Open Access
2014 An Optimized Forecasting Approach Based on Grey Theory and Cuckoo Search Algorithm: A Case Study for Electricity Consumption in New South Wales
Ping Jiang, Qingping Zhou, Haiyan Jiang, Yao Dong
Abstr. Appl. Anal. 2014(SI11): 1-13 (2014). DOI: 10.1155/2014/183095

Abstract

With rapid economic growth, electricity demand is clearly increasing. It is difficult to store electricity for future use; thus, the electricity demand forecast, especially the electricity consumption forecast, is crucial for planning and operating a power system. Due to various unstable factors, it is challenging to forecast electricity consumption. Therefore, it is necessary to establish new models for accurate forecasts. This study proposes a hybrid model, which includes data selection, an abnormality analysis, a feasibility test, and an optimized grey model to forecast electricity consumption. First, the original electricity consumption data are selected to construct different schemes (Scheme 1: short-term selection and Scheme 2: long-term selection); next, the iterative algorithm (IA) and cuckoo search algorithm (CS) are employed to select the best parameter of GM(1,1). The forecasted day is then divided into several smooth parts because the grey model is highly accurate in the smooth rise and drop phases; thus, the best scheme for each part is determined using the grey correlation coefficient. Finally, the experimental results indicate that the GM(1,1) optimized using CS has the highest forecasting accuracy compared with the GM(1,1) and the GM(1,1) optimized using the IA and the autoregressive integrated moving average (ARIMA) model.

Citation

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Ping Jiang. Qingping Zhou. Haiyan Jiang. Yao Dong. "An Optimized Forecasting Approach Based on Grey Theory and Cuckoo Search Algorithm: A Case Study for Electricity Consumption in New South Wales." Abstr. Appl. Anal. 2014 (SI11) 1 - 13, 2014. https://doi.org/10.1155/2014/183095

Information

Published: 2014
First available in Project Euclid: 6 October 2014

zbMATH: 07021888
Digital Object Identifier: 10.1155/2014/183095

Rights: Copyright © 2014 Hindawi

Vol.2014 • No. SI11 • 2014
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