# Predictive Optimization of the Heat Demand in Buildings at the City Level
## 1 Introduction
Furthermore, [PERSON] and [PERSON] [20] found that decentralized heat storage was most energy efficient when they optimized the configuration of heat storage in the district heating system. Regardless, the utilization of the buildings as the short-term heat storage provides a valuable tool to further improve the flexibility and energy efficiency in heating. It has been estimated that through optimized operation and management 20-30% savings in building energy consumption are possible without any hardware changes in the building [21]. There have been some efforts in utilizing the thermal mass of buildings in peak load cutting at the large scale. [PERSON] et al. [22] performed peak load cutting utilizing the heat storage of buildings without building models. Reduction of 5.8% in peak loads was achieved. However, the changes in the heating loads had to be small to ensure minimal changes in the indoor temperature. Larger changes in heat loads would require the use of building models to maintain the indoor temperature at the desired level as [PERSON] et al. [23] did in their study. However, the calculation of the parameters of the applied building model needed experimental data that would be laborious to collect and limited the applicability of the model. [PERSON] et al. [24] performed peak load cutting in a residential heating system with 50 buildings utilizing DSM algorithm. The algorithm determined the buildings where the heating should completely be cut off and then the decrease in the indoor temperature of the buildings was predicted with [PERSON]'s cooling law considering the thermal time constant of the buildings. However, they only considered cutting the heating completely off. This leads to the suboptimal use of buildings thermal mass, as only buildings with high enough indoor temperatures to withstand the complete cut off of heating will participate in the peak load cutting. Moreover, the estimation of the building time constant is not an easy matter. [PERSON] et al. [25] applied DSM for three different-sized district heating systems considering different building groups. They emphasize the complexity of the matter and that it is affected by many factors such as the building structure and the outdoor and indoor conditions. However, their study did not include models of any kind for the buildings to take into account the dynamics of the indoor environment. 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. In another work, peak load cutting was studied in two different buildings utilizing the indoor temperature model and it was found that buildings can have very different heat storage capabilities and the energy savings or peak load cuts achieved in a single building are not necessarily the indication of the larger scale performance [28]. Consequently, the optimization of individual buildings alone is of no interest when considering the overall benefits to the energy system [29; 30]. Therefore, in this work, the two developed forecast models are applied together in the predictive optimization of the heat demand at the city level. Furthermore, [PERSON] et al. [31] have reported that most of the literature relating to the district level optimization concentrate on the optimal supply of a district assuming the energy demand of the buildings as known, perfectly predicted, and unalterable. In this work, the developed models enable the utilization of the heat storage capacity of the buildings allowing to alter the heat demand profile of the buildings making them active participants in the energy systems. All this is possible as the models are accurate and easily applicable to different buildings offering a valuable tool for the optimization of energy networks to improve energy efficiency. In the next section, the measurement data used in the simulations is described.