Separating the HVAC energy use from the total residential load can be used to improve energy usage monitoring and to enhance the house energy management systems (HEMS) for existing houses that do not have dedicated HVAC circuits. In this paper, a novel method is proposed to separate the HVAC dominant load component from the house load. The proposed method utilizes deep learning techniques and the physical relationship between HVAC energy use and weather. It employs novel long short-term memory (LSTM) encoder-decoder machine learning (ML) models, which are developed based on future weather data input in place of weather forecasts. In addition to being used in the proposed HVAC separation method, the LSTM models are employed also for accurate day-ahead HVAC and solar photovoltaic (PV) energy forecasts. To test and validate the proposed method, the SHINES dataset, a publicly available dataset spanning three years at 15-minute time resolution from a large-scale DOE experimental project, is used. Two computational case studies are constructed with a test HEMS to investigate the power regulating capability of smart home virtual operation as dispatchable loads or generators. Prediction results obtained with the proposed method show hourly and daily CV(RMSE) of 29.4% and 11.1%, respectively. These results are well within the bounds of error established by academia and the ASHRAE building model and calibration standards.

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Published in IEEE Access, v. 9.

This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/.

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This work was supported in part by the National Science Foundation (NSF) under Grant ECCF 1936494, in part by the NSF Graduate Research Fellowship under Grant 1839289, and in part by the Department of Education (DoEd) Graduate Assistance in Areas of National Need (GAANN) Fellowship.