Energy consumption prediction and diagnosis of public buildings based on support vector machine learning: A case study in China

Author:

Liu Yang,Chen Hongyu,Zhang Limao,Wu Xianguo,Wang Xian-jia

Funder

Nation Natural Science Foundation of China

Publisher

Elsevier BV

Subject

Industrial and Manufacturing Engineering,Strategy and Management,General Environmental Science,Renewable Energy, Sustainability and the Environment

Reference37 articles.

1. Real-time prediction model for indoor temperature in a commercial building;Afroz;Appl. Energy,2018

2. A methodology to estimate baseline energy use and quantify savings in electrical energy consumption in higher education institution buildings: case study, Federal University of Itajuba (UNIFEI);Batlle;J. Clean. Prod.,2020

3. Chinese Building Energy Consumption Report (2018). Beijing, PR China,2018

4. Chinese residential electricity consumption: estimation and forecast using micro-data;Cao;Resour. Energy Econ.,2019

5. A bottom-up approach to residential load modeling;Capasso;IEEE Transactions on Power Systems,1994

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