A novel approach for photovoltaic plant site selection in megacities utilizing power load forecasting and fuzzy inference system

Rasoul Jalalifar, Mahmoud Reza Delavar and Seyed Farid Ghaderi

Renewable Energy, 2025, vol. 243, issue C

Abstract: The development of sustainable and efficient energy solutions is necessary due to the increasing demand for electrical energy in metropolitan areas. This research proposes a novel approach to identify priority locations for urban solar investments. Investment priorities are guided by power load forecasts and spatio-temporal load modeling. The purpose of this study is to overcome the limitations of static criteria and enhance decision-making in photovoltaic plant site selection. To address this issue, this study suggests a novel approach for solar plant site selection in Tehran by utilizing multicriteria decision making (MCDM) methodologies including the Fuzzy Inference System (FIS), integrating spatio-temporal load forecasting using the SA-ConvLSTM (Self Attention ConvLSTM) algorithm. With a Root Mean Square Error (RMSE) of 35.13 % and a Mean Absolute Error (MAE) of 26.14 % after six months, the SA-ConvLSTM algorithm outperformed other techniques including LSTM, GRU and ConvLSTM in load forecasting. FIS was employed to generate the suitability maps for the site selection of solar power plants. Sensitivity analysis revealed that FIS provided the most stable results, with a mean RMSE of 1.39, while WLC was the least stable, with RMSE of 2.93. The results highlight the importance of combining load forecasting with MCDM for targeted solar investments.

Keywords: Solar energy; Fuzzy inference system; Site selection; Solar power plant; Deep learning (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:243:y:2025:i:c:s0960148125001892

DOI: 10.1016/j.renene.2025.122527

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