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.
Highlights
In developed countries, buildings account for 20–40% of the total energy consumption and half of this energy is used by heating, ventilation and air conditioning (HVAC) systems [1]
Real measurement data from a large district heating system with more than 4000 buildings was utilized in the validation of the modelling approaches
Forecast simulations with the measured data showed that for a 48-h city level heat demand forecast the mean absolute percentage error (MAPE) for both models was 4% indicating good modelling performance
Summary
Buildings account for 20–40% of the total energy consumption and half of this energy is used by heating, ventilation and air conditioning (HVAC) systems [1]. For 15 of the 28 EU countries the annual heat demand in buildings presents the largest energy demand surpassing electricity and cooling demands [2]. For the above mentioned reasons, the implementation of new energy efficiency measures for the heating and building sectors is of utmost importance. In this article two different modelling approaches for forecasting the heat demand from individual buildings up to city level are presented. It is argued that relatively straightforward methods would be an important asset for improving the optimization of heat demand for large buildings and heating systems, such as district heating networks, enabling peak load cutting and demand side management actions leading to increased energy efficiency
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