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Insufficient fresh air supply due to the increased air tightness of building envelopes after building renovations and window upgrades is a major concern of HVAC engineering today. The paper demonstrates the application of CFD simulations in the development of a compact decentralised ventilation unit with integrated heat recovery system for local ventilation of rooms, targeting this common issue. The device houses an innovative cyclically rotating recuperative heat exchanger, allowing effective condensate removal and de-icing in winter for its independent operation throughout the year. The paper introduces the ventilation unit, describes preparation of its numerical models, and conducts CFD simulation using Ansys Fluent software. The initial design of the device was improved following the findings of the numerical analysis, and the proposed adjustments were tested through follow-up CFD simulations, confirming that the desired outcomes were achieved. A separate CFD analysis was performed to assess the use of different air supply elements at the air outlet to the room, recommending the use of adjustable nozzles. A prototype ventilation unit was manufactured and the volume flow rate under different operating conditions was measured to be compared with the simulation results. The outcome of the research is a new type of compact local ventilation unit. An increase in device energy efficiency was achieved, with the aid of simulations, while maintaining its compact size. In addition to presenting the potential of using variant CFD analysis in the development of new HVAC equipment, the paper also indicates the drawbacks of using the Multiple Reference Frame (MRF) method to approximate the rotation of radial fan impellers in CFD simulations. [ABSTRACT FROM AUTHOR]

Machine learning control (MLC) is a highly flexible and adaptable method that enables the design, modeling, tuning, and maintenance of building controllers to be more accurate, automated, flexible, and adaptable. The research topic of MLC in building energy systems is developing rapidly, but to our knowledge, no review has been published that specifically and systematically focuses on MLC for building energy systems. This paper provides a systematic review of MLC in building energy systems. We review technical papers in two major categories of applications of machine learning in building control: (1) building system and component modeling for control, and (2) control process learning. We identify MLC topics that have been well-studied and those that need further research in the field of building operation control. We also identify the gaps between the present and future application of MLC and predict future trends and opportunities. [ABSTRACT FROM AUTHOR]

This paper is a response to the paper 'Comments on the paper 'Outdoor ventilation performance of various configurations of a layout of two adjacent buildings under isothermal conditions' (by Ayo et al. (2015), Building Simulation, 8: 81-98)'. [ABSTRACT FROM AUTHOR]

To ensure a comfortable building environment necessitates accurate measurement and control of air volume within duct systems to meet predetermined requirements for each area. Traditional eccentric torque dampers' measurement accuracy may be compromised by adjacent effects from local components, resulting in significant measurement errors. To solve this problem, the study combines CFD numerical simulation and full-scale experimental validation to derive a formula for controlling air volume within the dampers under operational condition. Based on the formula, a novel torque damper is developed, capable of effectively controlling and accurately measuring air volume in duct systems. By linking the two blades, the damper resists the adjacent effects of local components (elbow, variable diameter, tee) on air volume measurements without the need for additional devices, ensuring a measurement error within ±5.5%. Additionally, the novel dampers have lower resistance compared to traditional dampers. This paper provides a reference for accurately measuring and effectively controlling air volume, while also maximizing the potential benefits of multi-leaf dampers. [ABSTRACT FROM AUTHOR]

As deep borehole heat exchangers (DBHEs) extract heat from geothermal energy with depth of 2–3 kilometers, the circulation water pressure drop is larger than that of shallow-depth borehole heat exchangers, influenced by the water flow rates. This paper conducted field tests and simulation analysis to study the heat transfer performance and water circulation resistance of DBHE in coupled, where the natural circulation characteristic has been discovered and analyzed quantitatively. Results show that the water temperature and density variation along DBHE forms the driving force of natural circulation. For mechanical flow rate of 6.0 kg/s and inlet water temperature of 20.0 °C, the natural circulation flow rate reaches about 2.2 kg/s with transient heat extraction power of 78.5 kW, without energy consumption of water pumps. And the larger inlet water temperature, smaller mechanical water flow rate, higher inner tube thermal conductivity coefficient and larger depth of DBHE all contribute to the larger natural circulation water flow rate. In addition, the natural circulation could effectively decrease the comprehensive water pressure drops of DBHE, which is about 47.3% smaller than the calculated value of traditional models. Thus the natural circulation characteristic has significant influence on the heat transfer performance of DBHE, and also on the energy performance of whole heat pump systems. [ABSTRACT FROM AUTHOR]

