Conference Year

July 2018


building thermal model, virtual energy storage, detached two-floor house, online adaptive correction


The residential building sector accounts for approximately 37% of total U.S. electricity consumption. Within the residential building sector, heating and cooling is the main target for peak load shifting/reduction since it is the largest contributor to peak demand. In fact, the flexibility of residential HVAC loads can provide continuous variation of demand to provide grid services. Such load can be taken as virtual energy storage (VES) resources by varying their demand over a baseline so that they appear to be providing the same service as electrical battery energy storage to the grid. The performance of HVAC load control to provide VES relies heavily on the accuracy of indoor temperature or cooling/heating demand predictions and therefore the quality of building model. Most building models can be broken down into two categories: gray-box models, i.e. Resistance-Capacitance (RC) model and data-mining based models. The RC model, built based on a combination of physical principles and measured data, is constituted with electrical analogue pattern with resistance (R) and capacitance (C). Different RC models with different structures and orders have been widely applied in prediction of building indoor temperature or cooling/heating demand. But, in general, the RC models require long periods of data to train model coefficients as well as considerable training time and computation burden. Recently, data-mining based models have gained increasing interest due to their capability in analyzing large-scale data and flexibility in practical applications. But, the data-mining based models tend to have invisible model structures which poses a problem when trying to use the model for optimal control or model predictive control of the HVAC system. Hence, for this control-oriented modeling scope, there is a continuing need for efficient online system identification techniques, which can provide explicit parameters for the model. Traditional regression models fit well for this specific purpose. This paper presents a rolling horizon building thermal model for identifying indoor temperatures in separate floors of a typical detached residential house. The developed model includes online adaptive correction component. Since this adaptive algorithm needs to be implemented online, a less computation-demanding polynomial fitting algorithm is adopted. This kind of fitting problem can be cast as linear regression problem with multiple variables, parameters of which can be efficiently obtained by well-known gradient descent method. The validation is realized by comparing the predicted results with the measured data from a real typical detached two-floor house. The results show that the developed model has satisfactory performance in predicting the building indoor temperature in 1st and 2nd floors.