Probabilistic modelling of occupants’ thermostat preferences for residential building energy simulation and rating

IF 2.2 4区工程技术 Q2 CONSTRUCTION & BUILDING TECHNOLOGY

Journal of Building Performance Simulation Pub Date : 2022-11-21 DOI:10.1080/19401493.2022.2147674

Dilini Wickrama Achchige, Dong Chen, G. Kokogiannakis, M. Fiorentini

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引用次数: 2

Abstract

ABSTRACT Fixed thermostat setpoints and schedules are commonly used in residential building energy simulation and rating. While this approach is simple to implement, it does not represent occupants with varying preferences. In this study, based on field data from 102 households in three Australian cities, two alternative thermostat setting approaches were investigated. The first method (Probability Distribution Approach) uses all the values in a thermostat settings probability distribution generated from the field data. This was compared with a more straightforward method, where the thermostat settings were derived by applying weighted average thermostat settings. Both approaches were benchmarked against a series of simulations that used randomly generated thermostat settings with the same thermostat settings probability distributions. Results show that the Probability Distribution Approach matches better the benchmarking results (CV(RMSE) 1-8%) than the weighted average method (CV(RMSE) 9-37%), particularly for cooling demand.

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住宅建筑能源模拟与评级中住户恒温器偏好的概率建模

固定温控器设定值和时间表是住宅建筑能耗模拟和评定中常用的一种方法。虽然这种方法很容易实现，但它并不能代表具有不同偏好的居住者。在这项研究中，基于来自澳大利亚三个城市102户家庭的现场数据，研究了两种可选的恒温器设置方法。第一种方法(概率分布方法)使用从现场数据生成的恒温器设置概率分布中的所有值。这与一种更直接的方法进行了比较，其中恒温器设置是通过应用加权平均恒温器设置得来的。这两种方法都与一系列模拟进行了基准测试，这些模拟使用随机生成的恒温器设置和相同的恒温器设置概率分布。结果表明，概率分布方法比加权平均方法(CV(RMSE) 9-37%)更符合基准测试结果(CV(RMSE) 1-8%)，特别是对于冷却需求。

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来源期刊

Journal of Building Performance Simulation CONSTRUCTION & BUILDING TECHNOLOGY-

CiteScore

5.50

自引率

12.00%

发文量

审稿时长

12 months

期刊介绍： The Journal of Building Performance Simulation (JBPS) aims to make a substantial and lasting contribution to the international building community by supporting our authors and the high-quality, original research they submit. The journal also offers a forum for original review papers and researched case studies We welcome building performance simulation contributions that explore the following topics related to buildings and communities: -Theoretical aspects related to modelling and simulating the physical processes (thermal, air flow, moisture, lighting, acoustics). -Theoretical aspects related to modelling and simulating conventional and innovative energy conversion, storage, distribution, and control systems. -Theoretical aspects related to occupants, weather data, and other boundary conditions. -Methods and algorithms for optimizing the performance of buildings and communities and the systems which service them, including interaction with the electrical grid. -Uncertainty, sensitivity analysis, and calibration. -Methods and algorithms for validating models and for verifying solution methods and tools. -Development and validation of controls-oriented models that are appropriate for model predictive control and/or automated fault detection and diagnostics. -Techniques for educating and training tool users. -Software development techniques and interoperability issues with direct applicability to building performance simulation. -Case studies involving the application of building performance simulation for any stage of the design, construction, commissioning, operation, or management of buildings and the systems which service them are welcomed if they include validation or aspects that make a novel contribution to the knowledge base.