Energy Demand Forecasting: Combining Cointegration Analysis and Artificial Intelligence Algorithm

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.

Aplicação de modelos de previsão e inteligência artificial para avaliar demanda no segmento de sistemas de iluminação automotivo

“…Métodos de Inteligência Artificial como a RNA estão sendo utilizados, com mais frequência, nas previsões de demanda, pois vêm apresentando relevantes acuracidades nas análises, gerando menor erro médio absoluto ( ) e erro percentual médio absoluto ( ) em comparação com modelos tradicionais [20]. Técnicas de Inteligência Artificial foram e estão sendo implementadas com sucesso em áreas como biomedicina, aplicações aeroespaciais, indústria automotiva, eletrônica, indústria financeira, entre outras [21].…”

Micro-Genetic Algorithm Embedded Multi-Population Differential Evolution based Neural Network for Short-Term Load Forecasting

“…which attain competitive advantages for nonlinear load mapping and generalisation, although offering hinderances to criteria making and parameter setting. With the advancements in technology and computer aided predictions, the process of power load forecasting has seen improvements via ANN, wavelet transforms, fuzzy algorithms, SVM etc [8][9][10][11].…”

Forecasting of Chinese E-Commerce Sales: An Empirical Comparison of ARIMA, Nonlinear Autoregressive Neural Network, and a Combined ARIMA-NARNN Model

“…Previous studies show that ARIMA and NARNN approaches have better prediction performance in time series prediction [16][17][18][19][20][21][22][23][24]. For example, Cheng C and Qin P [16] used the ARIMA model to predict the time series data of settlement of seawalls and got higher accuracy than the gray prediction.…”

“…Also, Bonetto R and Rossi M [24] used the NARNN model to predict the power demand and get a good predictive effect. In time series prediction of ECS, currently, most scholars [16][17][18][19][20][21][22][23][24][25][25][26][27][28][29][30][31] However, in the e-commerce industry, the types of products are very numerous; that is to say, there are more than one time series to be predicted. Moreover, the sales volume of e-commerce products fluctuates greatly and is easy to be affected by many factors such as price, promotion, ranking, etc.…”