Search

During the design and planning stage of a district energy system, the prediction of the cooling and heating loads is an important step. The accurate estimate of the load pattern can provide a basis for the configuration and optimization of the system. To meet the demand in practical application, this paper proposes a fast load prediction method for district energy systems based on a presimulated forward modelling database and KNN (K-nearest neighbor) algorithm and develops it into a practical tool. Owing to the absence of some design parameters at the planning stage, scenario analysis is also used to determine some input conditions for load prediction. In this paper, the scenarios cover three types of building: office, shopping mall and hotel. To test the performance of this new method, we randomly selected 15 virtual buildings (5 buildings for each type) with different design parameters and took their detailed BPS (building performance simulation) model as a benchmark to assess the prediction results of the new method. The index "ratio of the hours with effective prediction" is defined as the ratio of the hours whose relative error of hourly load prediction is less than 15% to the hours whose load is not 0 in the whole year, and the test result shows that this index is not less than 0.9 (90%) for the predicted cooling load of all 45 test cases and the predicted heating load of 25 of the 45 cases. As a research achievement with practical value, this paper accomplishes the programming work of the tool and makes it into a software. The application of this software in the actual project of district energy system is also presented. The performance of the new load prediction tool was compared with the traditional approach commonly used in engineering—the load estimation based on reference building models—and the result shows that the fast load estimate tool can provide the same level of prediction accuracy as traditional simulation methods. [ABSTRACT FROM AUTHOR]

Commercial buildings in hot and humid tropical climates rely significantly on cooling systems to maintain optimal occupant comfort. A well-accurate day-ahead load profile prediction plays a pivotal role in planning the energy requirements of cooling systems. Despite the pressing need for effective day-ahead cooling load predictions, current methodologies have not fully harnessed the potential of advanced deep-learning techniques. This paper aims to address this gap by investigating the application of innovative deep-learning models in day-ahead hourly cooling load prediction for commercial buildings in tropical climates. A range of multi-output deep learning techniques, including Deep Neural Networks (DNNs), Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Long Short-Term Memory networks (LSTMs), are employed to enhance prediction accuracy. Furthermore, these individual deep learning techniques are synergistically integrated to create hybrid models, such as CNN-LSTM and Sequence-to-Sequence models. Experiments are conducted to choose the time horizons from the past that can serve as input to the models. In addition, the influence of various categories of input parameters on prediction performance has been assessed. Historical cooling load, calendar features, and outdoor weather parameters are found in decreasing order of influence on prediction accuracy. This research focuses on buildings located in Singapore and presents a comprehensive case study to validate the proposed models and methodologies. The sequence-to-sequence model provided better performance than all the other models. It offered a CV-RMSE of 7.4%, 10%, and 6% for SIT@Dover, SIT@NYP, and the simulated datasets, which were 2.3%, 3%, and 1% less, respectively, than the base Deep Neural Network model. [ABSTRACT FROM AUTHOR]

A ceiling cooling system integrated with a mechanical ventilation system has been widely used in modern buildings with large sensible cooling loads due to the high thermal comfort level and large energy efficiency. However, there is a lack of systematic research on the influence factors such as ceiling surface temperature and cooling load on the indoor air distribution and thermal environment, and the impact of ventilation system type in the ceiling cooling room is still unclear. Therefore, this paper presented an experimental study of indoor air distribution and thermal environment in a ceiling cooling (CC) room with mixing ventilation (MV), underfloor air distribution (UFAD) and stratum ventilation (SV); the ceiling surface temperature was 17 °C–26 °C and the internal or external cooling load was 41.5 W/m2–69.5 W/m2. The results showed that the vertical air temperature difference and contaminant removal effectiveness were 0.2 °C–0.4 °C and 0.53–0.85 with CC + MV, 0 °C–1.2 °C and 0.68–1.25 with CC + UFAD and 0.3 °C–0.9 °C and 0.50–0.83 with CC + SV, and the corresponding heat removal effectiveness and air diffusion performance index were 0.96–1.11 and 96–100%, 0.9–1.5 and 57–100% and 1.11–1.34 and 71–100%, respectively. Moreover, the difference between mean radiant temperature and air temperature and the predicted mean vote of thermal sensation were from 0 °C to 0.9 °C and between 0 and 0.26 with CC + MV, from −0.1 °C to 2.2 °C and between −0.1 and 0.42 with CC + UFAD and from −0.1 °C to 0.9 °C and between −0.2 and 0.13 with CC + SV. Hence, the ventilation system type clearly affected the indoor air distribution and thermal environment in the ceiling cooling room, and the experimental results would be beneficial for the design and control of a ceiling cooling system combined with a mechanical ventilation system in practice. [ABSTRACT FROM AUTHOR]

