Journal Article

Methods of Forecasting Electric Energy Consumption: A Literature Review

Efremenkov, Egor A., Klyuev, Roman V., Morgoev, Irbek D., Morgoeva, Angelika D., Gavrina, Oksana A., Mengxu, Qi, Martyushev, Nikita V.

2022

Overview

Balancing the production and consumption of electricity is an urgent task. Its implementation largely depends on the means and methods of planning electricity production. Forecasting is one of the planning tools since the availability of an accurate forecast is a mechanism for increasing the validity of management decisions. This study provides an overview of the methods used to predict electricity supply requirements to different objects. The methods have been reviewed analytically, taking into account the forecast classification according to the anticipation period. In this way, the methods used in operative, short-term, medium-term, and long-term forecasting have been considered. Both classical and modern forecasting methods have been identified when forecasting electric energy consumption. Classical forecasting methods are based on the theory of regression and statistical analysis (regression, autoregressive models); probabilistic forecasting methods and modern forecasting methods use classical and deep-machine-learning algorithms, rank analysis methodology, fuzzy set theory, singular spectral analysis, wavelet transformations, Gray models, etc. Due to the need to take into account the specifics of each subject area characterizing an energy facility to obtain reliable forecast results, power consumption modeling remains an urgent task despite a wide variety of other methods. The review was conducted with an assessment of the methods according to the following criteria: labor intensity, requirements for the initial data set, scope of application, accuracy of the forecasting method, the possibility of application for other forecasting horizons. The above classification of methods according to the anticipation period allows highlights the fact that when predicting power consumption for different time intervals, the same methods are often used. Therefore, it is worth emphasizing the importance of classifying the forecast over the forecasting horizon not to differentiate the methods used to predict electricity consumption for each period but to consider the specifics of each type of forecasting (operative, short-term, medium-term, long-term).

• Publisher: MDPI AG
• Subject: Alternative energy sources / Automation / Balance of power / Consumers / Control theory / Decision theory / deep learning / Electric power / Electrical installations / Electricity / Electricity distribution / Energy consumption / Energy management / Energy resources / energy saving / Forecasting / Fuzzy sets / Game theory / Literature reviews / Machine learning / modeling / Planning / power consumption / Power supply