Proceedings of the International Conference on Applications of Machine Intelligence and Data Analytics (ICAMIDA 2022)

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Solar Power Generation Prediction

Authors
Vinod B. Kumbhar1, *, Mahesh S. Chavan2, Saurabh R. Prasad1, Sachin M. Karmuse1
1D.K.T.E. Society’s Textile and Engineering Institute Ichalkaranji, Kolhapur, India
2KIT’s College of Engineering, Kolhapur, India
*Corresponding author. Email: vinodkumbhar2012@gmail.com
Corresponding Author
Vinod B. Kumbhar
Available Online 1 May 2023.
DOI
10.2991/978-94-6463-136-4_44How to use a DOI?
Keywords
Solar Irradiance; XGBoost; Optuna; Webapp; Real-time forecast
Abstract

Predicting sun irradiance has been a crucial subject in the production of renewable energy. Prediction enhances solar system development and operation and provides several financial benefits to power companies. Statistical techniques like artificial neural networks (ANN), support vector machines (SVM), or autoregressive moving average can be used to forecast the irradiance (ARMA). However, because to their scalability or the fact that they are unable to be employed with huge data, they either lack accuracy due to their inability to capture long-term reliance. Thus, in this paper the XGBoost algorithm is implemented for prediction and Optuna Algorithm for Hyper parameter tuning and optimizing the results. Aside from predicting the solar irradiance. It is crucial to create a tool that will estimate the entire amount of energy that can be produced by a solar power plant, array, or household solar setup based on the expected solar radiation and the site's particular solar panel or array parameters. In this work methodology designed and developed a system that will not only predict the solar irradiance for next 15 days based on real time forecast but it will also predict the power generation in units for your solar power panel or array. This system is currently implemented in a webapp that can be accessed through any browser.

Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the International Conference on Applications of Machine Intelligence and Data Analytics (ICAMIDA 2022)
Series
Advances in Computer Science Research
Publication Date
1 May 2023
ISBN
10.2991/978-94-6463-136-4_44
ISSN
2352-538X
DOI
10.2991/978-94-6463-136-4_44How to use a DOI?
Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Vinod B. Kumbhar
AU  - Mahesh S. Chavan
AU  - Saurabh R. Prasad
AU  - Sachin M. Karmuse
PY  - 2023
DA  - 2023/05/01
TI  - Solar Power Generation Prediction
BT  - Proceedings of the International Conference on Applications of Machine Intelligence and Data Analytics (ICAMIDA 2022)
PB  - Atlantis Press
SP  - 513
EP  - 519
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-136-4_44
DO  - 10.2991/978-94-6463-136-4_44
ID  - Kumbhar2023
ER  -