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Energies 2017, 10(4), 490; doi:10.3390/en10040490

Research and Application of Hybrid Forecasting Model Based on an Optimal Feature Selection System—A Case Study on Electrical Load Forecasting

1
School of Mathematics and Statics, Lanzhou University, Lanzhou 730000, China
2
School of Statistics, Dongbei University of Finance & Economics, Dalian 116000, China
3
State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 10029, China
*
Author to whom correspondence should be addressed.
Academic Editor: Pierluigi Siano
Received: 30 January 2017 / Revised: 10 March 2017 / Accepted: 27 March 2017 / Published: 5 April 2017
(This article belongs to the Special Issue Innovative Methods for Smart Grids Planning and Management)
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Abstract

The process of modernizing smart grid prominently increases the complexity and uncertainty in scheduling and operation of power systems, and, in order to develop a more reliable, flexible, efficient and resilient grid, electrical load forecasting is not only an important key but is still a difficult and challenging task as well. In this paper, a short-term electrical load forecasting model, with a unit for feature learning named Pyramid System and recurrent neural networks, has been developed and it can effectively promote the stability and security of the power grid. Nine types of methods for feature learning are compared in this work to select the best one for learning target, and two criteria have been employed to evaluate the accuracy of the prediction intervals. Furthermore, an electrical load forecasting method based on recurrent neural networks has been formed to achieve the relational diagram of historical data, and, to be specific, the proposed techniques are applied to electrical load forecasting using the data collected from New South Wales, Australia. The simulation results show that the proposed hybrid models can not only satisfactorily approximate the actual value but they are also able to be effective tools in the planning of smart grids. View Full-Text
Keywords: electrical load forecasting; feature learning; global recurrent networks; forecasting validity degree electrical load forecasting; feature learning; global recurrent networks; forecasting validity degree
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Dong, Y.; Wang, J.; Wang, C.; Guo, Z. Research and Application of Hybrid Forecasting Model Based on an Optimal Feature Selection System—A Case Study on Electrical Load Forecasting. Energies 2017, 10, 490.

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