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Energies 2016, 9(12), 1050; doi:10.3390/en9121050

Research and Application of a Hybrid Forecasting Model Based on Data Decomposition for Electrical Load Forecasting

1
College of Law, Guangxi Normal University, Guilin 541004, China
2
School of Statistics, Dongbei University of Finance and Economics, Dalian 116023, China
3
School of Mathematics and Statistics, Lanzhou University, Lanzhou 730000, China
*
Author to whom correspondence should be addressed.
Academic Editor: Wei-Chiang Hong
Received: 26 October 2016 / Revised: 1 December 2016 / Accepted: 2 December 2016 / Published: 14 December 2016
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Abstract

Accurate short-term electrical load forecasting plays a pivotal role in the national economy and people’s livelihood through providing effective future plans and ensuring a reliable supply of sustainable electricity. Although considerable work has been done to select suitable models and optimize the model parameters to forecast the short-term electrical load, few models are built based on the characteristics of time series, which will have a great impact on the forecasting accuracy. For that reason, this paper proposes a hybrid model based on data decomposition considering periodicity, trend and randomness of the original electrical load time series data. Through preprocessing and analyzing the original time series, the generalized regression neural network optimized by genetic algorithm is used to forecast the short-term electrical load. The experimental results demonstrate that the proposed hybrid model can not only achieve a good fitting ability, but it can also approximate the actual values when dealing with non-linear time series data with periodicity, trend and randomness. View Full-Text
Keywords: electrical load forecasting; data decomposition; genetic algorithm; generalized regression neural network electrical load forecasting; data decomposition; genetic algorithm; generalized regression neural network
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Dong, Y.; Ma, X.; Ma, C.; Wang, J. Research and Application of a Hybrid Forecasting Model Based on Data Decomposition for Electrical Load Forecasting. Energies 2016, 9, 1050.

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