This paper proposes the optimal algorithm for controlling the HVAC system in the target building. Previous studies have analyzed pre-selected algorithms without considering the unique data characteristics of the target building, such as location, climate conditions, and HVAC system type. To address this, we compare the accuracy of cooling load prediction using ANN and LSTM algorithms, widely used in building energy research, to determine the optimal algorithm for HVAC control in the target building. We develop a simulation model calibrated with actual data to ensure data reliability and compare the energy consumption of the existing HVAC control method and the two algorithms-based methods. Results show that the ANN algorithm, with a CV(RMSE) of 12.7%, has a higher prediction accuracy than the LSTM algorithm, CV(RMSE) of 17.3%, making it a more suitable algorithm for HVAC control. Furthermore, implementing the ANN-based approach results in a 3.2% cooling energy reduction from the optimal control of Air Handling Unit (AHU) Discharge Air Temperature (DAT) compared to the fixed DAT at 12.8 °C in a representative day. This study demonstrates that ML-based HVAC system control can effectively reduce cooling energy consumption in HVAC systems, providing an effective strategy for energy conservation and improved HVAC system efficiency. [ABSTRACT FROM AUTHOR]

An important aspect in improving the energy efficiency of buildings is the effective use of building heating and cooling load prediction models. A lot of studies have been undertaken in recent years to anticipate cooling and heating loads. Choosing the most effective input parameters as well as developing a high-accuracy forecasting model are the most difficult and important aspects of prediction. The goal of this research is to create an intelligent data-driven load forecast model for residential construction heating and cooling load intensities. In this paper, the shuffled shepherd red deer optimization linked self-systematized intelligent fuzzy reasoning-based neural network (SSRD-SsIF-NN) is introduced as a novel intelligent data-driven load prediction method. To test the suggested approaches, a simulated dataset based on the climate of Dhahran, Saudi Arabia will be employed, with building system parameters as input factors and heating and cooling loads as output results for each system. The simulation of this research is executed using MATLAB software. Finally, the theoretical and experimental results demonstrate the efficacy of the presented techniques. In terms of Mean Square Error (MSE), Root Mean Square Error (RMSE), Regression (R) values, Mean Absolute Error (MAE), coefficient of determination (R2), and other metrics, their prediction performance is compared to that of other conventional methods. It shows that the proposed method has achieved the finest performance of load prediction compared with the conventional methods. [ABSTRACT FROM AUTHOR]

This paper proposes a meta-modeling workflow to forecast the cooling and heating loads of buildings at individual and district levels in the early design stage. Seven input variables, with large impacts on building loads, are selected for designing meta-models to establish the MySQL database. The load profiles of office, commercial, and hotel models are simulated with EnergyPlus in batches. A sequence-to-sequence (Seq2Seq) model based on the deep-learning method of a one-dimensional convolutional neural network (1D-CNN) is introduced to achieve rapid forecasting of all-year hourly building loads. The method performs well with the load effective hour rate (LEHR) of around 90% and MAPE less than 10%. Finally, this meta-modeling workflow is applied to a district as a case study in Shanghai, China. The forecasting results well match the actual loads with R2 of 0.9978 and 0.9975, respectively, for the heating and cooling load. The LEHR value of all-year hourly forecasting loads is 98.4%, as well as an MAPE of 4.4%. This meta-modeling workflow expands the applicability of building-physics-based methods and improves the time resolution of conventional data-driven methods. It shows small forecasting errors and fast computing speed while meeting the required precision and convenience of engineering in the building early design stage. [ABSTRACT FROM AUTHOR]

