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The world’s demand for energy is consciously on the rise. Renewable energy sources have numerous advantages over conventional fossil fuels. Although they provide many benefits, such as sustainability and ecological advantages, the practical integration of these sources into traditional grid systems faces numerous challenges, given that they are relatively new technologies. One of the main challenges is the intermittent nature of energy production from renewable resources. Employing artificial intelligence (AI) in time series forecasting can represent a potential solution to the challenges connected to balancing the production and consumption of energy from renewable resources. This paper aims to employ long short-term memory (LSTM) networks to forecast energy production from renewable sources, specifically solar power plants. To achieve satisfactory performance, metaheuristics were applied for hyperparameter optimization. Additionally, this paper presents an adjusted type of a popular optimization algorithm, aiming to overcome the limitations of the original approach. Two experiments were conducted on publicly available datasets. The optimized models show good results, with an error of less than 1 V in predictions for one hour ahead.

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Forecasting Energy Production at Solar Substations Using LSTM Networks Optimized by Adaptive Metaheuristics

  • 会议论文

  • 发表日期：

    2025年

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  • Luka Jovanovic,
  • Milos Antonijevic,
  • Miodrag Zivkovic,
  • Branislav Radomirovic,
  • Angelina Njegus,
  • Nebojsa Bacanin

摘要

The world’s demand for energy is consciously on the rise. Renewable energy sources have numerous advantages over conventional fossil fuels. Although they provide many benefits, such as sustainability and ecological advantages, the practical integration of these sources into traditional grid systems faces numerous challenges, given that they are relatively new technologies. One of the main challenges is the intermittent nature of energy production from renewable resources. Employing artificial intelligence (AI) in time series forecasting can represent a potential solution to the challenges connected to balancing the production and consumption of energy from renewable resources. This paper aims to employ long short-term memory (LSTM) networks to forecast energy production from renewable sources, specifically solar power plants. To achieve satisfactory performance, metaheuristics were applied for hyperparameter optimization. Additionally, this paper presents an adjusted type of a popular optimization algorithm, aiming to overcome the limitations of the original approach. Two experiments were conducted on publicly available datasets. The optimized models show good results, with an error of less than 1 V in predictions for one hour ahead.