An Optimized Forecasting Approach Based on Grey Theory and Cuckoo Search Algorithm: A Case Study for Electricity Consumption in New South Wales
Open Access
- 1 January 2014
- journal article
- research article
- Published by Hindawi Limited in Abstract and Applied Analysis
- Vol. 2014, 1-13
- https://doi.org/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.Keywords
Funding Information
- National Natural Science Foundation of China (70973015)
This publication has 38 references indexed in Scilit:
- Short-term power load forecasting using grey correlation contest modelingExpert Systems with Applications, 2012
- The Forecasting of Net Electricity Consumption of the Consumer Groups in TurkeyEnergy Sources, Part B: Economics, Planning, and Policy, 2011
- Electricity consumption prediction with functional linear regression using spline estimatorsJournal of Applied Statistics, 2010
- Electricity consumption and economic growth in seven South American countriesEnergy Policy, 2010
- Annual electricity consumption forecasting by neural network in high energy consuming industrial sectorsEnergy Conversion and Management, 2008
- The causal relationship between electricity consumption and economic growth in the ASEAN countriesEnergy Policy, 2006
- Adaptive nonlinear state-space modelling for the prediction of daily mean PM10 concentrationsEnvironmental Modelling & Software, 2006
- Forecasting electricity consumption in New Zealand using economic and demographic variablesEnergy, 2005
- Electricity consumption and economic growth in IndiaEnergy Policy, 2002
- Neural networks in forecasting electrical energy consumption: univariate and multivariate approachesInternational Journal of Energy Research, 2001