Urban overheating, intensified by climate change, poses a critical challenge in Central European cities, witnessing a rise in tropical days. Conventional mechanical cooling systems in buildings significantly contribute to carbon dioxide emissions, exacerbating global warming. In response, windcatchers—traditional Iranian natural cooling systems—emerge as a promising sustainable solution for contemporary architecture, even in non-arid climates. This research aims to evaluate windcatchers' efficacy in improving building thermal comfort in Central European climates, focusing on Vienna's urban environment. This study identifies optimal windcatcher designs by analyzing key variables: height variation, inlet dimensions, urban exposure, Building Management System (BMS) temperature thresholds, and integration with an earth tube system using Design Builder simulation software version 6. The findings reveal that a windcatcher standing at 2.5 m tall, with inlet dimensions of 0.9 m by 1.4 m, in an open, less densely populated urban setting, and with open valves when indoor temperatures surpass 22 °C, demonstrates the most effective reduction in cooling load. Moreover, both one-sided and two-sided windcatchers outperform conventional ventilation through openings. Additionally, combining a one-sided windcatcher with an earth tube system ensures efficient cooling even when exterior temperatures exceed 25 degrees Celsius. When augmented by a heat pump, this integrated system can provide heated ventilation. [ABSTRACT FROM AUTHOR]

Energy efficiency is currently a hot topic in engineering due to the monetary and environmental benefits it brings. One aspect of energy efficiency in particular, the prediction of thermal loads (specifically heating and cooling), plays a significant role in reducing the costs associated with energy use and in minimising the risks associated with climate change. Recently, data-driven approaches, such as artificial intelligence (AI) and machine learning (ML) techniques, have provided cost-effective and high-quality solutions for solving energy efficiency problems. This research investigates various ML methods for predicting energy efficiency in buildings, with a particular emphasis on heating and cooling loads. The review includes many ML techniques, including ensemble learning, support vector machines (SVM), artificial neural networks (ANN), statistical models, and probabilistic models. Existing studies are analysed and compared in terms of new criteria, including the datasets used, the associated platforms, and, more importantly, the interpretability of the models generated. The results show that, despite the problem under investigation being studied using a range of ML techniques, few have focused on developing interpretable classifiers that can be exploited by stakeholders to support the design of energy-efficient residential buildings for climate impact minimisation. Further research in this area is required. [ABSTRACT FROM AUTHOR]

The heat rejected by outdoor units of split A/C conditioners can impact the ambient outdoor environment of mixed-mode buildings. Nevertheless, how this environmental impact may affect the space-conditioning energy use and indoor air pollution is poorly understood. By coupling EnergyPlus and Fluent, this study examines the effects of outdoor units' heat rejection on the building surroundings, building cooling load, and indoor PM2.5 exposure of a six-storey mixed-mode building. The building had an open-plan room on each floor, with the outdoor unit positioned below the window. The coupled model was run for a selected day when the building was cooled by air conditioning and natural ventilation. Five mixed-mode cooling strategies were simulated, reflecting different window-opening schedules, airflow rates of outdoor units, and cooling set-points. The results indicate that compared with the always-air-conditioned mode, the mixed-mode operation could significantly mitigate the negative impact of heat rejection on space-cooling energy consumption. Increasing the airflow rate of outdoor units led to a lower increase in demand for space cooling and lower indoor PM2.5 exposure. If one of the six rooms needs to be cooled to a lower temperature than the others; choosing the bottom-floor room helped achieve more energy savings and better indoor air quality. [ABSTRACT FROM AUTHOR]

