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Title of Journal: Neural Comput Applic

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Abbravation: Neural Computing and Applications

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Springer London

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DOI

10.1016/0148-9062(93)93068-9

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ISSN

1433-3058

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DGM 1 1 model optimized by MVO multiverse opt

Authors: Huiru Zhao Xiaoyu Han Sen Guo
Publish Date: 2016/12/26
Volume: 30, Issue: 6, Pages: 1811-1825
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Abstract

A large number of renewable energies and uncertain power load accessing electric power system make the power load forecasting more complicated and face more new challenges This paper presents a hybrid annual peak load forecasting model namely MVODGM 1 1 which employs the latest optimization algorithm MVO multiverse optimizer to determine two parameters of DGM 1 1 model and then uses the optimized DGM 1 1 model to forecast annual peak load The annual peak load of Shandong province in China from 2005 to 2014 is selected as the empirical example and the analysis results demonstrate that the MVO algorithm for parameters’ determination of DGM 1 1 model has significant superiority over the least square estimation method particle swarm optimization and fruit fly optimization algorithm in terms of annual peak load forecasting In addition the proposed MVODGM 1 1 peak load forecasting model has more excellent forecasting performance than other nonoptimized forecasting techniques and other optimized DGM 1 1 models due to its ascended local optima avoidance and better convergence speed The hybrid MVODGM 1 1 model proposed in this paper is feasible and effective in annual peak load forecasting which can improve the forecasting accuracy

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