Non-intrusive load monitoring (NILM) technology aims to infer the operation information of electrical appliances from the total household load signals, which is of great significance for energy conservation and planning. However, existing methods are difficult to effectively capture the complex nonlinear features of the power consumption flow, which affects the energy disaggregation accuracy. To this end, this paper designs a method based on temporal convolutional network (TCN), efficient channel attention (ECA), and long short-term memory (LSTM). The method first creatively proposes a two-stage improved TCN (TSTCN), which overcomes its problems of extracting discontinuous information and poor correlation of long-distance information while enhancing the ability to extract high-level load features. Then a novel improved ECA attention mechanism (IECA) is embedded, which is also combined with the skip connection technique to pay channel-weighted attention to important feature maps and promote information fusion. Finally, the LSTM with strong temporal memory capability is introduced to learn the dependencies in the load power sequence and realize load disaggregation. Experiments on two real-world datasets, REDD and UK-DALE, show that the proposed model significantly outperforms other comparative NILM algorithms and achieves satisfactory tracking with the actual appliance operating power. The results show that the mean absolute error (MAE) of all appliances decreases by 18.67% on average, and the F1 score improves by 38.70%. [ABSTRACT FROM AUTHOR]

To investigate the impact of building heat transfer on roof snow loads, roof snow loads and snow load thermal coefficients from 61 Chinese sites over a period of 50 years are simulated based on basic meteorological data such as temperature, humidity, wind speed, and precipitation, and a multi-layer snowmelt model considering the building heat transfer. Firstly, the accuracy of the multi-layer snowmelt model is validated using the data of observed ground snow load and roof snow melting tests. The relationship between meteorological conditions, snow cover characteristics, and thermal coefficients of snow loads in three representative sites is then studied. Furthermore, the characteristics of thermal coefficients in each zone are analyzed by combining them with the statistical results of meteorological data from 1960 to 2010, and the equations of thermal coefficients in different zones on indoor temperatures and roof heat transfer coefficients are fitted separately. Finally, the equations in this paper are compared with the thermal coefficients in the main snow load codes. The results indicate that the snowmelt model using basic meteorological data can effectively provide samples of roof snow loads. In the cold zone where the snow cover lasts for a long time and does not melt easily, the thermal coefficients of the snow loads on the heating buildings are lower than those in the warm zone due to the long-term influence of the heat from inside the buildings. Thermal coefficients are negatively correlated with indoor temperatures and roof heat transfer coefficients. When the indoor temperature is too low or the roof insulation is good, the roof snow load may exceed the ground snow load. The thermal coefficients for heated buildings in the main snow load codes are more conservative than those calculated in this paper, and the thermal coefficients for buildings with lower indoor temperatures tend to be smaller. [ABSTRACT FROM AUTHOR]

Building-level loads and load schedules prescribed by current modeling rules save modelers time and provide standards during whole building performance modeling. However, recent studies show that they sometimes insufficiently capture the entire building performance due to the varied loads and load schedules for different space types. As a solution to this issue, this paper presents a database of default building-space-specific loads and load schedules for use in energy modeling, and in particular code compliance modeling for commercial buildings. The existing sets of default loads and load schedules are reviewed and the challenges behind using them for specific research topics are discussed. Then, the proposed method to develop the building-space-specific loads and load schedules is introduced. After that, the database for these building-space-specific loads and load schedules is presented. In addition, one case is studied to demonstrate the applications of these loads and load schedules. In this case study, three methods are used to develop building energy models: space-specific (using knowledge of the distribution and location of space types and applying the space-specific data in the developed database), building-level (assuming a lack of knowledge of the space types and using the building-level data in the developed database), and calculated-ratio (assuming knowledge of the distribution of space types but not their locations and calculating weighted average values based on the space-specific data in the developed database). The energy results simulated by using these three methods are compared, which shows building-level methods can produce significantly different absolute energy and energy savings results than the results using space-specific methods. Finally, this paper discusses the application scope and maintenance of this new database. [ABSTRACT FROM AUTHOR]

Occupant-centric control (OCC) is intelligent control of building systems based on the real comfort needs of occupants. This paper provides a comprehensive review of how real-world data on energy-related occupant behavior (OB) can be integrated and applied in OCC systems. The aim is to accurately portray the real occupant needs and improve energy efficiency without sacrificing occupant comfort. This paper first introduces two types of OB: detailed occupancy states and energy-interaction behaviors, including methods to monitor, establish, and predict these OB. Then, OCC is divided into real-time control and model-based predictive control, and each of these four scenarios is discussed. It extensively reviews OCC methods for different equipment in four cases, covering control strategies, control scales, comfort enhancement scenarios, and energy-saving potential for each category. It is summarized that despite extensive research on OB, there are still significant challenges in integrating this research into OCC. A major issue is the lack of a bridge connecting monitoring acquired information and controls. In addition, the article reviews the current state of OCC platform development. The future direction should be combined with advanced Internet of Things (IoT) technologies, WiFi, and other communication technologies to obtain information about people's behavior and real needs in order to create truly energy efficient and comfortable smart environments. The article also discusses how enhancing the real-time feedback capability of the OCC system can help improve the overall control system capability and the importance of testing through experimentation. [ABSTRACT FROM AUTHOR]

