A Dynamic Model for Indoor Temperature Prediction in Buildings

Abstract

A novel dynamic model for the temperature inside buildings is presented, aiming to improve energy efficiency by providing predictive information on the heat demand. To analyse the performance and generalizability of the modelling approach, real measurement data was gathered from five different types of buildings. Easily available data from various sources was utilized. The chosen model structure leads to a minimal number of input variables and free parameters. Simulations with real data from five buildings, and applying the identical model structure showed that the average modelling error during the 28-h prediction horizon was constantly below 5%. The results thus demonstrate that the model structure can be standardized and easily applied to predict the indoor temperatures of large buildings. This would finally enable demand side management and the predictive optimization of the heat demand at city level.

Predictive Optimization of the Heat Demand in Buildings at the City Level

“…For indoor temperature forecast, a previously developed dynamic model was applied and is presented here in Equation (1) [26]:…”

“…Outdoor temperature forecast should also be used but in this study the simulations were done using measured outdoor temperatures. In previous studies, the average modelling error for the indoor temperature model was below 5% [26] and for the city level heat demand forecast it was 4% [27]. For more in-depth discussion on the applied models and their validation, readers are referred to [26] and [27].…”

“…In previous studies, the average modelling error for the indoor temperature model was below 5% [26] and for the city level heat demand forecast it was 4% [27]. For more in-depth discussion on the applied models and their validation, readers are referred to [26] and [27]. However, it must be noted that the models were developed for the optimization of the heat demand at the city level.…”

“…The parameters a and b of the heat demand model (Equation (2)) were estimated using one week of measured heat demand and outdoor temperature data as in [27]. On the other hand, as in this work there were hundreds of buildings studied and there were no indoor temperature measurements available from these buildings, the measured indoor temperature could not be used for the estimation of the parameters a, b, x 1 , and x 2 of the indoor temperature model (Equation (1)) as in [26]. Therefore, the indoor temperature of the buildings had to be simulated.…”

“…In previous studies, the authors have developed models for indoor temperature and heat demand forecast for buildings to overcome the current modelling issues hindering the application of the models in a large scale [26,27]. However, the optimization was not considered in these works.…”