The several parameters affect the heat load of a building; geometry, construction, layout, climate and the users. These parameters are complex and interrelated. Comprehensive models are needed to understand relationships among the parameters that can handle non-linearities. The aim of this study is to predict heat load of existing buildings benefiting from width/length ratio, wall overall heat transfer coefficient, area/volume ratio, total external surface area, total window area/total external surface area ratio by using artificial neural networks and compare the results with a building energy simulation tool called KEP-IYTE-ESS developed by Izmir Institute of Technology. A back propagation neural network algorithm has been preferred and both simulation tools were applied to 148 residential buildings selected from 3 municipalities of Izmir-Turkey. Under the given conditions, a good coherence was observed between artificial neural network and building energy simulation tool results with a mean absolute percentage error of 5.06% and successful prediction rate of 0.977. The advantages of ANN model over the energy simulation software are observed as the simplicity, the speed of calculation and learning from the limited data sets.Scientific and Technological Research Council of Turkey (TUBITAK-109M450

Buildings account for 40% of total energy consumption in the European Union, yet at the same time they have considerable energy saving potentials. Historic buildings should be treated different from contemporary ones when it comes to energy improvements. The specifications which underline historical, sociocultural and architectural values require certain care during realization of energy saving implementations to sustain these values. The purpose of this study is to demonstrate how the energy efficient retrofitting in historic buildings should be managed in a transdisciplinary approach with a case study conducted on the historic building in ̄zmir - Turkey. A detailed building energy simulation tool was used to determine the impacts of energy efficient retrofits. The actual energy consumption of case building was based on the utility bills regarding electricity and heating fuel consumption. Building energy simulation tool was calibrated by comparing the measured and simulated indoor air temperatures and total energy consumptions. The inappropriate retrofits, which contradict to the cultural heritage values, were eliminated with a transdisciplinary approach. Later appropriate retrofits were gathered into three packages to evaluate their effects on the energy consumption. The results show that energy saving of more than 34% can be obtained without damaging the heritage values.Swedish Energy Agency within the national R&D program for energy efficiency in historic building

Geothermal district heating system design consists of two parts: heating system and piping network design. District heating system design and a case study for a university campus is given in Yildirim et al. [1] in detail. In this study, piping network design optimisation is evaluated based on heat centre location depending upon the cost and common design parameters of piping networks which are pipe materials, target pressure loss (TPL) per unit length of pipes and installation type. Then a case study for the same campus is presented. © 2010 Elsevier Ltd

Thermal comfort has an important role in human life, considering that people spend most of their lives in indoor environments. However, the necessity of ensuring the thermal comfort of these people presents an important problem, calculating the thermal comfort accurately. The assessment of thermal comfort has always been problematic, from past to present, and the studies conducted in this field have indicated that there is a gap between thermal comfort and thermal sensation. Although recent studies have shown an effort to take human psychology into account more extensively, these studies just focused on the physiological responses of the human body under psychological disturbances. On the other hand, the mood state of people is one of the most significant parameters of human psychology. Thus, this paper investigated the effect of occupants’ mood states on thermal sensation; furthermore, it introduced a novel “Mood State Correction Factor” (MSCF) to the existing thermal comfort model. To this aim, experiments were conducted at a mixed-mode building in a university between 15 August 2021 and 15 August 2022. Actual Mean Vote (AMV) and Profile of Mood States (POMS) were used to examine the effect of mood state on thermal sensation. The outcomes of this study showed that in the mood states of very pessimistic and very optimistic, the occupants felt warmer than the calculated one and the MSCFs are calculated as −0.125 and −0.114 for the very pessimistic and very optimistic mood states, respectively. It is worth our time to note that the experiments in this study were conducted during the COVID-19 Global Pandemic and the results of this study could differ in different cultural backgrounds.