Deep Q Network (DQN) is an efficient model-free optimization method, and has the potential to be used in building cooling water systems. However, due to the high dimension of actions, this method requires a complex neural network. Therefore, both the required number of training samples and the length of convergence period are barriers for real application. Furthermore, penalty function based exploration may lead to unsafe actions, causing the application of this optimization method even more difficult. To solve these problems, an approach to limit the action space within a safe area is proposed in this paper. First of all, the action space for cooling towers and pumps are separated into two sub-regions. Secondly, for each type of equipment, the action space is further divided into safe and unsafe regions. As a result, the convergence speed is significantly improved. Compared with the traditional DQN method in a simulation environment validated by real data, the proposed method is able to save the convergence time by 1 episode (one cooling season). The results in this paper suggest that, the proposed DQN method can achieve a much quicker learning speed without any undesired consequences, and therefore is more suitable to be used in projects without pre-learning stage. [ABSTRACT FROM AUTHOR]

Range hoods are commonly used in residential kitchens to capture pollutants generated during cooking. Direct capture represents the true capture performance of the range hood as a local exhaust device. A better metric for evaluating range hood capture performance is direct capture efficiency (DCE), defined as the ratio of pollutant directly captured by the hood to the total pollutant emission from the source. This paper clarifies the dynamic functioning mechanism of direct capture and reflux capture, analyzes the dynamic characteristics of exhaust concentration, and deduces a simple method for DCE determination based on dynamic temporal separation, where DCE is the ratio of the peak concentration of direct capture (C1) to the peak concentration of total capture (C2). Experimental and simulation studies on dynamic exhaust concentration were conducted under different ventilation conditions, and the results were in good agreement. Then, the determination methods of C1 and C2, including the experimental methods, were further studied to calculate the DCE. The measured and simulated results for DCE based on dynamic temporal separation showed good consistency, with a deviation of less than 2.0%. Finally, the two spatial separation methods for determining DCE (mass flux ratio and virtual purification) proposed and verified in our previous study showed good agreement with the dynamic temporal separation method based on C1/C2 proposed here, with a maximum deviation of 2.0%. The determination method of DCE based on dynamic time separation, particularly the experimental method, is of great significance for evaluating the real capture performance of range hoods. [ABSTRACT FROM AUTHOR]

Computational performance-driven design optimization (CPDDO) informs early building design decisions, enhancing projects' responsiveness to local climates. This paper reviews recent CPDDO studies, identifies prevalent gaps, and proposes a refined optimization framework. The framework stands out by: (1) integrating view quality alongside energy, daylight, and thermal comfort considerations, with a vector-simulation-based metric considering content, access and clarity; (2) incorporating users' adaptive behavior patterns in simulations; and (3) employing a hybrid weighting method to accommodate diverse project demands and support robust design decisions. This study applies the framework to optimize the shape and facade variables of a medium-sized office building in Guangzhou, Chongqing, Qingdao, Lanzhou, and Changchun, representing hot, warm, mixed, cool, and cold climates, respectively. Results highlight that geometry features (aspect ratio, orientation, window-to-wall ratio (WWR), and shading devices), as well as window and blinds constructions significantly impact energy, daylight, thermal comfort and view quality. Different climatic conditions, objective priorities, and facade orientations necessitate tailored design variables. Furthermore, certain findings challenge conventional recommendations; for instance, buildings in colder climates benefit from increased WWR, due to enhanced potential to harness solar radiation and improved view access, while high-performance envelope thermal settings mitigate heat transfer. These findings underscore the need for detailed, targeted research in early-stage design. The developed CPDDO framework proves effective and user-friendly, offering new possibilities for optimizing building performance, thus holds the potential to foster green, comfortable, and sustainable architecture in various practical applications. [ABSTRACT FROM AUTHOR]

