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Effective planning and targeted implementation of building energy efficiency programs explicitly require robust knowledge on significance pattern of involving factors for precision model-based estimates of potential energy savings. Though extensively adopted for factor significance characterization, prior sensitivity analysis (SA) frameworks fail to capture typical influences attributed to commonly used categorical factors. Moreover, they often assume the assessed factors are independent which can be invalid in many cases to generate misleading outcomes. To overcome these observable defects, this paper develops a novel integrated stochastic level-based sensitivity analysis approach for exhaustively patterning effectual significance of all available building factors of varied type while incorporating diverse factor interactions on validated reliability. While the integration of categorical factors is achieved through factor-type dependent variation levelling, the random factor interactions are apprehended by stochastic sampling and nonlinear support vector network computing to accommodate collinearity trap. Multivariate clustering identifies significance pattern via a composite multi-statistic sensitivity metric derived from stochastic energy responses. Case results of 289 U.S. residential houses validate its utility. This presented approach benefits to perform reliable full sensitivity investigations and analyses under collinearity for building energy systems with verifiable robustness. [ABSTRACT FROM AUTHOR]

Increasing the building renovation rate is a key initiative to drive energy efficiency in the sector and contribute to the European Green Deal. Within this paper, the BIM-based building renovation decision support system RenoDSS is described. RenoDSS (i) automatically generates renovation scenarios based on user preferences and the IFC representation (including second level space boundaries data) of the current building configuration (baseline), (ii) calculates baseline and renovation scenario KPIs, and (iii) identifies the most suitable scenarios by comparing the scenarios' energy, finance, and sustainability KPIs. Validation conducted at two pilot sites shows that RenoDSS reduces the time for (i) identifying potential renovation scenarios, (ii) calculating their energy and LCA/LCC KPIs, and (iii) producing an IFC file for each renovation scenario by around 80% compared to tool-supported manual means. RenoDSS is used by (i) renovation designers that want to optimize the thermal building hull in combination with PV, solar thermal, and HVAC, as well as (ii) decision makers who want to collaboratively identify the most promising renovation option out of the ones generated by RenoDSS. [ABSTRACT FROM AUTHOR]

The optimization of energy consumption in buildings' HVAC systems plays a crucial role in reducing greenhouse gas emissions worldwide. In new and deeply-renovated buildings, characterized by modulating terminal units and aiming at the maximum exploitation of renewables, an accurate hydraulic balance of the distribution network may become critical, resulting in a significant increase of pumping energy consumptions and a deterioration of indoor thermal comfort conditions. In the present paper, we compare the performance of traditional manual balancing valves and new pressure independent control valves (PICVs), used to balance the hydronic loop of HVAC systems. To perform this analysis, a new MATLAB-Simulink model has been specifically developed to simulate the behavior of PICVs and has been used in an application case study. The efficiency of manual and pressure independent control valves is evaluated numerically by simulating a multi-zone distribution network under variable operating conditions and considering different control strategies of the circulating pump. Results show that in off-design conditions, when some of the branches of the hydraulic network are closed, traditional manual balancing valves are not able to guarantee the nominal mass flow rate in the remaining loops. On the contrary, no significant variations of water mass flow rate are observed when PICVs are adopted, even in partial load conditions. Despite their additional cost, PICVs allow to ensure better indoor thermal comfort sensations for individuals, avoiding under/over-heating of rooms and, moreover, yield a decrease of the pump electric consumption with respect to traditional manual balancing valves. In addition, based on the obtained results, general rules concerning the adoption of PICVs are provided, depending on the extension of the hydraulic loop and the adopted pump control logic. The reported results highlight the role that PICVs can play to ensure energy savings in HVAC systems without penalizing users' indoor comfort conditions. [ABSTRACT FROM AUTHOR]

• Investigated climate change impacts on office building performance in Brussels. • Applied a long-term climate change sensitive system sizing and design approach. • Time-integrated discomfort analysis showed a major surge in overheating by 2100. • Long-term climate change resistivity is ensured in reference building until 2100. • Primary energy use and GHG emission findings are useful in mixed humid climates. As climate change continues, it is expected that the risk of overheating will rise in both new and existing buildings in mixed humid climate zones in Europe. This study introduced a novel climate change sensitive sizing and design approach for cooling and heating systems in nearly zero-energy office buildings in Brussels, Belgium, for different weather scenarios. This approach considered the long-term effects of climate change on building performance. The climate change effects were assessed using current and future climate data from the regional atmospheric model, MAR. To demonstrate the approach, a case study of a nearly zero-energy office building in Brussels was conducted. The reference building model was first calibrated using monthly energy use data from the year 2019 using ASHRAE Guideline 14. Then, the building was evaluated with different HVAC strategies and their performances were quantified. The results indicated an increase in overheating as high as 1.2 °C and cooling energy use as high as 13.5 kWh/m2 and a decrease in overcooling as low as 0.3 °C and heating energy use as low as 10.9 kWh/m2 in the reference building by the end of the century. In addition, due to climate change sensitive sizing and design approach coupled with optimal sizing, the reference building was climate change resistant towards the worst-case scenario by end of the century with up to 3.7 for climate change overheating resistivity and 20.2 for climate change overcooling resistivity. Finally, the paper provided recommendations for future practice and research based on the study findings. [ABSTRACT FROM AUTHOR]

