{"title":"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","authors":"Sang-woo Ham, P. Karava, Ilias Bilionis, J. Braun","doi":"10.1080/19401493.2022.2032352","DOIUrl":null,"url":null,"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.","PeriodicalId":49168,"journal":{"name":"Journal of Building Performance Simulation","volume":"142 1","pages":"251 - 267"},"PeriodicalIF":2.2000,"publicationDate":"2022-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A scalable and practical method for disaggregating heating and cooling electrical usage using smart thermostat and smart metre data\",\"authors\":\"Sang-woo Ham, P. Karava, Ilias Bilionis, J. Braun\",\"doi\":\"10.1080/19401493.2022.2032352\",\"DOIUrl\":null,\"url\":null,\"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.\",\"PeriodicalId\":49168,\"journal\":{\"name\":\"Journal of Building Performance Simulation\",\"volume\":\"142 1\",\"pages\":\"251 - 267\"},\"PeriodicalIF\":2.2000,\"publicationDate\":\"2022-02-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Building Performance Simulation\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1080/19401493.2022.2032352\",\"RegionNum\":4,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"CONSTRUCTION & BUILDING TECHNOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Building Performance Simulation","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1080/19401493.2022.2032352","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"CONSTRUCTION & BUILDING TECHNOLOGY","Score":null,"Total":0}
A scalable and practical method for disaggregating heating and cooling electrical usage using smart thermostat and smart metre data
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.
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.
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