Hybrid feature-based neural network regression method for load profiles forecasting
Satan A. Zhakiyev N. Nugumanova A. Friedrich D.
December 2025Springer Nature
Energy Informatics
2025#8Issue 1
This study addresses the critical need for improved demand forecasting models that can accurately predict energy consumption, particularly in the context of varying geographical and climatic conditions. The work introduces a novel demand forecasting model that integrates clustering techniques and feature engineering into neural network regression, with a specific focus on incorporating correlations with air temperature. Evaluation of the model’s efficacy utilized a benchmark dataset from Tetouan, Morocco, where existing forecasting methods yielded RMSE values ranging from 6429 to 10,220 [MWh]. In contrast, the proposed approach achieved a significantly lower RMSE of 5168, indicating its superiority. Subsequent application of the model to forecast demand in Astana, Kazakhstan, as a case study, showcased its efficacy further. Comparative analysis against a baseline neural network method revealed a notable improvement, with the proposed model exhibiting a MAPE of 5.19% compared to the baseline’s 17.36%. These findings highlight the potential of the proposed approach to enhance demand forecasting accuracy, particularly across diverse geographical contexts, by leveraging climate-related inputs, the methodology also demonstrates potential for broader applications, such as flood forecasting, agricultural yield prediction, or water resource management.
Daily load Profile , Day-ahead market , Feature Engineering , K-means clustering , Neural network regression , Silhouette Value
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Department of Science and Innovation, Astana IT University, Astana, 010000, Kazakhstan
Big Data and Blockchain technologies Research Center, Astana IT University, Astana, Kazakhstan
School of Engineering, Institute for Energy Systems, University of Edinburgh, Edinburgh, EH93DW, United Kingdom
Department of Energy Engineering, University of Genova, Savona, 17100, Italy
Department of Science and Innovation
Big Data and Blockchain technologies Research Center
School of Engineering
Department of Energy Engineering
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