Application of machine learning methods in photovoltaic output power prediction: A review

Abstract

As the proportion of photovoltaic (PV) power generation rapidly increases, accurate PV output power prediction becomes more crucial to energy efficiency and renewable energy production. There are numerous approaches for PV output power prediction. Many researchers have previously summarized PV output power prediction from different angles. However, there are relatively few studies that use machine learning methods as a means to conduct a separate review of PV output power prediction. This review classifies machine learning methods from different perspectives and provides a systematic and critical review of machine learning methods for recent PV output power applications in terms of the temporal and spatial scales of prediction and finds that the artificial neural network and support vector machine are used much more frequently than other methods. In addition, this study examines the differences between the output power prediction of individual PV plants and regional PV stations and the benefits of regional PV plant prediction, while this paper presents some performance evaluation matrices commonly used for PV output power prediction. In addition, to further improve the accuracy of machine learning methods for PV output power prediction, some researchers suggest preprocessing the input data of the prediction models or considering hybrid machine learning methods. Furthermore, the potential advantages of machine model optimization for prediction performance improvement are discussed and explored in detail.

Intelligent Modeling and Optimization of Solar Plant Production Integration in the Smart Grid Using Machine Learning Models

“…In essence, the intricate web of interdependencies necessitates a focused examination of weather data to enhance the precision of PV power generation predictions. [ 29 ] In this article, the primary objective is to categorize ML methods based on various perspectives and deliver a systematic and critical overview of their applications in recent PV power output scenarios, specifically focusing on temporal and spatial prediction scales. Notably, the authors observe that ANNs and SVMs emerge as the predominant choices among the diverse ML methods.…”

Utilização De Redes Neurais Artificiais Para Análise De Sensibilidade Na Geração De Energia Solar Fotovoltaica

“…existem relativamente poucos estudos que usam métodos de aprendizado de máquina como um meio de realizar uma revisão separada da previsão de potência de saída fotovoltaica. Zhang et al (2022).…”

“…imagens de satélite) para produzir melhores resultados de previsão a longo prazo. Reforça-se que é preciso desenvolver melhores algoritmos de otimização para otimizar o método de aprendizado de máquina principalmente quando se fala em integrar modelos de previsão com várias tecnologias para melhorar o desempenho da previsão de potência de saída fotovoltaica, ou seja, a continuidade de estudos na área é necessário Zhang et al (2022).…”

Variational mode decomposition combined fuzzy—Twin support vector machine model with deep learning for solar photovoltaic power forecasting

“…developed a deep learning approach (RNN-LSTM) to forecast the PV output power of the considered solar farms [27]. performed a review of machine learning methods from different perspectives and provided a critical review of machine learning models for recent PV output power applications [28]. Yu et al (2022) developed a convolutional long short-term memory network (CLSTM) prediction model optimized by adaptive mutation particle swarm optimization for solar power generation forecasting [29].…”