| Issue |
E3S Web Conf.
Volume 387, 2023
International Conference on Smart Engineering for Renewable Energy Technologies (ICSERET-2023)
|
|
|---|---|---|
| Article Number | 01004 | |
| Number of page(s) | 9 | |
| Section | Electronic and Electical Engineering | |
| DOI | https://doi.org/10.1051/e3sconf/202338701004 | |
| Published online | 15 May 2023 | |
Integrating Machine Learning Algorithms for Predicting Solar Power Generation
1 Bannari Amman Institute of Technology, Sathayamangalam - 638401, India
2 New Prince Shri Bhavani College Of Engineering and Technology, Approved by AICTE, Affilated To Anna University
3 Assistant Professor, Prince Dr. K. Vasudevan College of Engineering and Technology, Chennai – 127
4 Assistant Professor, Prince Shri Venkateshwara Padmavathy Engineering College, Chennai – 127
* Corresponding author: sangeetha@bitsathy.ac.in
In recent years, there has been a growing interest in using artificial intelligence (AI) techniques to predict solar power generation. One such technique is the use of an artificial neural network (ANN) with a genetic algorithm (GA) to optimize its parameters. This approach involves training an ANN to predict solar power generation based on historical data and using a GA to optimize the ANN’s architecture and activation function. The GA searches for the best combination of hidden layers and activation functions to minimize the error between the predicted and actual solar power generation. This paper presents an algorithm for implementing an ANN-GA for predicting solar power generation. The algorithm involves preprocessing the data, defining the ANN architecture, defining the fitness function, and implementing the GA to optimize the ANN’s parameters. The results of this approach can be useful for predicting future solar power generation and optimizing the performance of solar power systems.
Key words: Machine Learning Algorithms / Solar Power Generation / Renewable Energy Sources / Predictive Modeling / Artificial Neural Networks / Support Vector Machines
© The Authors, published by EDP Sciences, 2023
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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