Analyzing meteorological parameters using Pearson correlation coefficient and implementing machine learning models for solar energy prediction in Kuching, Sarawak

Tan, Geoffrey; Afrouzi, Hadi Nabipour; Ahmed, Jubaer; Hassan, Ateeb; Sukki, Firdaus Muhammad

Abstract

Solar energy is one of the clean renewable energy sources that can offset the rising consumption of fossil fuels. However, the meteorological parameters, such as solar irradiance, ambient and solar module temperatures, relative humidity, etc., constantly change,and so does the solar power generation. Such variations cause instability in the power grid operation due to injecting an unpredicted amount of power. Hence, solar energy prediction models capable of learning from past weather data and predicting future energy generation are highly desired for grid operation and planning. The objective of this study is to determine the suitable meteorological parameters for the solar energy prediction model based on the Pearson correlation coefficient and to implement themin different machine learning models. It is found in this study that five meteorological parameters, namely Air temperature, cloud opacity, global tilted irradiance, relative humidity, and zenith angle,correlate highly with solar energy generation. Later, based on the correlations, four machine-learning models were implemented to predict the solar power for Kuching, Sarawak. The accuracy of the models is measured through standard matrices such as root mean square error, mean square error, mean absolute error, and R-squared value.

Citation

Tan, G., Afrouzi, H. N., Ahmed, J., Hassan, A., & Sukki, F. M. (2024). Analyzing meteorological parameters using Pearson correlation coefficient and implementing machine learning models for solar energy prediction in Kuching, Sarawak. Future Sustainability, 2(2), 20-26. https://doi.org/10.55670/fpll.fusus.2.2.3

• Journal Article Type: Article
• Acceptance Date: Feb 12, 2024
• Online Publication Date: May 15, 2024
• Publication Date: 2024-05
• Deposit Date: Oct 15, 2024
• Publicly Available Date: Oct 15, 2024
• Journal: Future Sustainability
• Electronic ISSN: 2995-0473
• Publisher: Future Publishing
• Peer Reviewed: Peer Reviewed
• Volume: 2
• Issue: 2
• Pages: 20-26
• DOI: https://doi.org/10.55670/fpll.fusus.2.2.3
• Keywords: Energy modeling, Machine Learning, Pearson Correlation Coefficient, Regression techniques, Solar energy prediction, Solar forecasting
• Publisher URL: https://fupubco.com/fusus/issue/view/23