# Predictive Optimization of the Heat Demand in Buildings at the City Level
## 1 Introduction  
According to a recent study by Connolly [1], heating in buildings presents the largest energy demand in 15 EU countries, and it is also higher than cooling demand in all 28 EU countries. At the same time, the peak demand for heating is considerable. In heat production, peak loads occur when the heat demand exceeds the capacity of the power plants. During the peak loads, oil powered reserve capacity is typically needed. This raises production costs and environmental impact for the energy company and creates a strong economic and environmental incentive to find ways to cut peak loads and reduce the use of oil. Reducing peak loads would also enable more buildings to be connected to the heating network. This article presents simulation studies employing previously developed, easily adaptable indoor temperature and heat demand models to demonstrate their applicability to predictively optimize the heat demand and to cut the peak loads by utilizing buildings as short-term heat storage. The applied modelling approach is argued to provide a cost-effective way to implement predictive optimization and to improve energy efficiency in heating. One way to cut peak loads is by means of demand side management (DSM). The term was introduced by [PERSON] [2] in the 1980s. He defined DSM as activities aimed to influence the electricity use of customers in ways that will produce desired changes in the load shape. Consequently, the majority of the published literature discusses DSM in the context of electricity [3; 4; 5]. However, DSM is equally applicable to district heating. In [4], three possible ways to achieve the change in the end-user energy consumption with DSM are described. The first way is to reduce the energy consumption during the peak loads. In case of heating, this could mean a temporary loss of comfort. Secondly, the energy consumption could be shifted to a different period of time. This would mean that the buildings are pre-heated before the peak loads. The third way is to use on-site generated energy for exampleheat pumps, solar collectors, or even fireplaces. All these ways can be efficiently utilized if the heat consumption can be predicted. The authors have studied a concept for cutting the peak loads and optimizing the heat demand and the main idea is to utilize buildings as short-term heat storages [6]. It is well known that in this way the peak loads can be cut, the indoor temperature swings can be reduced and the time of heat demand can be shifted [7; 8; 9; 10; 11]. However, as the predictive control for buildings has gained popularity, the time used for modelling has been identified as one of the most important factors when considering the total implementation work [12; 13]. Model Predictive Control (MPC) is currently one of the most studied control strategies for buildings and it has been reported that the modelling work can be as high as 60% of the total MPC implementation work [12; 14]. Considering the above, easily applicable models would make the implementation of advanced model-based control and optimization methods significantly more cost-effective. [PERSON] and [PERSON] [15], [PERSON] et al. [16], and [PERSON] et al. [17] all optimized heat production in the district heating system assuming a central heat storage unit. In this work, the aim was to study how the heat demand can be optimized and how much peak loads can be cut at the city level by modelling each individual building to enable the utilization of their thermal storage capacity. Motivation for this comes from the fact that the heat storage capacity of buildings offers effective and efficient way to store heat [18; 19]. Moreover, the capacity already exists, and only proper ways to utilize that are needed. It is also argued that the solution is more cost-effective as there are no large investment costs involved like in the case of large central heat storage solutions.