Abstract
As a large energy consumer, the building sector accounts for 30–40% of energy consumption and around 40% of carbon emissions. How to improve energy efficiency in the building sector has become an urgent issue in urban sustainable development. Building energy prediction is a flexible and cost-efficient approach to improve energy efficiency. Green buildings can also improve energy efficiency but the energy saving is still lower than expected. Hence, is it is very important to improve the energy efficiency of green buildings. However, research on green building energy consumption prediction is not sufficient. To improve prediction accuracy, an integration model for energy consumption forecast was proposed. Data were collected from a green building for one year period in Shenzhen. Results showed that the proposed model had higher prediction accuracy, which validated the integration model. Meanwhile, the eight typical building operational patterns of energy consumption were identified according to the hour, month and day type. Model can be used to evaluate different design schemes and building operation strategies as well as real-time fault detection and diagnosis. The proposed model will improve the energy efficiency of green buildings; reduce building energy consumption and carbon emissions.
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Acknowledgements
This research was conducted with the support of the National Science Foundation of China (Grant No. 71974132) and Shenzhen Government Basic Research Foundation for Free exploration (Grant No. JCYJ20170818141151733).
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Hu, T., Ding, Z. (2021). An Integrated Prediction Model for Building Energy Consumption: A Case Study. In: Ye, G., Yuan, H., Zuo, J. (eds) Proceedings of the 24th International Symposium on Advancement of Construction Management and Real Estate. CRIOCM 2019. Springer, Singapore. https://doi.org/10.1007/978-981-15-8892-1_116
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DOI: https://doi.org/10.1007/978-981-15-8892-1_116
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