A Review of Auto-Regressive Methods Applications to Short-Term Demand Forecasting in Power Systems

Abstract

The paper conducts a literature review of applications of autoregressive methods to short-term forecasting of power demand. This need is dictated by the advancement of modern forecasting methods and their achievement in good forecasting efficiency in particular. The annual effectiveness of forecasting power demand for the Polish National Power Grid for the next day is approx. 1%; therefore, the main objective of the review is to verify whether it is possible to improve efficiency while maintaining the minimum financial outlays and time-consuming efforts. The methods that fulfil these conditions are autoregressive methods; therefore, the paper focuses on autoregressive methods, which are less time-consuming and, as a result, cheaper in development and applications. The prepared review ranks the forecasting models in terms of the forecasting effectiveness achieved in the literature on the subject, which enables the selection of models that may improve the currently achieved effectiveness of the transmission system operator. Due to the applied approach, a transparent set of forecasting methods and models was obtained, in addition to knowledge about their potential in the context of the needs for short-term forecasting of electricity demand in the national power system. The articles in which the MAPE error was used to assess the quality of short-term forecasts were analyzed. The investigation included 47 articles, several dozen forecasting methods, and 264 forecasting models. The articles date from 1997 and, apart from the autoregressive methods, also include the methods and models that use explanatory variables (non-autoregressive ones). The input data used come from the period 1998–2014. The analysis included 25 power systems located on four continents (Asia, Europe, North America, and Australia) that were published by 44 different research teams. The results of the review show that in the autoregressive methods applied to forecasting short-term power demand, there is a potential to improve forecasting effectiveness in power systems. The most promising prognostic models using the autoregressive approach, based on the review, include Fuzzy Logic, Artificial Neural Networks, Wavelet Artificial Neural Networks, Adaptive Neurofuse Inference Systems, Genetic Algorithms, Fuzzy Regression, and Data Envelope Analysis. These methods make it possible to achieve the efficiency of short-term forecasting of electricity demand with hourly resolution at the level below 1%, which confirms the assumption made by the authors about the potential of autoregressive methods. Other forecasting models, the effectiveness of which is high, may also prove useful in forecasting by electricity system operators. The paper also discusses the classical methods of Artificial Intelligence, Data Mining, Big Data, and the state of research in short-term power demand forecasting in power systems using autoregressive and non-autoregressive methods and models.

A Neural Network Forecasting Approach for the Smart Grid Demand Response Management Problem

“…They are divided into auto-regressive and moving average models and state-space models. A comprehensive table outlining the findings of the review of 47 publications detailing 264 forecasting models from 1997 to 2018 is presented in Czapaj et al [4]. It summarizes the status of research on short-term power demand forecasting for power systems using auto-regressive and non-auto-regressive approaches and models.…”

“…An analysis was conducted on the effectiveness of the forecasting models as determined by the Mean Average Percentage Error (MAPE) measure. Table 1 lists the top 10 forecasting techniques and models: DEA, FR, GRM, GA, ANFIS, ANN, FGRM, WANN, ANN, and FL, where the repeated ANN belongs to different models [4]. FGRM and GRM employ the explanatory variables, while the remaining eight models are auto-regressive.…”

High Penetration of Renewable Energy Sources and Power Market Formation for Countries in Energy Transition: Assessment via Price Analysis and Energy Forecasting

“…Autoregressive (AR) and moving average (MA) models, as well as combined ARMA models, are a common and classic approach, that is relatively inexpensive and quick to implement from a computing perspective. These models tend to give relatively good results when used for short-term forecasting of simple time series [24]. The Box-Jenkins method [25] is often used to determine the appropriate order of such models.…”

“…Energies 2023, 16, x FOR PEER REVIEW 12 of 25 implement from a computing perspective. These models tend to give relatively good results when used for short-term forecasting of simple time series [24]. The Box-Jenkins method [25] is often used to determine the appropriate order of such models.…”

Heavy Tail and Long-Range Dependence for Skewed Time Series Prediction Based on a Fractional Weibull Process

“…Such kinds of models are based on the idea that current value can be expressed as a linear combination of several past values and a random error. The advantage of the autoregressive model is that the formulation is simple and, thus, the computational complexity is not large [12]. The autoregressive integrated moving average model is proposed for the short-term wind speed forecasting in [13].…”