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Energies 2016, 9(8), 640; doi:10.3390/en9080640

Comparative Study of Hybrid Models Based on a Series of Optimization Algorithms and Their Application in Energy System Forecasting

School of Statistics, Dongbei University of Finance and Economics, Dalian 116023, China
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Academic Editor: José C. Riquelme
Received: 4 April 2016 / Revised: 14 July 2016 / Accepted: 2 August 2016 / Published: 16 August 2016
(This article belongs to the Special Issue Energy Time Series Forecasting)
View Full-Text   |   Download PDF [7019 KB, uploaded 16 August 2016]   |  

Abstract

Big data mining, analysis, and forecasting play vital roles in modern economic and industrial fields, especially in the energy system. Inaccurate forecasting may cause wastes of scarce energy or electricity shortages. However, forecasting in the energy system has proven to be a challenging task due to various unstable factors, such as high fluctuations, autocorrelation and stochastic volatility. To forecast time series data by using hybrid models is a feasible alternative of conventional single forecasting modelling approaches. This paper develops a group of hybrid models to solve the problems above by eliminating the noise in the original data sequence and optimizing the parameters in a back propagation neural network. One of contributions of this paper is to integrate the existing algorithms and models, which jointly show advances over the present state of the art. The results of comparative studies demonstrate that the hybrid models proposed not only satisfactorily approximate the actual value but also can be an effective tool in the planning and dispatching of smart grids. View Full-Text
Keywords: energy system; comparative study; optimization algorithms; forecasting validity degree; time series forecasting energy system; comparative study; optimization algorithms; forecasting validity degree; time series forecasting
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Ma, X.; Liu, D. Comparative Study of Hybrid Models Based on a Series of Optimization Algorithms and Their Application in Energy System Forecasting. Energies 2016, 9, 640.

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