A Review on Machine Learning Applications for Solar Plants

Abstract

A solar plant system has complex nonlinear dynamics with uncertainties due to variations in system parameters and insolation. Thereby, it is difficult to approximate these complex dynamics with conventional algorithms whereas Machine Learning (ML) methods yield the essential performance required. ML models are key units in recent sensor systems for solar plant design, forecasting, maintenance, and control to provide the best safety, reliability, robustness, and performance as compared to classical methods which are usually employed in the hardware and software of solar plants. Considering this, the goal of our paper is to explore and analyze ML technologies and their advantages and shortcomings as compared to classical methods for the design, forecasting, maintenance, and control of solar plants. In contrast with other review articles, our research briefly summarizes our intelligent, self-adaptive models for sizing, forecasting, maintenance, and control of a solar plant; sets benchmarks for performance comparison of the reviewed ML models for a solar plant’s system; proposes a simple but effective integration scheme of an ML sensor solar plant system’s implementation and outlines its future digital transformation into a smart solar plant based on the integrated cutting-edge technologies; and estimates the impact of ML technologies based on the proposed scheme on a solar plant value chain.

Artificial intelligence applications in solar energy

“…To successfully implement artificial intelligence-based control systems, predictive maintenance algorithms, and energy management platforms, it is necessary to have robust hardware, dependable data connection, and smooth compatibility with preexisting systems and protocols. In addition, it is of the utmost importance to guarantee the cybersecurity and data privacy of solar energy systems that are enabled by artificial intelligence to protect against possible attacks and weaknesses [248]- [250].…”

A Comprehensive Review of Supervised Learning Algorithms for the Diagnosis of Photovoltaic Systems, Proposing a New Approach Using an Ensemble Learning Algorithm

“…This paper examines the types of faults in PV systems, their causes and consequences, and presents the most popular supervised learning methods for PV fault diagnosis in recent years. It also summarizes the reviews published on Artificial Intelligence methods for PV fault diagnosis from 2016 to 2023 [37,[44][45][46][47][48][49][50][51][52]. Finally, the Extra Trees algorithm is proposed as a new robust classifier capable of improving the inadequacies of other classifiers in the fault diagnosis of photovoltaic (PV) systems.…”