Development and validation of a second-order thermal network model for residential buildings. (15th January 2022)

• Record Type: Journal Article
• Authors: Wang, Junke; Jiang, Yilin; Tang, Choon Yik; Song, Li
• Is Part Of: Applied energy. Volume 306:Part B(2022)
• Publication Date: 2022-01-15
• Subjects: Thermal network modeling -- Home thermal model -- Thermal characteristics identification -- Space air temperature prediction -- Weather condition; Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2021.118124
• Languages: English
• ISSNs: 0306-2619

Abstract

Highlights: Develop a second-order thermal network model and a hybrid parameter identification scheme for residential buildings. Characterize dynamics of space air and interior wall surface temperatures separately. Enable automatic, sequential, and optimal estimation of the model parameters. Yield reliable parameter estimates and accurate prediction using only a modest amount of training data. Provide a tool to facilitate grid-interactive Heating, Ventilation, and Air Conditioning (HVAC) operation. Abstract: Heating, Ventilation, and Air Conditioning (HVAC) systems can maintain the space air temperature of residential buildings, either directly by heating/cooling the air, or indirectly via heat transfer to and from the building structure that acts as a thermal mass. Hence, HVAC systems can help achieve load shifting, peak load reduction, and/or energy cost saving, thus enabling grid-interactive HVAC operation. A home thermal model that can accurately reflect the dynamics of the space air and interior wall surface temperatures, is therefore valuable. This paper develops such a model using the standard RC (resistance-capacitance) approach. The model contains a virtual envelope node and an internal space node and is thus second-order. A hybrid parameter identification scheme, made up of the least-squares and optimal search methods, is also developed. The proposed model and scheme were validated using data collected from a test home. It was found that a modest amount of training data was sufficient to yield reliable parameter estimates and accurate prediction. It was also found that when making 24-hour-ahead prediction of the space air temperature, both methods had comparable performances when the training data began in a transition season. However, when they began in an HVAC season, the optimal search method performed better. Therefore, the least-squares method is recommended during a transition season due to its lower computational burden, while the optimal search method is recommended during an HVAC season due to its better estimation performance.