Short-Term Solar Power Prediction using Machine Learning Algorithms : A High Performing approach

Authors

  • Md Ahsanul Alam Department of EEE, Green University of Bangladesh, Dhaka, Bangladesh
  • Md Nayem Sharker Department of EEE, Green University of Bangladesh, Dhaka, Bangladesh
  • Md Badrul Ahsan Tamal Department of EEE, Green University of Bangladesh, Dhaka, Bangladesh
  • Mohibul Islam Sazib Department of EEE, Green University of Bangladesh, Dhaka, Bangladesh

DOI:

https://doi.org/10.3329/gubjse.v9i1.74890

Keywords:

LSTM, ANN, SVM, principal component analysis, PV Energy, renewable energy

Abstract

The use of solar energy is growing in popularity across the globe as a clean and sustainable energy source. Nevertheless, integrating solar power into the grid and guaranteeing a steady supply of electricity is made more difficult by the weather patterns’ tendency to cause unpredictability in solar power generation. One potential solution to these issues is the use of machine learning (ML) techniques for short term solar photovoltaic (PV) power forecasting. This research looks into how well various machine learning algorithms work for short-term PV power forecasting. It also offers a summary of the variables that affect time-series data performance and suggests high-performance methods. Long Short-term Memory (LSTM), Support Vector Machines (SVM), and Artificial Neural Networks (ANN) were tested on an 88,494-item dataset with 20 features at a 15-minute resolution. As accuracy metrics, root mean square error (RMSE) and mean square error (MSE)  show that ANNs and LSTM perform better than SVM. The superiority of the suggested methods is demonstrated by a  careful comparison with recently published results. The reason for the effectiveness of ANNs and LSTMs is their capacity to  represent the complex and non-linear relationships found in the data. Short-term solar power forecasting is complex, and  SVMs are better equipped to handle linear relationships. These findings will be a useful guidance for those who intend to work in the field of PV power forecasting, even though further research is required to generalize this observation. 

GUB JOURNAL OF SCIENCE AND ENGINEERING, Vol 9(1), 2022 P 82-95

Downloads

Download data is not yet available.
Abstract
87
PDF
58

Downloads

Published

2024-07-13

How to Cite

Alam, M. A., Sharker, M. N., Tamal, M. B. A., & Sazib, M. I. (2024). Short-Term Solar Power Prediction using Machine Learning Algorithms : A High Performing approach. GUB Journal of Science and Engineering, 9(1), 82–95. https://doi.org/10.3329/gubjse.v9i1.74890

Issue

Vol. 9 No. 1 (2022)

Section

Articles

License

Copyright (c) 2022 Green University of Bangladesh

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Authors who publish in the GUB Journal of Science and Engineering agree to the following terms that:

  1. Authors retain copyright and grant the GUB Journal of Science and Engineering the right of first publication of the work.
  2. image
    Articles in GUB Journal of Science and Engineering are licensed under a Creative Commons CC BY-NC-ND License Attribution-NonCommercial-NoDerivatives 4.0 International License. This license permits Share — copy and redistribute the material in any medium or format.

Copyright and Reprint Permissions

  1. Individual contributions contained in it are protected by the copyright of Green University of Bangladesh.
  2. Photocopies of this journal in full or parts for personal or classroom usage may be allowed provided that copies are not made or distributed for profit or commercial advantage and the copies bear this notice and the full citation.
  3. Copyright for components of this work owned by others must be honored. Abstracting with credit is permitted.
  4. Specific permission of the publisher and payment of a fee are required for multiple or systemic copying, advertising or promotional purposes, resale, republishing, posting on servers, redistributing to lists and all forms of document delivery.
  5. Subscribers may reproduce a table of contents or prepare lists of articles including abstracts for internal circulation within their institutions.
  6. Permission of the Publisher is required for resale and distribution outside the institution. Permission of the publisher is required for all other derivative works, including compilations and translations.
  7. Except as outlined above, no part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, such as electronic, mechanical, photocopying, magnetic recording or otherwise, without prior written permission of the publisher.
  8. Permissions may be sought directly from the office of the executive editor of GUB Journal of Science and Engineering through E-mail at gubjse@fse.green.edu.bd.

Notice

  • Responsibility for the contents of an article rests upon the author(s) and not upon the editor or the publisher. Therefore, on responsibility is assumed by the publisher for any injury and/or damage to persons or property as a matter of products liability, negligence or otherwise, or from any use or operation of any methods, product instructions or ideas contained in the material herein.