散热器(发动机冷却)
能源性能
高效能源利用
恒温器
能量(信号处理)
工程类
计算机科学
模拟
建筑工程
系统工程
工业工程
机械工程
汽车工程
电气工程
数学
统计
作者
Massimiliano Manfren,P.A.B. James,Victoria Aragon,Lamberto Tronchin
出处
期刊:Energy and AI
[Elsevier]
日期:2023-10-01
卷期号:14: 100304-100304
被引量:14
标识
DOI:10.1016/j.egyai.2023.100304
摘要
The transition to low carbon energy systems poses challenges in terms of energy efficiency. In building refurbishment projects, efficient technologies such as smart controls and heat pumps are increasingly being used as a substitute for conventional technologies with the aim of reducing carbon emissions and determining operational energy and cost savings, together with other benefits. Measured building performance, however, often reveals a significant gap between the predicted energy use (design stage) and actual energy use (operation stage). For this reason, lean and interpretable digital twins are needed for building energy monitoring aimed at persistence of savings and continuous performance improvement. In this research, interpretable regression models are built with data at multiple temporal resolutions (monthly, daily and hourly) and seamlessly integrated with the goal of verifying the performance improvements due to Smart Thermostatic Radiator Valves (TRVs) and Gas Absorption Heat Pumps (GAHPs) as well as giving insights on the performance of the building as a whole. Further, as part of modelling research, Time Of Week and Temperature (TOWT) approach is reformulated and benchmarked against its original implementation. The case study chosen is Hale Court sheltered housing, located in the city of Portsmouth (UK). This building has been used for the field-testing of innovative technologies such as TRVs and GAHPs within the EU Horizon 2020 project THERMOSS. The results obtained are used to illustrate possible extensions of the use of energy signature modelling, highlighting implications for energy management and innovative building technologies development.
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