A new ARMAX model based on evolutionary algorithm and particle swarm optimization for short-term load forecasting

Electric Power Systems ResearchPublished 21 April 2008

Bo Wang, Nengling Tai, Zhai Hai-qing, Jian Ye, Jiadong Zhu, Qi Liangbo

DOI10.1016/j.epsr.2008.02.009

Source URL

Citations79

SJR quartileQ1

SJR score1.14

SNIP1.37

TL;DR

The new ARMAX model for short-term load forecasting takes advantage of evolutionary strategy to speed up the convergence of particle swarm optimization (PSO), and applies the crossover operation of genetic algorithm to enhance the global search ability.

Abstract

In this paper, a new ARMAX model based on evolutionary algorithm and particle swarm optimization for short-term load forecasting is proposed. Auto-regressive (AR) and moving average (MA) with exogenous variables (ARMAX) has been widely applied in the load forecasting area. Because of the nonlinear characteristics of the power system loads, the forecasting function has many local optimal points. The traditional method based on gradient searching may be trapped in local optimal points and lead to high error. While, the hybrid method based on evolutionary algorithm and particle swarm optimization can solve this problem more efficiently than the traditional ways. It takes advantage of evolutionary strategy to speed up the convergence of particle swarm optimization (PSO), and applies the crossover operation of genetic algorithm to enhance the global search ability. The new ARMAX model for short-term load forecasting has been tested based on the load data of Eastern China location market, and the results indicate that the proposed approach has achieved good accuracy.

Keywords

Decision SciencesEngineeringEnvironmental Science

Journal of Basic EngineeringA New Approach to Linear Filtering and Prediction Problems

30,999 Citations1960R. E. Kalman

IEEE Transactions on Power SystemsNeural networks for short-term load forecasting: a review and evaluation

2,202 Citations2001Henrique S. Hippert, Carlos E. Pedreira +1 more

This review examines a collection of papers (published between 1991 and 1999) that report the application of NNs to short-term load forecasting, and critically evaluating the ways in which the NNs proposed in these papers were designed and tested.

IEEE Transactions on Power SystemsLoad Forecasting Using Support Vector Machines: A Study on EUNITE Competition 2001

843 Citations2004Bing Chen, Ming‐Wei Chang +1 more

How SVM, a new learning technique, is successfully applied to load forecasting is discussed in detail and some important conclusions are that temperature might not be useful in such a mid-term load forecasting problem and that the introduction of time-series concept may improve the forecasting.

IEEE Transactions on Power SystemsShort-term hourly load forecasting using time-series modeling with peak load estimation capability

507 Citations2001Nima Amjady

This paper presents a new time series modeling for short term load forecasting, which can model the valuable experiences of the expert operators and can exactly forecast the daily peak load of a power system.

IEEE Transactions on Evolutionary ComputationApplication of a fuzzy neural network combined with a chaos genetic algorithm and simulated annealing to short-term load forecasting

200 Citations2006Gwo-Ching Liao, Ta‐Peng Tsao

A fuzzy neural network combined with a chaos-search genetic algorithm (CGA) and simulated annealing (SA) applied to short-term power-system load forecasting as a sample test demonstrates an encouraging degree of accuracy superior to other commonly used forecasting methods available.

IEEE Transactions on Power SystemsA new short-term load forecasting approach using self-organizing fuzzy ARMAX models

161 Citations1998Hong‐Tzer Yang, Chaoming Huang

A combined use of heuristics and evolutionary programming (EP) scheme is relied on to solve the problem of determining optimal number of input variables, best partition of fuzzy spaces and associated fuzzy membership functions in the FARMAX model.

IEEE Transactions on Power SystemsNext Day Load Curve Forecasting Using Hybrid Correction Method

136 Citations2005Tomonobu Senjyu, Paras Mandal +2 more

The test results show that the proposed forecasting method could provide a considerable improvement of the forecasting accuracy especially as it shows how to reduce neural network forecast error over the test period by 23% through the application of a fuzzy logic correction.

NEW SHORT-TERM LOAD FORECASTING PRINCIPLE WITH THE WAVELET TRANSFORM FUZZY NEURAL NETWORK FOR THE POWER SYSTEMS

15 Citations2004Nengling Tai

This paper presents a new method based on the wavelet transform (WT) fuzzy neural network for the short-term load forecasting that can provide forecast precision.

IEEE Power Engineering Society General Meeting, 2005Weather sensitive short-term load forecasting using knowledge-based ARX models

14 Citations2005H. Chen, Yijun Du +1 more

An advanced evolutionary algorithm for load forecasting with the Kalman filter

6 Citations2000Z.S.H. Chan

Results show that although the EA requires more function evaluations, it outperforms the EM algorithm consistently and is benchmarked with that of the expectation-maximization algorithm in minimizing the mean-square-error of the KF prediction.