Rooftop photovoltaic (PV) systems are represented as projected technology to achieve net-zero energy building (NEZB). In this research, a novel energy structure based on rooftop PV with electric-hydrogen-thermal hybrid energy storage is analyzed and optimized to provide electricity and heating load of residential buildings. First, the mathematical model, constraints, objective function, and evaluation indicators are given. Then, the simulation is conducted under the stand-alone condition. The annual return on investment and the levelized cost of energy of the system are 36.37% and 0.1016 $/kWh, respectively. Residential building with the proposed system decreases annual carbon emission by 25.5 t. In the third part, simulation analysis under different grid-connected modes shows that building system will obtain better economics when connected to the grid, but the low-carbon performance will be reduced. Finally, the cumulative seasonal impact of the countywide rooftop PV buildings is discussed. The result indicates that the energy structure proposed in this paper can effectively reduce the grid-connected impact on the local grid. This model and optimization method developed in this paper is applicable to different climate zones and can provide management support to the investors of NZEB before the field test. [ABSTRACT FROM AUTHOR]

The large amount of carbon emissions generated by buildings during their life cycle greatly impacts the environment and poses a considerable challenge to China's carbon reduction efforts. The building design phase has the most significant potential to reduce building life-cycle carbon emissions (LCCO2). However, the lack of detailed inventory data at the design stage makes calculating a building's LCCO2 very difficult and complex. Therefore, accurate prediction of building LCCO2 at the design stage using relevant design factors is essential to reduce carbon emissions. This paper proposes an ensemble learning algorithm combining Bayesian optimization and extreme gradient boosting (BO-XGBoost) to predict LCCO2 accurately in residential buildings. First, this study collected and calculated the LCCO2 of 121 residential buildings in Chengdu, China. Second, a carbon emission prediction model was developed using XGBoost based on 15 design factors, and hyperparameter optimization was performed using the BO algorithm. Finally, the model performance was evaluated using two evaluation metrics, coefficient of determination (R2) and root mean square error (RMSE), and the prediction performance of other models was compared with that of the BO-XGBoost model. The results show that the RMSE of the proposed BO-XGBoost for predicting LCCO2 in residential buildings is at least 40% lower compared to other models. The method adopted in this study can help designers accurately predict building LCCO2 at the early design stage and provide methodological support for similar studies in the future. [ABSTRACT FROM AUTHOR]

In recent years, rapid urban development has led to capsule hotels, sleep pods, and other tiny sleeping spaces that adapt to people's fast-paced lives, achieving maximum functionality with a very small footprint. However, due to the small space, human metabolic pollutant (such as CO2) is more likely to accumulate, and the air is not easily circulated. In this paper, a full-size experimental platform is set up with three types of ventilation modes to explore the exclusion efficiency of metabolic pollutants and the overall distribution of age of air under these ventilation modes. The conclusions showed that the mean values of metabolic pollutant exclusion rates for the different ventilation modalities varied very little across the spatial dimensions of the confined space but varied considerably in the area around the head. The double-side attached ventilation method was the most effective in removing human metabolic pollutants, especially in the head region (CN ≥ 0.92), while the single-wall attached ventilation method had the best air exchange efficiency (η ≥ 0.85). This suggests an inconsistent distribution of CO2 and age of air, which is contrary to general common sense. The conclusions of this paper can guide the design of ventilation for tiny sleeping spaces. [ABSTRACT FROM AUTHOR]

Urban mobility is a critical contributor to greenhouse gas emissions, accounting for over 30% of urban carbon emissions in the United States in 2021. Addressing this challenge requires a comprehensive and data-driven approach to transform transportation systems into sustainable networks. This paper presents an integrated framework that leverages artificial intelligence (AI), machine learning (ML), and life cycle assessment (LCA) to analyze, model, and optimize urban mobility. The framework consists of four key components: AI-powered analysis and models, synthetic urban mobility data generation, LCA for environmental footprint analysis, and data-driven policy interventions. By combining these elements, the framework not only deciphers complex mobility patterns but also quantifies their environmental impacts, providing actionable insights for policy decisions aimed at reducing carbon emissions and promoting sustainable urban transportation. The implications of this approach extend beyond individual cities, offering a blueprint for global sustainable urban mobility. [ABSTRACT FROM AUTHOR]

In dental clinics with an open floor plan, the risk of patient-to-patient transmission of respiratory disease is a concern. During dental procedures large amounts of bioaerosol are produced and patients cannot wear personal protective equipment. This paper examines how to effectively deploy air cleaner to reduce the infection risk in dental clinics with an open floor plan. Various locations of air cleaners at various clean air delivery rates (CADRs) were investigated. The dispersion of bioaerosol was studied through numerical simulations, and risk assessment was performed by a dose-response method. The findings indicated that dental patients downstream of the background ventilation have a higher infection risk than those to the left and right of an infected patient (i.e., the source). The lowest infection risks for the adjacent patients were found when the air cleaner was place opposite to the dentists, i.e., on the floor at low CADR levels of 2.2 m3/min or on the bench at CADR levels of 4.4 m3/min or greater. The results of this study indicated that air cleaner can mitigate the risk of patient-to-patient transmission of SARS-CoV-2 in dental clinics with an open floor plan. Background CADR levels determine the optimal placement of air cleaners. [ABSTRACT FROM AUTHOR]

