A scalable and practical method for disaggregating heating and cooling electrical usage using smart thermostat and smart metre data

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

Journal of Building Performance Simulation Pub Date : 2022-02-07 DOI:10.1080/19401493.2022.2032352

Sang-woo Ham, P. Karava, Ilias Bilionis, J. Braun

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引用次数: 0

Abstract

We present a scalable and practical method for disaggregating electrical usage for heat pump heating and cooling (HC) that uses low-resolution data from existing smart energy metres and smart thermostats. The disaggregation model is based on a Bayesian approach to account for the skewed characteristics of HC and non-HC energy consumption and adopts sequential Bayesian update to enable reliable predictions without long-term data. The modelling approach is demonstrated using disaggregated electricity consumption and thermostat operation signal data in two multi-family residential communities located in two different cities in Indiana, U.S. The results show that the model successfully disaggregated HC electricity consumption for various housing units by using 15-minute interval data with less than 12% error for a weekly time interval. Finally, seasonal parameters of the model were updated when a new HC operation signal was observed resulting in good predictions for different seasons.

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使用智能恒温器和智能电表数据分解加热和冷却电气使用的可扩展和实用方法

我们提出了一种可扩展和实用的方法来分解热泵供暖和制冷(HC)的电力使用，该方法使用来自现有智能电表和智能恒温器的低分辨率数据。分解模型基于贝叶斯方法来解释HC和非HC能源消耗的倾斜特征，并采用顺序贝叶斯更新来实现不需要长期数据的可靠预测。利用美国印第安纳州两个不同城市的两个多户住宅社区的电力消耗和恒温器运行信号数据对建模方法进行了验证。结果表明，该模型使用15分钟间隔数据成功地对不同住宅单元的HC电力消耗进行了分解，并且在一周时间间隔内误差小于12%。最后，当观测到新的HC运行信号时，对模型的季节参数进行更新，得到了对不同季节的较好预测。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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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.