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.
Highlights
Forecasting of electric energy consumption plays an important role in the operation of thermal power plants and is one of the most important basis for coal dispatch, electricity trading, and so on
Reconstructed results with a small amplitude and an indistinct wave period are eliminated; second, the rising trend is modeled using grey model (GM) (1, 1), and other periodical waves are determined using the RBF neural networks (NNs); third, the forecasting results of monthly electric energy consumption are obtained by adding the forecasting values of the previous models
Compared with the traditional monthly electric energy consumption forecasting method proposed by González–Romera et al [21], two primary innovations are notable in Method 1
Summary
Forecasting of electric energy consumption plays an important role in the operation of thermal power plants and is one of the most important basis for coal dispatch, electricity trading, and so on. The monthly consumption trends often comprise at least three kinds of sub-trends, namely, a long-term rising trend, numerous periodical waves, and the stochastic series Classical techniques, such as regression [1] and expert systems [2], are incapable of generating precise forecasting results because of low adaptability. González–Romera et al [19,20,21] adopted a moving average algorithm to extract the rising trend from a monthly electric energy demand series. The periodic wave is forecasted using the Fourier series, whereas the rising trend is predicted by NNs. The extracting techniques, combined with other methods, were proven to be capable of yielding better forecasting results compared with the time series and single NN methods.
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