Heating, ventilation and air conditioning (HVAC) systems account for approximately 50% of the total energy consumption in buildings. Advanced control and optimal operation, seen as key technologies in reducing the energy consumption of HVAC systems, indispensably rely on an accurate prediction of the building's heating/cooling load. Therefore, the goal of this research is to develop a model capable of making such accurate predictions. To streamline the process, this study employs sensitivity and correlation analysis for feature selection, thereby eliminating redundant parameters, and addressing distortion problems caused by multicollinearity among input parameters. Four model identification methods including multivariate polynomial regression (MPR), support vector regression (SVR), multilayer perceptron (MLP), and extreme gradient boosting (XGBoost) are implemented in parallel to extract value from diverse building datasets. These models are trained and selected autonomously based on statistical performance criteria. The prediction models were deployed in a nearly zero-energy office building, and the impacts of feature selection, training set size, and real-world uncertainty factors were analyzed and compared. The results showed that feature selection considerably improved prediction accuracy while reducing model dimensionality. The research also recognized that prediction accuracy during model deployment can be influenced significantly by factors like personnel mobility during holidays and weather forecast uncertainties. Additionally, for nearly zero-energy buildings, the thermal inertia of the building itself can considerably impact prediction accuracy in certain scenarios. [ABSTRACT FROM AUTHOR]

This study aimed to estimate the heating load (HL) and the cooling load (CL) of a residential building using neural networks and to simulate the thermal behavior of a four-layered wall with different orientations. The neural network models were developed and tested using Multi-Layer Perceptron (MLP) and Radial Basis (RB) networks with three algorithms, namely the Levenberg-Marquardt (LM), the Scaled Conjugate Gradient (SCG), and the Radial Basis Function (RB). To generate the data, 624 models were used, including six building shapes, four orientations, five glazing areas, and five ways of distributing glazing. The LM model showed the best accuracy compared to the experimental data. The L-shape facing south with windows on the east and south sides and a 20% window area was found to be the best shape for balancing the lighting and ventilation requirements with the heating and cooling loads near the mean value. The heating and cooling loads for this shape were 22.5 kWh and 24.5 kWh, respectively. The simulation part used the LH algorithm coded in MATLAB to analyze the temperature and heat transfer across the wall layers and the effect of solar radiation. The maximum and minimum percentage differences obtained by HAP are 10.7% and 2.7%, respectively. The results showed that the insulation layer and the wall orientation were important factors for optimizing the thermal comfort of a building. This study demonstrated the effectiveness of neural networks and simulation methods for building energy analysis. [ABSTRACT FROM AUTHOR]

Energy consumption in residential buildings accounts for a large portion of global energy use. Understanding residential building load characteristics is important in both the design and technical suitability analysis of residential air conditioning systems in terms of energy efficiency and carbon reduction. However, most current research mainly focuses on the load characteristics of individual buildings and not on the variation in load characteristics of building aggregation. In addition, the load characteristics of building aggregations vary with the building scale; however, most studies have compared those of buildings under a certain scale, and the change with the increase in building scale is still unclear. The main purpose of this study is to explore load characteristic differences among residential buildings of different scales and the impacts of those differences on HVAC system design. Based on the monitoring data collected in a residential district in Zhengzhou, China, we analyzed the load characteristics among different households and combinations of different numbers of households from the variation in peak load, total consumption and load distribution, as well as the daily load volatility. We indicate that the load characteristics of heating, ventilation and air conditioning systems of different scales should be considered in the design and operation stage. [ABSTRACT FROM AUTHOR]

