Enhanced PV Power Prediction Considering PM10 Parameter by Hybrid JAYA-ANN Model

• Published in: Electric power components and systems Vol. 52; no. 11; pp. 1998 - 2007
• Main Authors: Irmak, Erdal, Yeşilbudak, Mehmet, Taşdemir, Oğuz
• Format: Journal Article
• Language: English
• Published: Philadelphia Taylor & Francis 02.07.2024; Taylor & Francis Ltd
• Subjects: Alternative energy sources; artificial neural network; Artificial neural networks; comparison; metaheuristic optimization; Meteorological data; Parameters; Particulate emissions; Photovoltaic cells; Photovoltaic power production; prediction; Solar energy
• ISSN: 1532-5008; 1532-5016
• DOI: 10.1080/15325008.2024.2322668

The demand for electrical energy is continuously increasing in these days, particularly due to advancements in the industrial sector. This surge in demand has underscored the importance of seeking alternative energy sources, with solar energy emerging as a standout option due to its low investment costs and environmental friendliness. However, the variability in photovoltaic power production, influenced by meteorological data, necessitates accurate prediction methods. To enhance the precision of these predictions, incorporating new parameters alongside existing meteorological data is advantageous. In this regard, this study explores the impact of the particulate matter (PM10) parameter on photovoltaic power prediction using artificial neural network (ANN) model and JAYA-ANN. Comparing the prediction results based on root mean squared and mean absolute percentage errors reveals that the hybrid JAYA-ANN model consistently outperforms the ANN and persistence models. Notably, the PM10 parameter proves to be a significant input in forecasting daily photovoltaic power.