Energy consumption analysis based on machine learning model

Haibin Li; Zhenjia Jin; Rui Liu

doi:10.1117/12.2687739

24 October 2023 Energy consumption analysis based on machine learning model

Haibin Li, Zhenjia Jin, Rui Liu

Proceedings Volume 12804, Second International Conference on Sustainable Technology and Management (ICSTM 2023); 128040P (2023) https://doi.org/10.1117/12.2687739
Event: 2nd International Conference on Sustainable Technology and Management (ICSTM2023), 2023, Dongguan, China

Abstract

Effective building energy analysis techniques are the core to achieve low energy consumption. However, due to the difference between the predicted value and the actual value, the reliability of existing energy consumption simulation techniques has been questioned by practitioners. In recent years, with the development of building performance certification and measurement, more and more research institutions have collected a large amount of building performance information data, which makes it possible to analyze building energy consumption driven by data. Based on the theory of data mining and the demand of building energy consumption analysis, a theoretical framework of data-driven building energy consumption analysis and energy conservation design is proposed. A building energy consumption information model, which is used to collect building energy consumption information, is designed to lay a foundation for collecting a large number of building information and forming a database. Aiming at different energy-saving design problems of a single building, a set of solutions based on applied statistics and machine learning are proposed. Theoretically, it expands the theory and practice methods of building energy consumption analysis and energy saving design and deepens the understanding of building energy consumption data. In terms of technology, this study provides specific implementation methods for different energy-saving design problems, which lays a foundation for engineering practice.

Citation Download Citation

Haibin Li, Zhenjia Jin, and Rui Liu "Energy consumption analysis based on machine learning model", Proc. SPIE 12804, Second International Conference on Sustainable Technology and Management (ICSTM 2023), 128040P (24 October 2023); https://doi.org/10.1117/12.2687739

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KEYWORDS

Design and modelling

Data modeling

Analytical research

Machine learning

Windows

Visual process modeling

Statistical analysis

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