Enabling Demand Side Management: Heat Demand Forecasting at City Level

Abstract

Implementation of new energy efficiency measures for the heating and building sectors is of utmost importance. Demand side management offers means to involve individual buildings in the optimization of the heat demand at city level to improve energy efficiency. In this work, two models were applied to forecast the heat demand from individual buildings up to a city-wide area. District heating data at the city level from more than 4000 different buildings was utilized in the validation of the forecast models. Forecast simulations with the applied models and measured data showed that, during the heating season, the relative error of the city level heat demand forecast for 48 h was 4% on average. In individual buildings, the accuracy of the models varied based on the building type and heat demand pattern. The forecasting accuracy, the limited amount of measurement information and the short time required for model calibration enable the models to be applied to the whole building stock. This should enable demand side management and lead to the predictive optimization of heat demand at city level, leading to increased energy efficiency.

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

“…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].…”

“…In the previous work [27], the physical model parameter U was estimated using the volume of the building by fitting a power function of form f (x) = d × x p to the data of the known volumes and values of parameter U. This way the value of the parameter could be estimated easily for thousands of buildings.…”

“…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.…”