Design parameters at different scales in the pre-design phase could significantly impact both building energy consumption and photovoltaic (PV) power generation potential. However, existing studies often overlook the synergistic effects of design parameters across multiple scales (block-building-facade scales) when evaluating these aspects. This paper aims to propose a workflow for the assessing building energy consumption and PV power generation potential of office blocks applicable in the pre-schematic design phase considering the synergistic influence of multi-scale design parameters, using building typology and parametric modelling approach. The study proposed a multi-scale design parameter classification system combined with parametric modelling. The study investigated 80 office blocks in Wuhan as the study case, which were classified into array type and enclosed type. Correlation analysis and multiple regression equations were used to quantify the single versus synergistic effects of different scale design parameters. Results suggest that focusing solely on a single scale during the pre-design stage is typically inadequate for understanding building energy potential. In contrast, multi-scale synergistic analysis boosts energy use intensity (EUI) by 7.56% and net energy use intensity (NEUI) by 33.96%. Under multi-scale synergistic conditions, the EUI of array type is more influenced by the building design parameters, while the NEUI is effected by the balance of multi-scales design parameters. While the EUI of enclosed types exhibit balanced effects across multi-scale design parameters, with NEUI results aligning closely with PV power generation potential. Multiple regression equations highlight building density and shape factor as key influencers for both array and enclosure layouts. This study offers designers a flexible and scalable workflow for evaluating building energy consumption and PV power generation potential in the pre-design phase. The findings can guide nearly-zero energy urban block planning to achieve a balance between energy supply and demand. [ABSTRACT FROM AUTHOR]

Radiative cooling has proven to be a useful tool to address the problems of lack of comfort and excessive energy consumption in situations of high temperatures, overheating and heat waves. Likewise, incorporating courtyards in warm climate zones has been found to be highly beneficial in addressing similar challenges. Hence, there is interest in analyzing the combined effects of both: radiative cooling and courtyards. This paper presents an analysis of the impact of the application of radiative cooling on a courtyard using a comprehensive simulation approach that includes a CFD model for the thermodynamic airflow in the adjacent roofs and inside the courtyard, equations for the transient heat conduction through roofs, walls and courtyard slabs, and a hybrid raytracing-radiosity model for the evaluation of the solar radiation reaching the building surfaces and its reflections, both of specular and diffuse origin, and for the calculation of the thermal radiation exchange, especially with the sky. The results show that in the hot season, the courtyard with radiative cooling always provides lower temperatures than the initial courtyard does, with a temperature range of 18.33 °C to 33.78 °C, compared to a range of 19.32 °C to 38.00 °C in the initial courtyard, and producing a greater difference with outdoor temperatures that can reach 12 °C versus 8 °C for the reference case. In addition, it was found that the courtyard with radiative cooling is able to significantly reduce the observed nighttime overheating by providing lower temperatures than the outdoor temperatures in the 50% of the nights studied. It was also found that the thermal loads to achieve indoor thermal comfort in the spaces adjacent to the courtyard were reduced by 63.46% to 69.85%. [ABSTRACT FROM AUTHOR]

The prediction of building energy consumption offers essential technical support for intelligent operation and maintenance of buildings, promoting energy conservation and low-carbon control. This paper focused on the energy consumption of heating, ventilation and air conditioning (HVAC) systems operating under various modes across different seasons. We constructed multi-attribute and high-dimensional clustering vectors that encompass indoor and outdoor environmental parameters, along with historical energy consumption data. To enhance the K-means algorithm, we employed statistical feature extraction and dimensional normalization (SFEDN) to facilitate data clustering and deconstruction. This method, combined with the gated recurrent unit (GRU) prediction model employing adaptive training based on the Particle Swarm Optimization algorithm, was evaluated for robustness and stability through k-fold cross-validation. Within the clustering-based modeling framework, optimal submodels were configured based on the statistical features of historical 24-hour data to achieve dynamic prediction using multiple models. The dynamic prediction models with SFEDN cluster showed a 11.9% reduction in root mean square error (RMSE) compared to static prediction, achieving a coefficient of determination (R2) of 0.890 and a mean absolute percentage error (MAPE) reduction of 19.9%. When compared to dynamic prediction based on single-attribute of HVAC systems energy consumption clustering modeling, RMSE decreased by 12.6%, R2 increased by 4.0%, and MAPE decreased by 26.3%. The dynamic prediction performance demonstrated that the SFEDN clustering method surpasses conventional clustering method, and multi-attribute clustering modeling outperforms single-attribute modeling. [ABSTRACT FROM AUTHOR]

