District heating networks are commonly addressed in the literature as one of the most effective solutions for decreasing the greenhouse gas emissions from the building sector. These systems require high investments which are returned through the heat sales. Due to the changed climate conditions and building renovation policies, heat demand in the future could decrease, prolonging the investment return period. The main scope of this paper is to assess the feasibility of using the heat demand

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... door temperature function for heat demand forecast. The district of Alvalade, located in Lisbon (Portugal), was used as a case study. The district is consisted of 665 buildings that vary in both construction period and typology. Three weather scenarios (low, medium, high) and three district renovation scenarios were developed (shallow, intermediate, deep). To estimate the error, obtained heat demand values were compared with results from a dynamic heat demand model, previously developed and validated by the authors. The results showed that when only weather change is considered, the margin of error could be acceptable for some applications (the error in annual demand was lower than 20% for all weather scenarios considered). However, after introducing renovation scenarios, the error value increased up to 59.5% (depending on the weather and renovation scenarios combination considered). The value of slope coefficient increased on average within the range of 3.8% up to 8% per decade, that corresponds to the decrease in the number of heating hours of 22-139h during the heating season (depending on the combination of weather and renovation scenarios considered). On the other hand, function intercept increased for 7.8-12.7% per decade (depending on the coupled scenarios). The values suggested could be used to modify the function parameters for the scenarios considered, and improve the accuracy of heat demand estimations. Abstract In order to develop an acceptable real-time control approach in terms of accuracy and computation time in industrial and commercial applications, the based Back Propagation Neural Network (BPNN) approach was introduced into the discharge pressure optimization process of the transcritical CO2 heat pump systems. The relevant characteristic variables concerning to the discharge pressure was minimized by the Group Method of Data Handling (GMDH) method, and the relevance of all the variables with the optimal rejection pressure were investigated one by one. Prediction error of different type neural network were compared with each other. Finally, the performance of neural network based transcritical CO2 system was compared with that of conventional empirical correlations-based systems in terms of the optimal discharge pressure, which showed that the novel PSO-BP prediction model provides an innovative and appropriate idea for developers and manufacturers. Abstract In order to develop an acceptable real-time control approach in terms of accuracy and computation time in industrial and commercial applications, the based Back Propagation Neural Network (BPNN) approach was introduced into the discharge pressure optimization process of the transcritical CO2 heat pump systems. The relevant characteristic variables concerning to the discharge pressure was minimized by the Group Method of Data Handling (GMDH) method, and the relevance of all the variables with the optimal rejection pressure were investigated one by one. Prediction error of different type neural network were compared with each other. Finally, the performance of neural network based transcritical CO2 system was compared with that of conventional empirical correlations-based systems in terms of the optimal discharge pressure, which showed that the novel PSO-BP prediction model provides an innovative and appropriate idea for developers and manufacturers.