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

Aug 3, 2026

Upload by Aria

Solar energy supports clean energy transition, yet solar power generation fluctuates because meteorological factors such as solar irradiance, ambient and module temperatures, and relative humidity continuously vary. These variations can destabilize grid operation by causing unexpected power injection, making accurate solar forecasting essential. The study identifies suitable meteorological inputs using Pearson correlation analysis and incorporates them into multiple machine learning models for Kuching, Sarawak, then evaluates performance with standard error and determination metrics.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages7

Reading Time18 min

AccessFree to download

Introduction

Energy transition and motivation

Challenge of meteorological uncertainty

Need for forecasting and feature relevance

Methodology and model implementation

Pearson correlation for selecting meteorological parameters

Machine learning models for solar power prediction

Evaluation metrics

May 2024| Volume 02 | Issue 02 | Pages 20-26 Future Sustainability Journal homepage: [https://fupubco.com/fusus](https://fupubco.com/fusus) Open Access Journal ISSN 2995-0473 [https://doi.org/10.55670/fpll.fusus.2.2.3](https://doi.org/10.55670/fpll.fusus.2.2.3) Article Analyzing meteorological parameters using Pearson correlation coefficient and implementing machine learning models for solar energy prediction in Kuching, Sarawak Geoffrey Tan1, Hadi N. Afrouzi1*, Jubaer Ahmed2, Ateeb Hassan1, Firdaus M-Sukki2 1Faculty of Engineering Computing and Science, Swinburne University of Technology, Sarawak, 93350, Kuching, Malaysia 2School of Engineering and Built Environment, Edinburgh Napier University, Merchiston Campus, 10 Colinton Road, Edinburgh, EH10 5DT, UK | A R T I C L E I N F O | A B S T R A C T | | --- | --- | |
Article history:
Received 03 January 2024
Received in revised form 02 February 2024 Accepted 12 February 2024
Keywords:
Energy modeling, Machine Learning, Pearson Correlation Coefficient, Regression techniques,
Solar energy prediction, Solar forecasting
*Corresponding author Email address:
[hafrouzi@swinburne.edu.my](hafrouzi@swinburne.edu.my)
[DOI: 10.55670/fpll.fusus.2.2.3](DOI: 10.55670/fpll.fusus.2.2.3) | 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 them in 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. | 1. Introduction The entire world is going through an energy transition due to replacing fossil fuels with renewable energy sources. According to Holechek et al. [1], the world's energy consumption is increasing quickly and is predicted to increase by around 60% by 2030; during that time, fossil fuels will continue to dominate the world's energy use. However, the emissions from massive fuel combustion have resulted in global warming and depletion of the ozone layer, which caused significant climate change across the world. Therefore, many countries are introducing alternative green & renewable energy sources to meet the energy demand. Solar energy is one of the preferred candidates, abundant in many parts of the world. Malaysia is a tropical country with diverse energy resources, including fossil fuels and various renewable energy sources [2]. According toVaka et al. [3], just 8% of Malaysia's total energy is now produced from renewable sources, despite its commitment to reach 20% by 2025. Moreover, the Malaysian Investment Development Authority (MIDA) states that solar energy, hydroelectricity, and biomass are Malaysia's only flourishing renewable energy technologies. As it is in the equatorial zone, Malaysia has advantages in developing its solar energy technologies due to its abundance in this region [4] . Malaysia receives mean daily solar radiation of 4.7 to 6.5 k

FAQs

Why are meteorological parameters important for solar energy prediction?

Meteorological parameters such as solar irradiance, temperatures, humidity, and cloud-related factors change continuously, directly affecting photovoltaic output and power generation.

How does the study select meteorological parameters?

It uses the Pearson correlation coefficient to determine which meteorological features correlate highly with solar energy generation.

Which machine learning evaluation measures are used in the study?

Model accuracy is assessed using root mean square error, mean square error, mean absolute error, and the R-squared value.

Aug 3, 2026

Upload by Aria