Exterior shading devices and outdoor units can be closely coupled since these two building components are commonly installed next to each other. This study uses a coupled EnergyPlus-Fluent modeling approach to examine how a combination of exterior shading and heat rejection from outdoor units can affect the ambient outdoor environment of a building, and how changes in the ambient outdoor environment can influence cooling loads and indoor PM2.5 exposure. Three exterior shading devices were simulated, including horizontal overhangs, vertical overhangs, and vertical fins. Data from wind-tunnel experiments and field measurements were used to ensure the accuracy of the airflow model, energy model, and pollution model developed in this study. Results indicate that horizontal overhangs could almost offset the increase in cooling loads due to increased ambient outdoor temperatures caused by heat rejection. The use of vertical overhangs did not always mean lower demand for space cooling when heat rejection was considered. Heat rejection, horizontal overhangs, and vertical overhangs could help reduce indoor PM2.5 exposure, while indoor air pollution was worse after the implementation of vertical fins. This study shows how exterior shading devices and outdoor units can be coupled to achieve better building energy efficiency and improved occupant health. [ABSTRACT FROM AUTHOR]

Due to urban warming, the energy demand for cooling buildings is rising. The current study used CitySim (building energy model) to estimate the cooling energy requirements for 40 buildings in downtown Dubai using high-resolution climate data from weather research and forecasting (WRF) coupled with the single layer urban canopy model (SLUCM). Simulating the four mitigation scenarios allowed for the examination of the reduction in cooling load caused by the addition of greenery at a rate ranging from 25% to 100%. The insulated building's cooling demand reduced by a maximum of 13.89% under 100% GI (M4). Scenario M4 resulted in a reduction of 4.6 kWh/m2 and 3.1 kWh/m2 for the non-insulated and insulated low-rise residential buildings, respectively, while the high-rise buildings saw a reduction of 3.09–4.91 kWh/m2 for the non-insulated and 2.07–3.09 kWh/m2 for the insulated buildings. This study offers a potential remedy to deal with the problem of urban heating in subtropical environments. [ABSTRACT FROM AUTHOR]

As building systems account for almost half of the total energy consumed by the building sector to provide space heating, cooling, and ventilation, efficiently designing these systems can be the key to energy conservation in buildings. Dual VAV systems with an effective control strategy can substantially reduce the energy consumption in buildings, providing a significant scope of further research on this system configuration. This paper proposes to utilize the warm air duct of the dual VAV system as a dedicated outdoor air (DOA) unit when no heating is required, which allows the cooling load to be effectively distributed between two ducts. A specific control sequence is proposed with different supply air temperature reset strategies to estimate the heating, cooling loads, and fan power energy consumption of the proposed system. A simple two-zone office building is taken as a preliminary case study to simulate the airflow rates and fan power of a single duct VAV and proposed dual VAV systems to illustrate the concept. Finally, a larger multi-zone office building is simulated to measure the annual heating, cooling loads, and fan power energy and compare the energy savings among the systems. The results show significant fan power reduction ranging from 1.7 to 9% and notable heating energy reduction up to 76.5% with a small amount of cooling load reduction varying from 0.76 to 2.56% depending on the different locations for the proposed dual VAV systems. Further energy savings from different supply air temperature reset strategies demonstrate the opportunity of employing them according to climates and case studies. The proposed dual VAV system proves to have the potential to be adapted in buildings for the purpose of sustainability and energy savings. [ABSTRACT FROM AUTHOR]

Dense cities usually experience the urban heat island (UHI) effect, resulting in higher ambient temperatures and increased cooling loads. However, the typical lack of combining climatic variables with building passive design parameters in significant evaluations hinders the consideration of the UHI effect during the building design stage. In that regard, a global sensitivity analysis was conducted to assess the significance of climatic variables and building design features in building energy simulations for an office building. Additionally, this study examines the UHI effect on building energy performance in Qatar, a hot-arid climate, using both measurement data and computational modeling. This study collects measurement data across Qatar and conducts computational fluid dynamics (CFD) simulations; the results from both methods serve as inputs in building energy simulation (BES). The results demonstrate that space cooling demand is more sensitive to ambient temperature than other climatic parameters, building thermal properties, etc. The UHI intensity is high during hot and transition seasons and reaches a maximum of 13 °C. BES results show a 10% increase in cooling energy demand for an office building due to the UHI effect on a hot day. The results of this study enable more informed decision-making during the building design process. [ABSTRACT FROM AUTHOR]

