Paper Info
Title
DGM (1, 1) model optimized by MVO (multi-verse optimizer) for annual peak load forecasting.
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 MVO-DGM (1, 1)], which employs the latest optimization algorithm MVO (multi-verse 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 MVO-DGM (1, 1) peak load forecasting model has more excellent forecasting performance than other non-optimized forecasting techniques and other optimized DGM (1, 1) models due to its ascended local optima avoidance and better convergence speed. The hybrid MVO-DGM (1, 1) model proposed in this paper is feasible and effective in annual peak load forecasting, which can improve the forecasting accuracy.
Year
DOI
Venue
2018
10.1007/s00521-016-2799-1
Neural Computing and Applications
Keywords
Field
DocType
Annual peak load forecasting, DGM (1, 1), MVO, Hybrid MVO-DGM (1, 1) model, Parameter optimization
Convergence (routing),Particle swarm optimization,Least squares,Mathematical optimization,Renewable energy,Local optimum,Electric power system,Optimization algorithm,Mathematics,Peak load
Journal
Volume
Issue
ISSN
30
6
0941-0643
Citations 
PageRank 
References 
2
0.35
29
Authors
3
Name
Order
Citations
PageRank
Huiru Zhao170.79
Xiaoyu Han220.35
Sen Guo3311.47