Research and Application of a Novel Hybrid Model Based on a Deep Neural Network for Electricity Load Forecasting: A Case Study in Australia

Kailai Ni, Jianzhou Wang, Guangyu Tang and Danxiang Wei
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Kailai Ni: School of Statistics, Dongbei University of Finance and Economics, Dalian 116025, China
Jianzhou Wang: School of Statistics, Dongbei University of Finance and Economics, Dalian 116025, China
Guangyu Tang: School of Statistics, Dongbei University of Finance and Economics, Dalian 116025, China
Danxiang Wei: School of Statistics, Dongbei University of Finance and Economics, Dalian 116025, China

Energies, 2019, vol. 12, issue 13, 1-30

Abstract: Electricity load forecasting plays an essential role in improving the management efficiency of power generation systems. A large number of load forecasting models aiming at promoting the forecasting effectiveness have been put forward in the past. However, many traditional models have no consideration for the significance of data preprocessing and the constraints of individual forecasting models. Moreover, most of them only focus on the forecasting accuracy but ignore the forecasting stability, resulting in nonoptimal performance in practical applications. This paper presents a novel hybrid model that combines an advanced data preprocessing strategy, a deep neural network, and an avant-garde multi-objective optimization algorithm, overcoming the defects of traditional models and thus improving the forecasting performance effectively. In order to evaluate the validity of the proposed hybrid model, the electricity load data sampled in 30-min intervals from Queensland, Australia are used as a case to study. The experiments show that the new proposed model is obviously superior to all other traditional models. Furthermore, it provides an effective technical forecasting means for smart grid management.

Keywords: electricity load forecasting; hybrid model; data preprocessing strategy; multi-objective optimization algorithm; deep neural network (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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