Space heating and cooling of buildings is a major contributor to the ascending trend of global energy consumption and greenhouse gas (GHG) emissions. A potential solution to reduce the space heating and cooling is to use buildings' mass for active thermal energy storage (TES). Having air circulation between an active TES and its associated zones can significantly enhance their thermal coupling; however, reported research studies have not focused on this kind of active TES. To that end, this study aimed to evaluate the thermal performance of a ventilated block wall (VBW) in reducing space heating and cooling loads in cold-climate buildings. In this system, air is circulated between a zone and the voided cores of a VBW, where the air exchanges heat with the wall before returning to the zone. To have a generalizable assessment of the system's performance, typical-day and annual energy analyses were conducted under various boundary conditions and air circulation speeds. The study found that for a typical day with significant temperature fluctuation, a VBW with a 2 m/s air circulation speed throughout the day can lead to 67% more net energy exchange (the sum of thermal energy storage and release) when compared to having no air circulation. The annual analysis compared the energy performance between a VBW and a traditional wood-frame wall in three different cold climates. The results showed that substituting a wood-frame wall with a VBW can reduce space heating and cooling loads by 35.1 kWh/m2 (wall surface area) for a mixed dry–cold climate throughout the year. Having cement plaster as interior finishing can lead to 9% more net energy exchange than having drywall, on average, for all zone air temperature profiles. [ABSTRACT FROM AUTHOR]

In cold regions, due o the impact of climatic conditions, the heat load in winter and the cooling load in summer are unbalanced. In the long-term operation of the ground-source heat pump (GSHP), the soil heat imbalance phenomenon has still not been successfully solved. Therefore, this study took the GSHP of a public building in the cold area of northern China as the research object. Based on the unit performance data of the system over 8 years and the measured data of the soil temperature field, the long-term operation efficiency of the GSHP in the cold region and the variation law of the soil temperature field were explored. In order to further study the problem of soil heat imbalance, the effect of heat exchange hole groups at different intervals on the underground soil thermal environment after 30 years of operation in the system was simulated, and the optimization scheme of heat exchange hole spacing was proposed. The research results support the improvement and optimization of GSHP design and construction, and have important practical significance for the popularization of GSHPs in cold regions. [ABSTRACT FROM AUTHOR]

Arid and hot regions, like Saudi Arabia, utilize up to 60% of the country's energy to regulate buildings' indoor comfort. Energy efficiency is a long-term sustainability measure that is part of the government's Vision 2030 strategy. A standard method of improving the thermal performance of buildings is through the use of insulation materials. Considering the cooling loads' requirement and the Global Warming Potential (GWP), the present research evaluated the effectiveness of insulation materials, including extruded polystyrene, expanded polystyrene, rock wool, and glass wool in the hot, arid climate. For this case study, four similar villas facing the cardinal directions were selected from the residential project at Qassim University. HOBO data loggers were used to collect indoor temperature data. Thermal performance and Life Cycle Assessment (LCA) were conducted in accordance with Saudi Building Code-602 (SBC-602). Simulation outputs based on the four cardinal directions were used for assessing the thermal performance and LCA of the different thicknesses and densities of insulation materials. This was done using IESVE and SimaPro, IMPACR2002+, to assess their cooling load and GWP, respectively. The results suggest the potential for using lower insulation thickness for the northern and western façades without violating the SBC. The results obtained the actual thicknesses of the three insulation materials for achieving indoor temperatures in the four cardinal directions and the selection of materials and their densities along with associated GWP. The outputs of the study have been generalized in the form of a performance-based flowchart as a tool for selecting the type and thickness of thermal and environmental insulation in residential buildings in the Qassim region of Saudi Arabia. [ABSTRACT FROM AUTHOR]

