Predicting monthly electricity consumption using a hybrid model combining the fuzzy model and the Holt-Winters model" (Applied Case Study: Zliten Electricity Company)
DOI:
https://doi.org/10.65405/gdb4rs49Keywords:
Electricity startup, Fuzzy logic, Holt-Winters, Mixed models, Prediction, Zliten.Abstract
The accuracy of forecasting monthly electricity consumption is considered the backbone of how to deal with the demand for electrical energy, so that electricity companies can improve their services in terms of efficiency and demand coverage in order to be able to provide services continuously. This study aims to find a model for predicting monthly electricity consumption in the city of Zliten, resulting from the integration of two models: the Holt-Winters aggregate model and the young logic model according to the method of Chen (1996), where data represented by monthly electricity consumption in the city of Zliten was used for the period extending from January 2017. Until December 2024, which is the same data used in our previous research [1]. The result was that the Holt-Winters aggregate model was superior to the fuzzy logic model in terms of accuracy in prediction. In this study, a mixed model was proposed consisting of the two previous models after specifying a weight for each model based on the network search method. It was applied to the study data for the period from January 2017 to December 2023, which was used as experimental data. The validity of the mixed model was confirmed using monthly data for the year 2024. The results showed more accuracy in the mixed model than in the previous models represented by the Holt-Winters aggregate model and the fuzzy logic model, with an ideal weight estimated at 73%. This was confirmed through the statistical criteria represented by the root mean square errors (RMSE), the mean absolute error (MAE), and the mean absolute percentage errors (MAPE), which showed clear superiority. With lower values in all criteria. Then the mixed model was compared to the previous models using statistical tests, and the mixed model was more statistically significant. Based on these results, we recommend using the proposed mixed model as a basic tool for planning and managing electricity demand in the study area.
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References
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