Prediction of energy consumption: Variable regression or time series? A case in China

Abstract

Energy consumption is closely related to industrial structure, economic prosperity, and population. Because of the Granger causal relationship between GDP and energy consumption, many researchers consider the regression method to predict energy consumption using economic variables. However, some other researchers regard the time series method (forecast) to estimate for energy consumption. To address the advantages and disadvantages of two methods in energy consumption prediction, we performed the deep learning (DL) based on the case of China. The experiment results show that the accuracy of time series forecast is much higher than regression methods. In addition, we also explored the relationship between industry, economy, population, and energy consumption. We found that GDP, population, secondary sector, and tertiary sector were significant to energy consumption. A tertiary sector can help to reduce energy consumption to some extent. This could give suggestions about changing industry structure. More and more attention has been paid to energy intensity. The strategic approach to sustainable development requires reducing energy intensity while developing the economy. Since China is committed to achieving carbon emission peak by 2030, this paper predicts the energy intensity of China in the next decade based on a DL algorithm. The prediction results show that energy intensity would slightly decrease in the next decade. Different types of energy represent the industrial structure behind economic growth. Therefore, this paper was conducted using ridge regression (RR) to explore the relationship between different types of energy and energy intensity. In addition, the effect (positive or negative) of different energy sources in reducing energy intensity has also been explored. We found that the best way of developing the economy and reducing energy consumption is to use natural gas and hydroelectric instead of coal. This can guide policy development about changing energy source structure.

Socio-economic productive capacities and energy efficiency: global evidence by income level and resource dependence

“…Consistently, Atalla and Bean ( 2017 ) verify that economic shifts from industry to service-led sectors improve overall energy productivity and the growth of industrial production lowers energy productivity in 39 countries during 1995–2009. Similar results are found by Li ( 2019 ) based on China’s 1965–2017 data.…”

A panoramic view and swot analysis of artificial intelligence for achieving the sustainable development goals by 2030: progress and prospects

“…12 Recommendations on using AI in the SDGs of environmental dimension: resources and intelligent management of electrical energy networks (e.g. smart grids), as well as enabling an efficient and predictive exploitation of renewable energy [135][136][137].…”

Enerji Tüketim Tahmini İçin Farklı Regresyon Algoritmalarının Analizi

“…These developed models are compared with the artificial neural network model. (Li 2019), states that there is a close relationship between economic development, population, industrial relations, and energy consumption. It has been determined that many researchers use regression method to solve the relationship between these variables and energy consumption.…”

“…Long short-term memory (LSTM) is defined as a customized version of the repetitive neural network model RNN (Li 2019). RNN and LSTM can be defined as an extension or extension of the classical artificial neural network model.…”

“…According to (Günay 2016), it is not sufficient to explain the increase in energy consumption only by population growth over the years. To understand the relationship between energy consumption data according to (Li 2019) and the factors that affect them, there is a disagreement about whether to use regression analysis or perform analyses over time series. In the studies, it has been observed that estimating the energy consumption from the data collected regularly at certain time intervals gives better results than the regression methods.…”