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Methods and Models for Electric Load Forecasting: A Comprehensive Review

Hammad Mahmoud A., Jereb Borut (), Rosi Bojan () and Dragan Dejan ()
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Hammad Mahmoud A.: Arab Academy for Science, Technology and Maritime Transport,Alexandria, Egypt
Dragan Dejan: University of Maribor/Faculty of Logistics, Celje, Slovenia

Logistics, Supply Chain, Sustainability and Global Challenges, 2020, vol. 11, issue 1, 51-76

Abstract: Electric load forecasting (ELF) is a vital process in the planning of the electricity industry and plays a crucial role in electric capacity scheduling and power systems management and, therefore, it has attracted increasing academic interest. Hence, the accuracy of electric load forecasting has great importance for energy generating capacity scheduling and power system management. This paper presents a review of forecasting methods and models for electricity load. About 45 academic papers have been used for the comparison based on specified criteria such as time frame, inputs, outputs, the scale of the project, and value. The review reveals that despite the relative simplicity of all reviewed models, the regression analysis is still widely used and efficient for long-term forecasting. As for short-term predictions, machine learning or artificial intelligence-based models such as Artificial Neural Networks (ANN), Support Vector Machines (SVM), and Fuzzy logic are favored.

Keywords: Electric load forecasting; Modeling electricity loads; Methods and models of forecasting (search for similar items in EconPapers)
Date: 2020
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DOI: 10.2478/jlst-2020-0004

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