Solar Intelligence Predictive Models for Power Generation and Radiation

DOI

10.2991/978-94-6463-718-2_165How to use a DOI?

Keywords

Solar Energy; Machine Learning; Predictive Models; Power; Generation; Solar Radiation

Abstract

The integration of solar intelligence predictive models in renewable energy development now finds it crucial for the optimization of power generation through the accurate forecasting of solar radiation. This intelligence functions by employing advanced machine learning techniques and huge datasets, making highly precise predictions of solar power generation and solar radiation levels. The models exploit historical weather data, satellite images, and real-time sensor input to anticipate the fluctuations in solar energy production, thus enabling better grid management as well as energy storage capacity efficiencies. The proper integration of solar energy generation into the power grid means the regressions of solar power production and radiation levels must be accurate. The project investigates the development of “Solar Intelligence”- a machine learning-based predictive model. All the models will be trained using different data sources, including historical solar radiation measurements, weather forecasts, and some environmental conditions. If forecasting becomes more reliable, grid operators can produce energy optimally, integrate renewable sources seamlessly, and keep the overall stability of the grid. Also, this “Solar Intelligence” system can multiply this power of forecasting, enabling the management of solar energy for utilities and for the individual consumer more effectively and making better decisions and getting the maximum benefit from this clean and renewable energy source.

Copyright

© 2025 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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TY  - CONF
AU  - Sabaresan Venugopal
AU  - Sujith Sasitharan
AU  - Yuvaraj Sankar
PY  - 2025
DA  - 2025/05/23
TI  - Solar Intelligence Predictive Models for Power Generation and Radiation
BT  - Proceedings of the International Conference on Sustainability Innovation in Computing and Engineering (ICSICE 2024)
PB  - Atlantis Press
SP  - 2002
EP  - 2013
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-718-2_165
DO  - 10.2991/978-94-6463-718-2_165
ID  - Venugopal2025
ER  -