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Forecasting Monthly Electric Energy Consumption Using Feature Extraction
School of Economics and Management, North China Electric Power University, Baoding 071003, Hebei, China
* Author to whom correspondence should be addressed.
Received: 22 July 2011; in revised form: 14 September 2011 / Accepted: 20 September 2011 / Published: 28 September 2011
Abstract: Monthly forecasting of electric energy consumption is important for planning the generation and distribution of power utilities. However, the features of this time series are so complex that directly modeling is difficult. Three kinds of relatively simple series can be derived when a discrete wavelet transform is used to extract the raw features, namely, the rising trend, periodic waves, and stochastic series. After the elimination of the stochastic series, the rising trend and periodic waves were modeled separately by a grey model and radio basis function neural networks. Adding the forecasting values of each model can yield the forecasting results for monthly electricity consumption. The grey model has a good capability for simulating any smoothing convex trend. In addition, this model can mitigate minor stochastic effects on the rising trend. The extracted periodic wave series, which contain relatively less information and comprise simple regular waves, can improve the generalization capability of neural networks. The case study on electric energy consumption in China shows that the proposed method is better than those traditionally used in terms of both forecasting precision and expected risk.
Keywords: monthly electric energy consumption; forecasting; feature extraction; discrete wavelet transform; neural network; grey model
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MDPI and ACS Style
Meng, M.; Niu, D.; Sun, W. Forecasting Monthly Electric Energy Consumption Using Feature Extraction. Energies 2011, 4, 1495-1507.
Meng M, Niu D, Sun W. Forecasting Monthly Electric Energy Consumption Using Feature Extraction. Energies. 2011; 4(10):1495-1507.
Meng, Ming; Niu, Dongxiao; Sun, Wei. 2011. "Forecasting Monthly Electric Energy Consumption Using Feature Extraction." Energies 4, no. 10: 1495-1507.