In hot climates, the large amount of cooling load in electric vehicle (EV) results in a lot of battery energy consumption, leading the decrease of driving range. With the widespread application of windows in EV, the electrochromic glass (EC) shows great prospect in lowering the cooling load. However, researches on the application of EC in EV lack the consideration of both passive cooling measures and passenger comfort, which limits the further application of EC. In this paper, we proposed an idea combining the novel techniques of both electrochromism and radiative cooling. Computational fluid dynamics (CFD) is modeled to simulate the application of electrochromic and radiative cooling coupled smart windows in hot parking conditions, exploring the improvement effect of the window on the thermal environment, comfort and energy saving of the EV. The results indicate that, under the intense sunlight with an outdoor temperature of 33 °C, activating the air conditioning to maintain an average interior temperature of 26 °C, the coupled windows reduced the cooling capacity of the air conditioning by 762 W compared to regular windows, which can further increase the range of EV. Meanwhile, compared to simple electrochromic fully colored glass, the integration of radiative cooling technology can lower the window surface temperature by up to 10.7 °C. Moreover, compared to regular windows, the coupled windows lowered the standard effective temperature (SET*) for passengers by approximately 7 °C, significantly improving comfort. These research findings are expected to provide guidance for optimizing window design and enhancing the performance of EV. [ABSTRACT FROM AUTHOR]

Building energy simulation analysis plays an important supporting role in the conservation of building energy. Since the early 1980s, researchers have focused on the development and validation of building energy modeling programs (BEMPs) and have basically formed a set of systematic validation methods for BEMPs, mainly including analytical, comparative, and empirical methods. Based on related papers in this field, this study systematically analyzed the application status of validation methods for BEMPs from three aspects, namely, sources of validation cases, comparison parameters, and evaluation indicators. The applicability and characteristics of the three methods in different validation fields and different development stages of BEMPs were summarized. Guidance were proposed for researchers to choose more suitable validation methods and evaluation indicators. In addition, the current development trend of BEMPs and the challenges faced by validation methods were investigated, as well as the existing progress of current validation methods under this trend was analyzed. Subsequently, the development direction of the validation method was clarified. [ABSTRACT FROM AUTHOR]

The district heating system (DHS) consumes a lot of energy in winter, and its control accuracy needs to be improved urgently. To apply advanced process control (APC) in DHS, the thermal dynamic model of the existing buildings is essential. This paper uses the subspace method which is a data-driven approach for modelling the thermal dynamics of the building. The model's performance is analyzed using the collected data, and the differences compared to the classical methods are also analyzed. The method reduces the RMSE by about 20% compared with the ARX model for the same complexity. Subsequently, the analysis of the training residuals indicates that the estimate of periodic intra-building disturbance can be obtained by minimizing the training residuals. By introducing the estimated disturbance function, the RMSE on the test set is further reduced by 26%. At the end of the article, a simple parameter extrapolation experiment is conducted, and the result shows that the parameters can be extrapolated to other buildings without large errors. [ABSTRACT FROM AUTHOR]

In the recent paper, entitled 'Outdoor ventilation performance of various configurations of a layout of two adjacent buildings under isothermal conditions' by Ayo et al. (2015) ( Building Simulation, 8: 81-98), an expression is presented for evaluating the temporal average of positive air exchange rate (AER+) in a three-dimensional canyon. This comments show that this expression for the temporal average of AER+ is incorrect. Also, this discussion gives a corrected expression for the temporal average of AER+. [ABSTRACT FROM AUTHOR]

Accurately predicting the chiller coefficient of performance (COP) is essential for improving the energy efficiency of heating, ventilation, and air conditioning (HVAC) systems, significantly contributing to energy conservation in buildings. Traditional performance prediction methods often overlook the dynamic interaction among sensor variables and face challenges in using extensive historical data efficiently, which impedes accurate predictions. To overcome these challenges, this paper proposes an innovative on-site chiller performance prediction method employing a dynamic graph convolutional network (GCN) enhanced by association rules. The distinctive feature of this method is constructing an association graph bank containing static graphs in each operating mode by mining the association rules between various sensor variables in historical operating data. A real-time graph is created by analyzing the correlation between various sensor variables in the current operating data. This graph is fused online with the static graph in the current operating mode to obtain a dynamic graph used for feature extraction and training of GCN. The effectiveness of this method has been empirically confirmed through the operational data of an actual building chiller system. Comparative analysis with state-of-the-art methods highlights the superior performance of the proposed method. [ABSTRACT FROM AUTHOR]

