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July 15, 2026foresightOpen Access

Electricity load forecasting using genetic algorithm optimized sigmoid regression

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Authors

MAMustafa AkpınarRARula AzzawiUDUsman Durrani

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Overview

Randomized trial estimates accuracy of electricity load forecasting in urban settings, suggesting improved forecasting capabilities.

Key Points

  • The aim is to create an accurate model for forecasting electricity load demand using optimized sigmoid regression parameters.
  • Utilized meteorological data to prepare daily electricity load dataset from February 1, 2014 to November 30, 2019.
  • Optimized five parameters of the sigmoid regression using a genetic algorithm.
  • Compared the performance of the proposed model with the naïve model.
  • The proposed model showed a relative mean absolute error of 71.8% in training and 18.1% in test datasets, outperforming the naïve model.
  • Seasonal models were estimated for winter and summer, enhancing accuracy.
  • Test dataset errors were lower than training dataset errors, indicating sufficient accuracy for forecasting.

Cite This Study

Akpınar et al. (2026) studied this question.

synapsesocial.com/papers/6a5723fd88b21df8754808f6https://doi.org/10.1108/fs-10-2024-0189
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