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Abstract

Clearness Index (CI) plays a pivotal role in determining the efficiency of solar energy systems. The relationship between CI and solar production is non-linear and significantly vary among sites. Based on a real dataset, this paper investigates the relationship between CI and solar energy generation, as well as the site-to-site variation of this critical relationship. At each site, the relationship between CI and solar generation is described by a non-linear parametric model, while the random-effects model is employed to capture the site-to-site variation. We evaluate the model's performance using Leave-One-Out Cross-Validation. The results provide insights on long-term solar energy planning by considering CL

Keywords

Clearness index, Solar generation, Random effect, Site-to-site variation, Long-term planning

(ProQuest: ... denotes formulae omitted.)

1. Introduction

As the adoption of solar photovoltaic (PV) systems continues to expand, understanding the factors that influence solar energy generation becomes increasingly critical [1]. Among these factors, Clearness Index (CI) plays a pivotal role in determining the efficiency and output of solar energy systems [2]. The CI is defined as the ratio of the actual solar radiation received at the Earth's surface over a specific time period to the theoretical maximum radiation under clear sky conditions during that same period:

... (1)

As shown in (1), this index is dimensionless and ranges from 0 (fully overcast skies) to 1 (perfectly clear skies). Variations in CI have significant impacts on the performance of solar PV systems [3]. Understanding the relationship and variation between CI and solar generation influences both short-term energy forecasting and long-term planning for solar capacity expansion.

The effects of CI on solar energy production are not only nonlinear but also vary widely across geographic locations and time scales. This complexity can be attributed to factors such as spatial region, airborne particulates, ambient temperature, and wind conditions [4]. For instance, regions prone to frequent cloud cover or atmospheric disturbances may experience more pronounced fluctuations in solar energy output, challenging the stability of grid operations [5]. Conversely, regions with consistently high CI values may generally provide favorable conditions for solar energy production, but the stability of solar energy generation in these areas can still be compromised by sudden weather changes or environmental disturbances.

Several studies have sought to improve the modeling of solar...

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