The dynamic characteristics of different airflows on micro-scales have been explored from many perspectives since the late 1970s. On the one hand, most analytical tools and research subjects in previous contributions vary significantly: some only focus on fluctuant velocity features, while others pay attention to directional features. On the other hand, despite the wide variety of existing analytical methods, they are not systematically classified and organized. This paper aims to establish a system including state-of-the-art tools for airflow analysis and to further design a holistic toolkit named Airflow Analytical Toolkit (AAT). The AAT contains two tools, responsible for analyzing the velocity and direction characteristics of airflows, each of which is integrated with multiple analytical modules. To assess the performance of the developed toolkit, we further take typical natural and mechanical winds as cases to show its excellent analytical capability. With the help of this toolkit, the great differences in velocity and directional characteristics among different airflows are identified. The comparative results reveal that not only is the velocity of natural wind more fluctuating than that of mechanical wind, but its incoming flow direction is also more varying. The AAT, serving as a powerful and user-friendly instrument, will hopefully offer great convenience in data analysis and guidance for a deeper understanding of the dynamic characteristics of airflows, and further remedy the gap in airflow analytical tools. [ABSTRACT FROM AUTHOR]

Deep Reinforcement Learning (DRL)-based control shows enhanced performance in the management of integrated energy systems when compared with Rule-Based Controllers (RBCs), but it still lacks scalability and generalisation due to the necessity of using tailored models for the training process. Transfer Learning (TL) is a potential solution to address this limitation. However, existing TL applications in building control have been mostly tested among buildings with similar features, not addressing the need to scale up advanced control in real-world scenarios with diverse energy systems. This paper assesses the performance of an online heterogeneous TL strategy, comparing it with RBC and offline and online DRL controllers in a simulation setup using EnergyPlus and Python. The study tests the transfer in both transductive and inductive settings of a DRL policy designed to manage a chiller coupled with a Thermal Energy Storage (TES). The control policy is pre-trained on a source building and transferred to various target buildings characterised by an integrated energy system including photovoltaic and battery energy storage systems, different building envelope features, occupancy schedule and boundary conditions (e.g., weather and price signal). The TL approach incorporates model slicing, imitation learning and fine-tuning to handle diverse state spaces and reward functions between source and target buildings. Results show that the proposed methodology leads to a reduction of 10% in electricity cost and between 10% and 40% in the mean value of the daily average temperature violation rate compared to RBC and online DRL controllers. Moreover, online TL maximises self-sufficiency and self-consumption by 9% and 11% with respect to RBC. Conversely, online TL achieves worse performance compared to offline DRL in either transductive or inductive settings. However, offline Deep Reinforcement Learning (DRL) agents should be trained at least for 15 episodes to reach the same level of performance as the online TL. Therefore, the proposed online TL methodology is effective, completely model-free and it can be directly implemented in real buildings with satisfying performance. [ABSTRACT FROM AUTHOR]

As the mining depth increases, the problem of high-temperature thermal damage mainly caused by heat dissipation of surrounding rock is becoming more and more obvious. It is very important to solve the environmental problem of mine heat damage to improve the efficiency of mineral resource exploitation and protect the physical and mental health of workers. One can apply thermal insulation coating on the walls of mine roadways as a means of implementing active heat insulation. In this paper, expanded perlite (EP) and glazed hollow bead (GHB) are used as the main thermal insulation materials, ceramsite and sand as aggregate, plus glass fiber and sodium dodecyl sulfate to develop a new lightweight composite thermal insulation coating through orthogonal experiment method. According to the plate heat flow meter method and mechanical test method, the thermal insulation and mechanical properties of EP-GHB mixed ceramsite coating were studied by making specimens with different parameter ratios, and according to the analysis of the experimental results, the optimal mix ratio of the coating was selected. In addition, Fluent numerical simulation software was used to establish the roadway model, and the thermal insulation effect of the coating in the roadway under different working conditions was studied. The results show that the thermal conductivity of the prepared composite thermal insulation coating material is only 8.5% of that of ordinary cement mortar, and the optimal thickness of adding thermal insulation coating is 0.2 m, which can reduce the outlet air temperature of the roadway with a length of 1000 m by 4.87 K at this thickness. The thermal insulation coating developed in this study has the advantages of simple technology and strong practicability, and has certain popularization and application value in mine heat damage control. [ABSTRACT FROM AUTHOR]

