Forecasting Domestic Solar Power Production via Recurrent Networks Tuned by a Modified Metaheuristic
- 28 May 2025
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 189-194
Abstract
The world's ever-increasing need for energy demands innovation and resourcefulness from engineers and power companies. Reducing transportation losses and providing reliable power are just some of the challenges facing power production from renewable resources. Nevertheless, addressing these challenges is essential for improving sustainability and slowing down climate change. The sporadic nature of power production from solar panels makes it difficult to integrate into existing power grid infrastructure. A robust forecasting system could help mitigate these issues. This study investigates the application of long short term memory (LSTM)-based neural networks to tackle production forecasting. Since forecasting models rely on proper hyperparameter selection, this study presents a modified optimizer for hyperparameter tuning to improve performance. Simulations on real world data have shown promising results, with LSTM based networks optimized by a modified metaheuristic based on the PSO showcasing favorable outcomes with a mean absolute percentage error (MAPE) as low as 4.112%.Keywords
Funding Information
- Science Fund of the Republic of Serbia (7502)
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