Modelling Ageing and Power Production of Solar PV Using Machine Learning Techniques

Solar photovoltaic (PV) power prediction plays a pivotal role in optimizing energy management within the re-newable energy industry. In this investigation, we explore the utilization of artificial neural networks (ANNs) to model solar PV ageing and, at the same time, forecast power generation. Diverse factors impacting power output are examined, and multiple ANNs are explored for prediction purposes. Real-world PV power data is collected and subjected to preprocessing to facilitate the training and testing of ANNs, including recurrent neural networks, autoencoders, and convolutional neural networks. The findings demonstrate the accurate short-term forecasting capa-bilities of ANNs, with particular emphasis on Long Short-term Memory (LSTM) networks. Additionally, the study delves into the effects of panel ageing on PV power by leveraging machine learning models and data analysis, leading to the identification of effective performance degradation prediction. The dataset is further segmented into subsets representing sunny and cloudy conditions, and employing separate models for each subset yields improved prediction accuracy. In fact, notable distinctions in power production characteristics between sunny and cloudy con-ditions are revealed. Thus, tailoring distinct models for different weather conditions is crucial to ensure precise power predictions and effectively address daily uncertainties. The research presents an extensive analysis of ANNs for PV power forecasting and emphasizes the potential of machine learning techniques in enabling accurate and reliable predictions.