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,Danxiang Wei,Jianzhou Wang,Guangyu Tang

doi:10.3390/en12132467

Kailai Ni, Danxiang Wei + Show 2 more

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https://doi.org/10.3390/en12132467

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Journal: Energies	Publication Date: Jun 26, 2019
Citations: 18	License type: CC BY 4.0

Affiliation: Dongbei University of Finance and Economics

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.

Highlights

IntroductionWith the development of productivity and society, the demand for electricity for production and living is growing constantly, which has led to an increased difficulty in power system management
With the development of productivity and society, the demand for electricity for production and living is growing constantly, which has led to an increased difficulty in power system management.Against this background, electricity load forecasting is of great help for the decision-making process of power market participants and regulators [1,2]
To meet the special requirements of load forecasting, an excellent hybrid model was proposed in this paper that integrates an advanced data preprocessing strategy, a powerful multi-objective optimization algorithm, and a cutting-edge deep neural network

Summary

Introduction

With the development of productivity and society, the demand for electricity for production and living is growing constantly, which has led to an increased difficulty in power system management. Against this background, electricity load forecasting is of great help for the decision-making process of power market participants and regulators [1,2]. Exaggerated forecasting can lead to excessive electricity production, which increases unnecessary operating costs and wastes energy. Inadequate forecasting can lead to a shortage in energy production, posing political, economic, and security threats to a country or a region. Many models have been proposed in the field of load forecasting, which can be divided into three general types: statistical models, artificial intelligence (AI) models, and hybrid models

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