Abstract
The use of artificial intelligence (AI) is increasing in various sectors of photovoltaic (PV) systems, due to the increasing computational power, tools and data generation. The currently employed methods for various functions of the solar PV industry related to design, forecasting, control, and maintenance have been found to deliver relatively inaccurate results. Further, the use of AI to perform these tasks achieved a higher degree of accuracy and precision and is now a highly interesting topic. In this context, this paper aims to investigate how AI techniques impact the PV value chain. The investigation consists of mapping the currently available AI technologies, identifying possible future uses of AI, and also quantifying their advantages and disadvantages in regard to the conventional mechanisms.
Highlights
Over the last few decades, artificial intelligence (AI) has emerged as one of the most prominent areas of research, due to its capability to automate systems for enhanced efficiency and performance [1]
Artificial neural network (ANN) and its sub-architectures are the most widely used AI techniques, but this depends on the use case
In the case of optimization, evolutionary algorithms, such as particle swarm optimization (PSO) and GA, are widely used, while in the case of time-series data, as in irradiance prediction or power forecasting, ANNs are used with great success
Summary
Over the last few decades, artificial intelligence (AI) has emerged as one of the most prominent areas of research, due to its capability to automate systems for enhanced efficiency and performance [1]. The data availability in PV power systems’ operation has advanced the development of AI to assist the system learning process in the design, control, and maintenance aspects for improving efficiency and reducing response time. This approach encouraged research activities in a data-driven perspective to analyze the complex and challenging problems in power systems.
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