzzy Q-learning for multi-agent decentralization | Evaluation | No | Energy supply, energy cost, reliability |
| [ |
2018 | Demand response | Reinforcement learning-based real-time power management | Evaluation | No | Energy storage, power management |
| [ |
2006 | Demand response | Reinforcement learning control | Analysis | No | Thermal storage, optimal control |
| [ |
2018 | Demand response | Online Markov chain-based energy management | Analysis | No | Hybrid tracked vehicle, fuel economy |
| [ |
2017 | Demand response | Deep transfer Q-learning with virtual leader–follower | Analysis | No | Energy demand, energy cost |
| [ |
2018 | Demand response | Real-time power management strategy | Evaluation | No | Energy storage, power management |
| [ |
2015 | Demand response | Centralized Lyapunov algorithm | Analysis | Yes | Energy cost |
| [ |
2018 | Demand response | Deep reinforcement learning (DRL) | Analysis | Yes | Fuel economy |
| [ |
2018 | Demand response | Reinforcement learning | Analysis | No | Energy efficiency, thermal comfort |
| [ |
2015 | Demand response | Device-based reinforcement learning | Evaluation | No | Energy cost, energy demand |
| [ |
2018 | Design | Naïve Bayes | Classification | Yes | Feature weighting, feature selection |
| [ |
2016 | Design | Decision tree Naïve Bayes (DTNB) | Prediction | No | Vehicle monitoring |
| [ |
2018 | Design | Modified harmony search algorithm | Analyzing | No | Economic emission dispatch |
| [ |
2018 | Design | Survey | Analyzing | No | Security, data quality |
| [ |
2018 | Design | Discrete wavelet transform and Extreme learning machine (DWT-ELM) | Prediction | No | Wind speed intermittency |
| [ |
2018 | Design | Mixture of latent multinomial Naïve Bayes classifier (MLMNB) | Prediction | Yes | Classification accuracy, conditional log-likelihood, under the ROC curve |
| [ |
2018 | Energy management | Modified imperialist competitive algorithm | Evaluation | No | Operation cost, air pollution, use of renewable energy sources |
| [ |
2017 | Energy management | Survey | Evaluation | No | Energy constraint, use of harvested energy |
| [ |
2017 | Energy management | Energy signature | Evaluation | No | Energy use, peak demand |
| [ |
2018 | Energy management | K-means clustering | Evaluation | No | Energy efficiency, big data analysis |
| [ |
2018 | Energy management | Non-linear multivariate regression model | Develop tool | No | Energy performance, energy efficiency, energy cost |
| [ |
2016 | Energy management | Survey | Analysis | No | Power generation side management, smart grid and renewable energy management, asset management and collaborative operation, demand-side management |
| [ |
2017 | Energy management | Survey | Analysis | No | Energy load, building operation, fraud detection |
| [ |
2018 | Energy management | Multi-agent systems | Analysis | No | Optimal management |
| [ |
2016 | Energy management | Decision tree | Analysis | No | Storage planning, energy balancing |
| [ |
2017 | Energy management | Linear Regression | Evaluation | No | Detection of energy theft, defective smart meter |
| [ |
2018 | Energy management | Equivalent consumption minimization strategy (ECMS), stochastic dynamic programming (SDP) | Evaluation | No | Minimization of fuel consumption, maintenance of battery state of charge |
| [ |
2018 | Production | Survey | Analysis | No | Energy demand, energy recovery |
| [ |
2016 | Production | EnergyPlus simulation software | Evaluation | No | Incentive analysis |
| [ |
2018 | Production | K-means cluster | Forecasting | No | Numerical weather prediction |
| [ |
2016 | Production | Probabilistic gradient boosting machines for GEFCom2014 | Forecasting | No | Wind track |
| [ |
2017 | Production | kNN classifier | Forecasting | No | Prediction accuracy |
| [ |
2017 | Production | Random forests | Analysis | No | Selection of predictor variables |
| [ |
2018 | Production | Long short-term memory-enhanced forget gate (LSTM-EFG) | Forecasting | No | Peak load, frequency regulation |
| [ |
2017 | Production | Survey | Analysis | No | Energy demand, energy cost |
| [ |
2018 | Production | kNN method | Evaluation | No | Fault Detection and Diagnosis |
| [ |
2018 | Production | Multiple imputation, Gaussian Process Regression | Forecasting | Yes | Dealing with missing data |
| [ |
2018 | Production | Survey | Forecasting | Yes | Accurate forecasting, optimization |
| [ |
2018 | Storage | Complex networks centrality metrics | Evaluation | No | Optimal positioning of storage systems |
| [ |
2018 | Storage | Survey | Analysis | No | Flexibility |
| [ |
2016 | V2g | Survey | Evaluation | No | Optimization |
| [ |
2018 | V2g | Survey | Evaluation | No | Storage potential, connectivity issues |
| [ |
2016 | V2g | Survey | Evaluation | Yes | Remaining useful life |
| [ |
2014 | V2g | Survey | Analysis | No | Lower total cost of ownership |
| [ |
2018 | V2g | Survey | Analysis | No | Energy storage |
Survey of data mining tools.
| Data Analysis Tool | Weka | Rapid Miner | R-Tool |
|---|---|---|---|
| Language | Java | Language-independent | C, Fortran, R |
| Advantages | Flexible to use and can be extended in Rapid Miner | Image identification at the grid point, statistical analysis and attribute selection, and detection of parameter for system optimization | Statistical analysis utilized to take major decision in grid-connected system |
| Limitations | Poor documentation, weak classical statistics, poor parameter optimization, weak |
Requires detailed knowledge of database handling | Less specialized for data mining, requires prominent knowledge of array language |
| Type | Machine learning | Statistical analysis, data mining, predictive analysis | Statistical computing |
| Specialization | It is best suited for mining association rules and data mining techniques | Specialized for business solutions that include predictive analysis and statistical computing | It has a large number of users in the fields of bioinformatics and social science |
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; Yuvaraja Teekaraman 3
1 Department of Electrical and Electronics Engineering, Sri Sairam College of Engineering, Bangalore 562 106, India;
2 EPIK d.o.o. Nasice, 31500 Našice, Croatia;
3 School of Engineering and Computing, American International University (AIU), Al Jahra 003200, Kuwait