Robustness of reduced-order models for prediction and simulation of the thermal behavior of dwellings

Abstract

The integration of buildings in a Smart Grid environment, enabling demand-side management and thermal storage, requires robust reduced-order building models that (i) allow simulation of the energy demand of buildings at a grid-level and (ii) contribute to the development of demand-side management control strategies. System identification is earned out to identify suitable reduced-order models that are able to predict and simulate the thermal response of a residential building. Both grey-box models, based 011 physical knowledge, and statistical black-box models are considered and identified 011 data obtained from simulations with a detailed physical model, deployed in the Integrated District Energy Assessment Simulation (IDEAS) package in Modelica. The robustness of identified black-box and grey-box models for day-ahead predictions and simulations of the thermal response of a dwelling is analysed. Whereas accurate day-ahead predictions are obtained for both grey-box and black-box models, the simulated indoor temperatures for the grey-box models tend to gradually deviate from the validation data. Thereby the influence of the data period used for the identification process is found to be of significant importance. Copyright © 2011 by IPAC'11/EPS-AG.