Analysis and Forecasting of Monthly Electricity Demand Time Series Using Pattern-Based Statistical Methods

被引：16

作者：

Pelka, Pawel ^{[1
]}

机构：

[1] Czestochowa Tech Univ, Elect Engn Fac, PL-42200 Czestochowa, Poland

来源：

ENERGIES | 2023年 / 16卷 / 02期

关键词：

medium-term load forecasting; pattern-based forecasting; time-series preprocessing; ENERGY-CONSUMPTION; TERM; SYSTEM;

D O I：

10.3390/en16020827

中图分类号：

TE [石油、天然气工业]; TK [能源与动力工程];

学科分类号：

0807 ; 0820 ;

摘要：

This article provides a solution based on statistical methods (ARIMA, ETS, and Prophet) to predict monthly power demand, which approximates the relationship between historical and future demand patterns. The energy demand time series shows seasonal fluctuation cycles, long-term trends, instability, and random noise. In order to simplify the prediction issue, the monthly load time series is represented by an annual cycle pattern, which unifies the data and filters the trends. A simulation study performed on the monthly electricity load time series for 35 European countries confirmed the high accuracy of the proposed models.

引用

页数：22

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