Proceedings of the International Conference on Intelligent Systems for a Sustainable Future (ISSF 2026)

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Machine Learning Based Prediction of Solar Power Generation by Incorporating Denoising Methods

Authors
Jyothi Godlaveti1, *, M. Padma Lalitha3, Harshitha Chejerla1, Chaitanya Palem1, Chandra Sekhar Vanta1, N. Manvitha Mahalakshmi2
1UG Scholars, Department of EEE, Annamacharya Institute of Technology and Sciences, Rajampet, Andhra Pradesh, India
2UG Scholar, Department of EEE, Annamacharya Univesity, Rajampet, Andhra Pradesh, India
3Professor, Department of EEE, Annamacharya University, Rajampet, Andhra Pradesh, India
*Corresponding author. Email: mpl@aitsrajampet.ac.in
Corresponding Author
Jyothi Godlaveti
Available Online 16 June 2026.
DOI
10.2991/978-94-6239-693-7_52How to use a DOI?
Keywords
WT; EMD; solar power forecast; deep learning; machine learning; renewable energy; and smart grid
Abstract

To achieve optimal planning of the energy system and stability of this system, it is essential to have precise forecasting of the solar power. Based on long-term solar production and meteorological records obtained at the Annamacharya University in Rajampet, this paper proposes a machine learning-based architecture to photovoltaic power prediction. In the bid to improve the quality of data, Empirical Mode Decomposition (EMD) and Wavelet Transform (WT) are applied to remove noise and extract the meaningful signal components. Various machine learning models are trained on the denoised data including regression, ensemble, and deep learning models. Considering the model performance, R2 MAE, MedAE and RMSE are utilized. The analysis of the results demonstrates that denoising-based models, in particular, ensemble and deep learning approaches, are better than traditional methods in terms of forecasting the accuracy. The proposed framework will promote reliable integration of photovoltaic systems in smart grids and management of solar energy.

Copyright
© 2026 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 Intelligent Systems for a Sustainable Future (ISSF 2026)
Series
Atlantis Highlights in Intelligent Systems
Publication Date
16 June 2026
ISBN
978-94-6239-693-7
ISSN
2589-4919
DOI
10.2991/978-94-6239-693-7_52How to use a DOI?
Copyright
© 2026 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  - Jyothi Godlaveti
AU  - M. Padma Lalitha
AU  - Harshitha Chejerla
AU  - Chaitanya Palem
AU  - Chandra Sekhar Vanta
AU  - N. Manvitha Mahalakshmi
PY  - 2026
DA  - 2026/06/16
TI  - Machine Learning Based Prediction of Solar Power Generation by Incorporating Denoising Methods
BT  - Proceedings of the International Conference on Intelligent Systems for a Sustainable Future (ISSF 2026)
PB  - Atlantis Press
SP  - 526
EP  - 538
SN  - 2589-4919
UR  - https://doi.org/10.2991/978-94-6239-693-7_52
DO  - 10.2991/978-94-6239-693-7_52
ID  - Godlaveti2026
ER  -