A Comprehensive Review of the Load Forecasting Techniques Using Single and Hybrid Predictive Models

Abdullah Al Mamun,Md Sohel,Debopriya Roy Dipta,Naeem Mohammad,Md Samiul Haque Sunny,Eklas Hossain

doi:10.1109/access.2020.3010702

Abstract

Load forecasting is a pivotal part of the power utility companies. To provide load-shedding free and uninterrupted power to the consumer, decision-makers in the utility sector must forecast the future demand for electricity with a minimum error percentage. Load prediction with less percentage of error can save millions of dollars to the utility companies. There are numerous Machine Learning (ML) techniques to amicably forecast electricity demand, among which the hybrid models show the best result. Two or more than two predictive models are amalgamated to design a hybrid model, each of which provides improved performances by the merit of individual algorithms. This paper reviews the current state-of-the-art of electric load forecasting technologies and presents recent works pertaining to the combination of different ML algorithms into two or more methods for the construction of hybrid models. A comprehensive study of each single and multiple load forecasting model is performed with an in-depth analysis of their advantages, disadvantages, and functions. A comparison between their performance in terms of Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE) values are developed with pertinent literature of several models to aid the researchers with the selection of suitable models for load prediction.

Highlights

Modern power system demands an uninterrupted supply of electricity to the load side
As electrical load prediction requires precision and frequent check for the varying parameters, researchers have worked on numerous Machine Learning (ML) models to enhance the performance of the existing forecasting technologies
This paper has presented a comprehensive study of notable single methods and delineated the state-of-the-art for constructing hybrid models

Summary

Introduction

Modern power system demands an uninterrupted supply of electricity to the load side This requires a proper idea of predicting present and future load demand with the least amount of error. Load forecasting is used to control several operations and decisions such as dispatch, unit commitment, fuel allocation, and off-line network analysis [1] This gives the power utility company an idea about the future demand of the consumers and an ample amount of time to mitigate the difference between the generation capacity and load demand. Load forecasting can be termed as a technique for demand and supply management It is a complex task requiring the analysis of various direct and indirect factors affecting the process. The values for the evaluation of a certain algorithm are to be kept within a few percentage points for its viability in load forecasting

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Conclusion