Many models have been developed to predict the energy consumption of various building types, including residential, office, institutional, educational, and commercial buildings. However, to date, no models have been designed specifically to predict poultry buildings' energy consumption. To address this information gap, this study integrated data-driven techniques, including artificial neural networks (ANN), support vector regressions (SVR), and random forest (RF), into a physical model to predict the energy consumption of poultry buildings in different climatic zones in Turkey. The following statistical indices were employed to evaluate the model's effectiveness: Root mean square error (RMSE), mean absolute percentage error (MAPE), and coefficient of determination (R2). The calculated and predicted values of the heating and cooling loads were also compared using visualization techniques. The results indicated that the RF model was the most accurate during the testing period according to the RMSE (0.695 and 6.514 kWh), MAPE (3.328 and 2.624%), and R2 (0.990 and 0.996) indices for heating and cooling loads, respectively. Overall, this model offers a simple decision-support tool to estimate the energy requirements of different buildings and weather conditions. [ABSTRACT FROM AUTHOR]

In hot dry regions, the building envelope receives abundant solar radiation, which contributes to heat stress and indoor thermal discomfort. To mitigate overheating inside spaces, cooling is the main basic requirement during most of the year. However, due to the harsh climatic conditions, buildings fail to provide passively the required comfort conditions. Consequently, they are fully dependent on-air conditioning systems, which are huge energy consumers. As roofs are exposed to the sun throughout the daytime, they are estimated to be the main source of heat stress. In return, they can contribute significantly to achieve optimum comfort and energy savings when efficient design strategies are used in an early design stage. To examine the potential for cooling load reduction and thermal comfort enhancement by using cooling roof techniques in residential buildings, a study was performed in the city of Biskra (southern Algeria). Accordingly, an in-field measurement campaign was carried out on test-cells during five days in summer. Three different cooling roof techniques were addressed: (a) cool reflective white paint (CR), (b) white ceramic tiles (CT) and (c) a cool-ventilated roof (C-VR). These roofing alternatives were investigated by monitoring both roof surface temperatures and indoor temperatures. Comparative analysis showed that a cool-ventilated roof is the most efficient solution, reducing the average indoor temperature by 4.95 °C. A dynamic simulation study was also performed based on TRNSYS software to determine the best roofing system alternatives in terms of thermal comfort and energy consumption, considering the hottest month of the year. Simulation tests were run on a base-case model representing the common individual residential buildings in Biskra. Results showed that a double-skin roof combined with cool-reflective paint is the most efficient roofing solution. By comparison to a conventional flat roof, meaningful improvements have been achieved, including reducing thermal discomfort hours by 45.29% and lowering cooling loads from 1121.91 kWh to 741.09 kWh. [ABSTRACT FROM AUTHOR]

The envelope is one of the most important driving factors in the energy efficiency of buildings. Typical for office and commercial buildings, curtain wall facades allow solar heat gains to be used during the winter but can lead to difficulties in reducing the cooling load during summer. The cooling load is dominant in most building types in the temperate maritime climate, while in the temperate continental climate, it is dominant mainly in office and commercial buildings. The goal of this research was to determine the potential of night passive cooling in an office building model in the most populated urban areas in Croatia-Zagreb and Split, which are located in two different climate zones. Suitable to the climate on-site, an appropriate building envelope and various types of passive and mechanical ventilation systems were selected for each location and case. Additional factors included and analysed were climate conditions, heat gains, the heat accumulation of the building, night ventilation through openings, unwanted air infiltration, and cooling loads. Through a detailed description of the model, passive cooling potential calculations, and Computer Fluid Dynamics (CFD) simulations, the results showed a potential of up to 43.5% savings in the cooling energy need for the temperate continental climate and 32.2% in the temperate marine climate. It was found from the analysis that night ventilation is expected to cool down the building enough to delay a need for cooling by several hours and improve fresh air requirements, thus saving power for cooling, and effectively reducing the need for air conditioning. [ABSTRACT FROM AUTHOR]