Long-Term Solar Power Generation Forecasting in the Eastern Coast Region of Malaysia using Artificial Neural Network (ANN) Method

Authors

  • Muhammad Aiqal Iskandar Solar Research Institute (SRI), Universiti Teknologi MARA (UiTM) Shah Alam, Selangor, Malaysia
  • Muhammad Azfar Shamil Abd Aziz Solar Research Institute (SRI), Universiti Teknologi MARA (UiTM) Shah Alam, Selangor, Malaysia
  • S. S. Sivaraju RVS College of Engineering and Technology, Coimbatore, Tamil Nadu, India
  • Nurdiyana Borhan Davex (Malaysia) Sdn. Bhd., Subang Jaya, Selangor, Malaysia
  • Wan Abd Al-Qadr Imad Wan Mohtar Institute of Biological Sciences, Faculty of Science, University of Malaya, Kuala Lumpur, Malaysia
  • Nurfadzilah Ahmad Solar Research Institute (SRI), Universiti Teknologi MARA (UiTM) Shah Alam, Selangor, Malaysia

DOI:

https://doi.org/10.37934/arfmts.117.2.6070

Keywords:

Correlation, power generation, forecasting, ANN

Abstract

Accurate prediction of power demand and generation is crucial for modern energy systems to efficiently allocate resources and facilitate energy trading. The integration of artificial intelligence (AI) and machine learning techniques has significantly improved the precision of power forecasting. This study focuses on the application of Artificial Neural Networks (ANN) for forecasting power generation in the Eastern Coast region of Malaysia, with a specific emphasis on solar power. The research methodology involves collecting and analyzing historical power data, weather data, and relevant variables. ANN models are trained, validated, and tested on a selected power grid to assess their accuracy and predictive capabilities. The expected outcomes aim to include the development of a precise power generation forecasting model, providing valuable insights for decision-makers to optimize energy operations and seamlessly integrate renewable sources. Additionally, the study explores potential challenges, limitations, and best practices associated with ANN-based power forecasting. The dataset covers the period from 2020 to 2023, with variables such as average output power, ambient temperature, PV module temperature, global horizontal irradiance, and wind speed recorded at 30-minute intervals. The architecture of the ANN model, implemented using the Keras framework, is described as a Sequential model with layers utilizing the 'ReLU' activation function. Model evaluation employs metrics like root mean square error (RMSE), mean square error (MSE), and mean absolute error (MAE) on the test set, offering insights into the model's overall fit, average deviation, and sensitivity to outliers. Results reveal strong correlations between PV module temperature, irradiance, and AC power generated.

Downloads

Download data is not yet available.

Author Biographies

Muhammad Aiqal Iskandar, Solar Research Institute (SRI), Universiti Teknologi MARA (UiTM) Shah Alam, Selangor, Malaysia

2021156057@student.uitm.edu.my

Muhammad Azfar Shamil Abd Aziz, Solar Research Institute (SRI), Universiti Teknologi MARA (UiTM) Shah Alam, Selangor, Malaysia

azfarshamil@uitm.edu.my

S. S. Sivaraju, RVS College of Engineering and Technology, Coimbatore, Tamil Nadu, India

sssivaraju@gmail.com

Nurdiyana Borhan, Davex (Malaysia) Sdn. Bhd., Subang Jaya, Selangor, Malaysia

nurdiyanaborhan@yahoo.com

Wan Abd Al-Qadr Imad Wan Mohtar, Institute of Biological Sciences, Faculty of Science, University of Malaya, Kuala Lumpur, Malaysia

qadyr@um.edu.my

Nurfadzilah Ahmad, Solar Research Institute (SRI), Universiti Teknologi MARA (UiTM) Shah Alam, Selangor, Malaysia

nurfadzilah6344@uitm.edu.my

Downloads

Published

2024-05-30

How to Cite

Muhammad Aiqal Iskandar, Muhammad Azfar Shamil Abd Aziz, S. S. Sivaraju, Nurdiyana Borhan, Wan Abd Al-Qadr Imad Wan Mohtar, & Nurfadzilah Ahmad. (2024). Long-Term Solar Power Generation Forecasting in the Eastern Coast Region of Malaysia using Artificial Neural Network (ANN) Method. Journal of Advanced Research in Fluid Mechanics and Thermal Sciences, 117(2), 60–70. https://doi.org/10.37934/arfmts.117.2.6070

Issue

Section

Articles