Integration of photovoltaic (PV) systems, desalination technologies, and Artificial Intelligence
(AI) combined with Machine Learning (ML) has introduced a new era of remarkable …

The use of renewable energy sources (RESs) at the distribution level has become
increasingly appealing in terms of costs and technology, expecting a massive diffusion in the …

This paper proposes artificial neural network (ANN) and regression models for photovoltaic
modules power output predictions and investigates the effects of climatic conditions and …

The uncertainty of weather conditions has always been a major challenge limiting the
performance of photovoltaic (PV) power prediction. Enhancing the accuracy and stability of …

This paper presents data-driven models for photovoltaic module temperature prediction and
analyzes the relation and effects of ambient conditions to module temperature. A total of 12 …

Solar photovoltaic (PV) systems, integral for sustainable energy, face challenges in
forecasting due to the unpredictable nature of environmental factors influencing energy …

Technological advancements, urbanization, high energy demand, and global requirements
to mitigate carbon footprints have led to the adoption of innovative green technologies for …

Solar energy is largely dependent on weather conditions, resulting in unpredictable,
fluctuating, and unstable photovoltaic (PV) power outputs. Thus, accurate PV power …

With the increasing proportion of photovoltaic power generation, solar power prediction
systems have become more important. Accurate photovoltaic power prediction can assist the …

This study explores the forecasting accuracy of SARIMAX, LSTM, and XGBoost models in
predicting solar PV output using one-year data from three solar PV installations in the …