Solar PV Power Forecasting and Ageing Evaluation Using Machine Learning Techniques

Solar photovoltaic (PV) power forecasting is a crucial aspect of efficient energy management in the renewable energy sector. This study examines the use of artificial neural networks (ANNs) to forecast solar PV power output. It considers various factors influencing power output and investigates different ANNs for prediction. Real-world PV power data is collected and preprocessed for training and testing ANNs such as recurrent neural networks, autoencoders, and convolutional neural networks. The results show that ANNs, particularly Long Short-term memory (LSTM), accurately forecast PV power output in the short term. The study also analyzes the impact of panel ageing on PV power using machine learning models, revealing effective prediction of performance degradation. Clustering the dataset into sunny and cloudy subsets, and using separate models for each subset improves prediction accuracy. The study presents a comprehensive analysis of ANNs for PV power forecasting and the influence of panel ageing, highlighting the potential of machine learning for precise and reliable predictions