# Predictive Optimization of the Heat Demand in Buildings at the City Level
## 5 Discussion
This is, of course, not the case as the indoor temperature can fluctuate several degrees in real buildings. Therefore, energy savings could be also achieved by more stable indoor temperature control. It is also important to note that the changes in the indoor temperature were small to ensure that no discomfort is inflicted upon the occupants. To further ensure the validity of the results the total heat consumption was not allowed to be lower than the original total heat consumption for the optimization period. This means that the same amount of heat was delivered to all buildings during the optimization period as would have been without the optimization schemes. However, it should be noted that the heat demand was predicted by the model and there is always uncertainty in the predictions. Therefore, as the optimization is based on the heat demand prediction, the accuracy of this prediction is important. In future work, the uncertainty of the heat demand prediction should be included in the optimization scheme. This would include the uncertainty of the heat demand model itself but also the uncertainty of the outdoor temperature forecast. The most significant feature of the proposed concept is the ease of the modelling effort. This is important, as in modern automation, the cost of implementation work plays a key role while the cost of the hardware is decreasing. Table 4 presents the input variables, outputs, and the number of free parameters in the models utilized in the optimization of the heat demand. Only four measured inputs are needed: indoor temperature, heating power, outdoor temperature, and volume of the building. All of these can be considered easily available through the building automation system or from heating company. If no indoor temperature measurements are available, a similar approach to estimate the indoor temperature as presented in this paper is utilized. However, this approach would need more comprehensive study in a greater number of real buildings as presented in Section 4.1. In total, six model parameters estimated using the measured data are needed for each building. The parameters for the power functions to calculate \(C\) and \(U\) values need to be estimated only once but may be updated if new information on actual \(C\) and \(U\) values becomes available. Furthermore, one week of historical measurement data for the heating power and outdoor temperature is enough to estimate the model parameters [26; 27]. Figure 7 presents a general concept for peak load cutting and optimization of the heat demand. As discussed above, the novelty of the concept comes from the applied, easily adaptable modelling approach. The optimization strategies applied in the current work can be considered in this framework. The results showed that the heat demand can be altered at the city level by applying the presented easily applicable modelling methods and by utilizing buildings as short-term heat storage. However, the applied optimization strategies are only some ways to perform the optimization of the heat demand. Another alternative would be the optimization of the heat demand based on the electricity price. Then the heat production in combined heat and power (CHP) plant would be scheduled so that the electricity production is maximized at the time of the high electricity price. The local energy production of buildings could also be taken into consideration in the optimization. The next step is to conduct field tests applying the developed methods. This is crucial for the commercialization of the results as the realization of the developed concept requires proper real-time and two-way information flow between the heat producer and end-users.