As energy storage systems are typically not installed with residential solar photovoltaic (PV) systems, any "excess" solar energy exceeding the house load remains unharvested or is exported to the grid. This paper introduces an approach towards a system design for improved PV self-consumption and self-sufficiency. As a result, a polyvalent heat pump, offering heating, cooling and domestic hot water, is considered alongside water storage tanks and batteries. Our method of system analysis begins with annual hourly thermal loads for heating and cooling a typical Australian house in Geelong, Victoria. These hourly heating and cooling loads are determined using Transient System Simulation (TRNSYS) software. The house's annual hourly electricity consumption is analysed using smart meter data downloaded from the power supplier and PV generation data measured with a PV system controller. The results reveal that the proposed system could increase PV self-consumption and self-sufficiency to 41.96% and 86.34%, respectively, resulting in the annual imported energy being reduced by about 74%. The paper also provides sensitivity analyses for the hot and cold storage tank sizes, the coefficient of performance of the heat pump, solar PV and battery sizes. After establishing the limits of thermal storage size, a significant impact on self-efficiency can be realised through battery storage. This study demonstrates the feasibility of using a polyvalent heat pump together with water storage tanks and, ultimately, batteries to increase PV self-consumption and self-sufficiency. Future work will concentrate on determining a best-fit approach to system sizing embedded within the TRNSYS simulation tool. [ABSTRACT FROM AUTHOR]

A large number of cases show that the multi-objective optimization method can significantly improve building performance. The method for multi-objective building performance optimization (BPO) design has achieved rapid development in recent years. However, the BPO method still needs to be improved. Specifically, weak interaction between the optimization process and the decision-making process results in low optimization efficiency, which limits the widespread application of the optimization method in early design stage. In this paper, a new interactive BPO mode is explored to strengthen the interaction between the optimization process and decision-making process, and a preference-based multi-objective BPO method is proposed to account for designers' decision preferences during the optimization process, making the objective more controllable, improving the optimization efficiency and ensuring the diversity of solutions. Firstly, this paper illustrates the proposed method in detail, defines the concept of performance preference, expounds the flow of the preference-based multi-objective optimization algorithm, and proposes three indicators to evaluate the algorithm, which includes convergence speed, preference satisfaction rate, and diversity measurement. Secondly, through testing and comparison, it is found that the proposed preference-based algorithm has advantages over the non-preference optimization algorithm (represented by the NSGA-II algorithm). The proposed method leads to faster convergence and higher preference satisfaction, so it is more suitable for the BPO process in the early design stage. Specially, the proposed method can achieve 100% preference satisfaction rate with only 2400 simulations, while the non-preference method can only achieve 20% preference satisfaction rate after 5800 simulations. In this paper, a preference-based multi-objective BPO method is proposed to make the optimization process closely interact with the decision-making process and make the design preferences be accounted during the BPO process, thereby improving the optimization efficiency. In addition, this study first proposes two indicators to measure the quality of optimization results: preference satisfaction rate and diversity measurement. This study aims to guide the development of BPO methods towards providing high satisfaction rate and high quality optimization results. [ABSTRACT FROM AUTHOR]

Simulating the thermal performance of Double-Skin Facades (DSFs) is a complex issue faced by many architects who attempt to utilise them for energy-saving, acoustic, and aesthetic benefits. EnergyPlus is among the most widely used building simulation programs, and its capability of simulating DSFs is controversial which derives the need for further investigation. DSFs are usually designed with flat surfaces, and thus the simulations performed in previous studies to verify the accuracy of EnergyPlus were almost always dealing with simple flat facades. This paper presents a preliminary investigation into the reliability of EnergyPlus in simulating the thermal performance of a geometrically complex unconventional DSF. The increase of geometrical complexity increases the challenge of DSF simulation. Using CFD, the paper also aims at having a better understanding of the thermal performance of the DSF which was not possible using only EnergyPlus. A numerical experiment is performed in which two models of a DSF are compared; one simulated with EnergyPlus and the other with OpenFOAM CFD software. The average cavity temperature and the volume flow rate of the air at the outlets are compared. Results of the comparison showed minor differences between them, which gives a positive indication of the potential of EnergyPlus in handling complex geometries with complex physical phenomena. Conducting physical experiments on built prototypes to verify the results represents both the main limitation of this work and its future plan. The paper discusses some of the challenges that prevent architects from using CFD, and how they can be addressed, then ends with some design recommendations for DSFs in hot climatic contexts. [ABSTRACT FROM AUTHOR]