Energy Demand Forecasting: Combining Cointegration Analysis and Artificial Intelligence Algorithm

Junbing Huang,Yuee Tang,Shuxing Chen

doi:10.1155/2018/5194810

Abstract

Energy is vital for the sustainable development of China. Accurate forecasts of annual energy demand are essential to schedule energy supply and provide valuable suggestions for developing related industries. In the existing literature on energy use prediction, the artificial intelligence-based (AI-based) model has received considerable attention. However, few econometric and statistical evidences exist that can prove the reliability of the current AI-based model, an area that still needs to be addressed. In this study, a new energy demand forecasting framework is presented at first. On the basis of historical annual data of electricity usage over the period of 1985–2015, the coefficients of linear and quadratic forms of the AI-based model are optimized by combining an adaptive genetic algorithm and a cointegration analysis shown as an example. Prediction results of the proposed model indicate that the annual growth rate of electricity demand in China will slow down. However, China will continue to demand about 13 trillion kilowatt hours in 2030 because of population growth, economic growth, and urbanization. In addition, the model has greater accuracy and reliability compared with other single optimization methods.

Highlights

Energy, which is a vital input for the economic and social development of any economy, has gained special attention
The artificial intelligence (AI) energy forecasting model, which is a class of machine learning (ML) method, has gained popularity in recent years because of its superiority in time series processing and its capability to deal with noise data
After the long-run equilibrium relationship among the variables is verified, adaptive genetic algorithm (AGA) is employed to optimize the coefficients of (1)-(2) (since we employ the ln form of variables, after carrying out the weights of (1)(2), the electric energy demand can be obtained using Y = eylin or Y = eyqua )

Summary

Introduction

Energy, which is a vital input for the economic and social development of any economy, has gained special attention. The artificial intelligence (AI) energy forecasting model, which is a class of ML method, has gained popularity in recent years because of its superiority in time series processing and its capability to deal with noise data. Several tools, such as artificial neural networks (ANN), genetic algorithm (GA), ant colony optimization (ACO), and particle swarm optimization, are commonly employed in the model [10,11,12,13,14,15,16,17]. Few econometric and statistical evidences are found that can prove the relationship between energy demand and its factors This relationship may change in the long run based on the current AI-based model

Objectives

Methods

Results