Methods of Forecasting Electric Energy Consumption: A Literature Review

Abstract

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).

Risk Spillovers between China’s Carbon and Energy Markets

“…This trend has arisen because neural networks allow for taking into account the non-linear nature of power consumption data and finding non-trivial dependencies in them. All of this allows for an improvement in the accuracy of operational, short-term, medium-term, and long-term forecasting of electricity consumption [39]. Specifically, among the three major energy markets, WTI crude oil, Brent crude oil, and natural gas, the WTI crude oil market had the strongest spillover effect on the carbon market, and the natural gas market had a more prominent spillover effect on the carbon market; furthermore, he used the rolling window technique to detect the time-varying characteristics of the spillover effect, and the results show that major policies and events could lead to large changes in the spillover index.…”

Electricity demand analysis and forecasting: The case of GADA special economic zone

“…Whereas mid-term and short-term forecasting are usually applied for planning the power production resources and scheduling and analyzing the distribution networks, respectively. The forecasted load in the mid-term and long-term is commonly used as an important reference value in policy formulation associated with national economic and economic development strategies [ 17 ].…”

Induction Motor Bearing Fault Diagnosis Based on Singular Value Decomposition of the Stator Current

“…The maintenance strategy and the appropriate statistical distribution should be based on the occurrence rate of system failures and breakdowns. Much work has been carried out by researchers in the field of creating such tools: transport optimization methods have been proposed [16,17]; the advantages and uses of ANNs for forecasting in various operations have been reviewed [18][19][20][21][22][23][24][25]; and studies are also underway to develop an intelligent optimal energy management strategy [26][27][28][29][30][31][32][33][34]. The subject of improving the reliability and availability of equipment through the use of various intelligent computer systems is highly relevant [35][36][37]; the chosen approach will directly determine the planning and maintenance strategy, the length of the equipment lifecycle and, subsequently, the entire plant lifecycle.…”