Study on A Simple Model to Forecast the Electricity Demand under China’s New Normal Situation
A simple model was built to predict the national and regional electricity demand by sectors under China’s new normal situation. In the model, the data dimensionality reduction method and the Grey model (GM(1,1)) were combined and adopted to disaggregate the national economic growth rate in...
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doaj-4328199ec5ee4736b636656dd92a56252020-11-25T00:25:58ZengMDPI AGEnergies1996-10732019-06-011211222010.3390/en12112220en12112220Study on A Simple Model to Forecast the Electricity Demand under China’s New Normal SituationJinchai Lin0Kaiwei Zhu1Zhen Liu2Jenny Lieu3Xianchun Tan4Economics and Management School, Wuhan University, Wuhan 430072, ChinaInstitutes of Science and Development, Chinese Academy of Sciences, Bejing 100190, ChinaLow-carbon Energy Research Center, Chongqing University of Technology, Chongqing 400054, ChinaETH Zurich, Transdisciplinarity Lab (USYS TdLab), Universitätstrasse 16, 8092 Zürich, SwitzerlandInstitutes of Science and Development, Chinese Academy of Sciences, Bejing 100190, ChinaA simple model was built to predict the national and regional electricity demand by sectors under China’s new normal situation. In the model, the data dimensionality reduction method and the Grey model (GM(1,1)) were combined and adopted to disaggregate the national economic growth rate into regional levels and forecast each region’s contribution rate to the national economic growth and regional industrial structure. Then, a bottom−up accounting model that considered the impacts of regional industrial structure transformation, regional energy efficiency, and regional household electric consumption was built to predict national and regional electric demand. Based on the predicted values, this paper analyzed the spatial changes in electric demand, and our results indicate the following. Firstly, the proposed model has high accuracy in national electricity demand prediction: the relative error in 2017 and 2018 was 2.90% and 2.60%, respectively. Secondly, China’s electric demand will not peak before 2025, and it is estimated to be between 7772.16 and 8458.85 billion kW·h in 2025, which is an increase of 31.28−42.88% compared with the total electricity consumption in 2016. The proportion of electricity demand in the mid-west regions will increase, while the eastern region will continue to be the country’s load center. Thirdly, under China’s new normal, households and the tertiary industry will be the main driving forces behind the increases in electric demand. Lastly, the drop in China’s economy under the new normal will lead to a decline in the total electricity demand, but it will not evidently change the electricity consumption share of the primary industry, secondary industry, tertiary industry, and household sector.https://www.mdpi.com/1996-1073/12/11/2220China’s new normalelectricity demandindustrial structure transformationspatial distributiondata dimensionality reduction |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Jinchai Lin Kaiwei Zhu Zhen Liu Jenny Lieu Xianchun Tan |
spellingShingle |
Jinchai Lin Kaiwei Zhu Zhen Liu Jenny Lieu Xianchun Tan Study on A Simple Model to Forecast the Electricity Demand under China’s New Normal Situation Energies China’s new normal electricity demand industrial structure transformation spatial distribution data dimensionality reduction |
author_facet |
Jinchai Lin Kaiwei Zhu Zhen Liu Jenny Lieu Xianchun Tan |
author_sort |
Jinchai Lin |
title |
Study on A Simple Model to Forecast the Electricity Demand under China’s New Normal Situation |
title_short |
Study on A Simple Model to Forecast the Electricity Demand under China’s New Normal Situation |
title_full |
Study on A Simple Model to Forecast the Electricity Demand under China’s New Normal Situation |
title_fullStr |
Study on A Simple Model to Forecast the Electricity Demand under China’s New Normal Situation |
title_full_unstemmed |
Study on A Simple Model to Forecast the Electricity Demand under China’s New Normal Situation |
title_sort |
study on a simple model to forecast the electricity demand under china’s new normal situation |
publisher |
MDPI AG |
series |
Energies |
issn |
1996-1073 |
publishDate |
2019-06-01 |
description |
A simple model was built to predict the national and regional electricity demand by sectors under China’s new normal situation. In the model, the data dimensionality reduction method and the Grey model (GM(1,1)) were combined and adopted to disaggregate the national economic growth rate into regional levels and forecast each region’s contribution rate to the national economic growth and regional industrial structure. Then, a bottom−up accounting model that considered the impacts of regional industrial structure transformation, regional energy efficiency, and regional household electric consumption was built to predict national and regional electric demand. Based on the predicted values, this paper analyzed the spatial changes in electric demand, and our results indicate the following. Firstly, the proposed model has high accuracy in national electricity demand prediction: the relative error in 2017 and 2018 was 2.90% and 2.60%, respectively. Secondly, China’s electric demand will not peak before 2025, and it is estimated to be between 7772.16 and 8458.85 billion kW·h in 2025, which is an increase of 31.28−42.88% compared with the total electricity consumption in 2016. The proportion of electricity demand in the mid-west regions will increase, while the eastern region will continue to be the country’s load center. Thirdly, under China’s new normal, households and the tertiary industry will be the main driving forces behind the increases in electric demand. Lastly, the drop in China’s economy under the new normal will lead to a decline in the total electricity demand, but it will not evidently change the electricity consumption share of the primary industry, secondary industry, tertiary industry, and household sector. |
topic |
China’s new normal electricity demand industrial structure transformation spatial distribution data dimensionality reduction |
url |
https://www.mdpi.com/1996-1073/12/11/2220 |
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