An ARMA cooperate with artificial neural network approach in short-term load forecasting

Authors:
Wang Jian-Jun;Niu Dong-Xiao;Li Li
Affiliations:
School of Business Administration, North China Electric Power University, Beijing, China;School of Business Administration, North China Electric Power University, Beijing, China;School of Business Administration, North China Electric Power University, Beijing, China
Venue:
ICNC'09 Proceedings of the 5th international conference on Natural computation
Year:
2009

Citing 4
Cited 0

Time Series Analysis, Forecasting and Control

Time Series Analysis, Forecasting and Control
Data Mining with Computational Intelligence (Advanced Information and Knowledge Processing)

Data Mining with Computational Intelligence (Advanced Information and Knowledge Processing)
Prediction of chaotic time series based on the recurrent predictor neural network

IEEE Transactions on Signal Processing
Hybrid intelligent systems for time series prediction using neural networks, fuzzy logic, and fractal theory

IEEE Transactions on Neural Networks

Quantified Score

Hi-index	0.00

Visualization

Abstract

Short-term load forecasting is important for electricity load planning and dispatches the loading of generating units in order to meet the electricity system demand. The precision of the load forecasting is related to electricity company's economic. This paper presents a approach named an autoregressive moving average(ARMA) cooperate with BP Artificial Neural Network(BPNN) approach, which can combine the linear component and nonlinear component at the same time. the experiment result shows that the MAPE of this method is 0.92%, and MSE is 17.07, compared to single ARMA's MAPE 2.08% and MSE 47.65 or BPNN's MAPE 2.63% and MSE 56.91, this method is outperform the single ARMA and BPNN forecast method.