Energy Demand Prediction of the Building Sector Based on Induced Kernel Method and MESSAGEix Model

Xin Tan (), Zijian Zhao (), Changyi Liu (), Shining Zhang (), Xing Chen (), Fangxin Hou (), Fang Yang and Fei Guo ()
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Xin Tan: Climate Change and Environment Office, Global Energy Interconnection Development and Cooperation Organization (GEIDCO), No. 8 Xuanwumennei Street, Xicheng District Beijing, China
Zijian Zhao: Climate Change and Environment Office, Global Energy Interconnection Development and Cooperation Organization (GEIDCO), No. 8 Xuanwumennei Street, Xicheng District Beijing, China
Changyi Liu: Climate Change and Environment Office, Global Energy Interconnection Development and Cooperation Organization (GEIDCO), No. 8 Xuanwumennei Street, Xicheng District Beijing, China
Shining Zhang: Climate Change and Environment Office, Global Energy Interconnection Development and Cooperation Organization (GEIDCO), No. 8 Xuanwumennei Street, Xicheng District Beijing, China
Xing Chen: Climate Change and Environment Office, Global Energy Interconnection Development and Cooperation Organization (GEIDCO), No. 8 Xuanwumennei Street, Xicheng District Beijing, China
Fangxin Hou: Climate Change and Environment Office, Global Energy Interconnection Development and Cooperation Organization (GEIDCO), No. 8 Xuanwumennei Street, Xicheng District Beijing, China
Fang Yang: Climate Change and Environment Office, Global Energy Interconnection Development and Cooperation Organization (GEIDCO), No. 8 Xuanwumennei Street, Xicheng District Beijing, China
Fei Guo: International Institute for Applied Systems Analysis (IIASA), Schlossplatz 1, A-2361 Laxenburg, Austria

Chinese Journal of Urban and Environmental Studies (CJUES), 2019, vol. 07, issue 04, 1-17

Abstract: The building sector, including resident, commercial and public services, is one of the most energy-intensive sectors nowadays. The share of buildings’ energy consumption in the final energy dramatically increases in various scenarios. As the preliminary work of the final energy prediction, the prediction of useful energy demand of the building sector is essential in the fields of energy-related research, especially for the scenarios design. To this end, this paper presents the prediction of energy demand in the building sector based on the Induced Kernel Method (IKM) for the useful energy. First, similar to other learning-based prediction methods, a database is constructed for the training. Specifically, the database contains not only the historical data of the useful energy demand and related indicators, but also some development templates to induce the prediction. Second, the detailed process is mathematically deduced to predict the useful energy demand components of the building sector, including electricity and heating. Finally, using various countries as examples, prediction results of the useful energy are presented in the numerical analysis. Furthermore, by using useful energy prediction results as the input of the MESSAGEix model, the paper further predicts global final energy of the building sector.

Keywords: Energy demand; building sector; kernel learning algorithms (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1142/S2345748119500167

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