• Building performance simulation is powerful but computationally expensive. • Machine Learning (ML) surrogate models can reduce simulation times significantly. • Building energy and peak loads are predicted under different operational settings. • Extreme gradient boosting outperformed other methods. • Linear regression showed the lowest training time with competitive predictive power. Building Performance Simulation (BPS) is a powerful and widely used technique to evaluate building design and operation strategies prior to construction or retrofitting. However, BPS models often have high computational costs, which is particularly limiting for applications that require a significantly large number of simulations, such as building design optimization or uncertainty analyses. To overcome this gap, researchers have turned to surrogate modeling, where a mathematical model, such as a machine learning algorithm, is trained to mimic the performance of a BPS, allowing to test numerous building design/operation configurations at low computational costs. Past studies have applied surrogate BPS modeling to predict the impact of building design parameters on energy performance. However, few have considered building operational parameters, such as occupancy, equipment and lighting usage, and thermostat setpoints, which significantly impact energy consumption and peak loads, especially in harsh climate conditions. This paper presents a unique evaluation and comparison of machine learning algorithms as surrogates to BPS predictions of building performance (energy consumption and peak loads) under different operational settings. Results indicate that Extreme Gradient Boosting outperformed all other methods in predictive accuracy, with R2 values reaching as high as 0.99 for some models. In contrast, linear regression models were the fastest to train and easiest to interpret while still achieving competitive prediction accuracies (R2 values > 0.9). This work provides direct evidence of the machine learning surrogate models' ability to accurately predict building performance under different operational settings. It also offers unique insights into the strengths and weaknesses of white-box and black-box predictive modeling approaches and the effect of dataset size on the results. [ABSTRACT FROM AUTHOR]

• Provided a platform to test various residential building control systems. • Used probability of occupancy to determine setback temperature when unoccupied. • Integrated optimizer with adaptive and occupancy control. • Generated dynamic electricity tariff based on wholesale electricity pricing. • Evaluated the peak load shaving potential with dynamic pricing and optimization. Energy consumption in buildings continues to rise with increased deployment of energy-consuming equipment such as Heating, Ventilation, and Air Conditioning (HVAC) amid a growing world economy. Renewable energy is projected to comprise a majority of the future electricity supply, but the intermittent nature of renewables means that consumption must respond to dynamic supply for optimal utilization. This paper proposes a novel HVAC control strategy for residential buildings using the adaptive comfort model, considering occupancy through probability and real-time information, and optimizing the HVAC schedule to reduce cost, maintain thermal comfort, and respond to the dynamic availability of renewable energy while being generalizable to different situations. To validate this approach, the Universal CPS Environment for Federation (UCEF) co-simulation platform is used to connect advanced building controls with the building energy simulation software EnergyPlus. Simulations are performed for a residential building in Sacramento, CA during a typical summer week. Economic impacts, energy consumption, and thermal comfort are analyzed for traditional, adaptive, and occupancy-based control strategies under demand-based, tiered, and fixed electric tariff systems. Simulation results show that occupancy consideration, adaptive thermal comfort, and optimization can reduce cost by 50.1 %, electricity consumption by 52.9 %, and discomfort by 56.2 % compared to traditional fixed setpoints. The ability of the proposed HVAC control strategy to shift energy consumption away from peak times under a demand-based tariff system is qualitatively analyzed and findings suggest that maximum load-shifting on a grid-scale is attained using occupancy consideration with optimized control and demand-based pricing. For individual residential buildings, similar economic benefits can be gained using the less-complex adaptive HVAC control strategy with existing tiered or simple electric tariff systems. [ABSTRACT FROM AUTHOR]

This paper investigates the use of dilated causal convolutional neural networks for fine-grained temporal forecasting of building zone states. Specifically, we build and evaluate models using a small set of exogenous features (e.g., external temperature) to autoregressively predict zone airflow setpoints every minute for a 24-h prediction window. We carefully explore the trade-off between generality and specificity in these models, training and evaluating them based on zone, zone type, month, season, and combinations thereof. When evaluated for a commercial office building in Eastern Washington with 16 zones served by variable air volume air handling units, we find that the highest performance comes from a zone-specific, season-agnostic approach; with it, we obtain an R 2 of 0.704 (averaged over zones) and an average normalized root mean square error (nRMSE) of 0.111. In contrast, the most general model (trained across all zones and seasons) yields an R 2 of only 0.416 and a nRMSE of 0.168, while a baseline zone-specific reduced order model obtains 0.443 R 2 and 0.159 nRMSE. We also report on factors affecting airflow forecasting performance, on the ability of models trained on a specific zone to generalize to other zones, and on the capability of those models trained on a specific month to generalize to other months. [ABSTRACT FROM AUTHOR]

The Solar Decathlon is a competition for universities from all over the world which focuses on designing, building and operating experimental, solar-powered houses. Participating in the project offers universities a unique and interdisciplinary platform for teaching, learning and research which combines practical experience with research. In 2022, the European edition was held in Germany for the first time. The event took place with a new urban profile to increase both the relevance of the competition and the learning experience. Its main topic was the further development of the European city, and specifically focused on the existing stock of residential apartment buildings. A total of 18 teams from 11 countries with over 500 students took part, with 16 teams ultimately building their houses on a shared solar campus. Demonstrating a balanced or a positive energy balance in practice was one of the essential goals of the competition. This was achieved by 13 of 15 projects in the energy contest. The prerequisites for this were a high level of energy efficiency and the consistent use of solar energy. Both strategies were embedded in convincing architectural concepts. These ranged from the minimised visibility of standard systems on rooftops, through to custom-built systems with full architectural integration in façades. Hybrid solar systems also became a focus, with the goal of making optimum use of the surfaces on the building envelope. This paper focuses on the energy engineering and technical and architectural integration of the solar systems. It also includes the results achieved in the competition linked to the learning experience. [ABSTRACT FROM AUTHOR]

[Display omitted] Reinforcement learning (RL) algorithms have shown great promise in controlling building systems to minimize energy use, operational cost, and occupant discomfort. RL agents learn a control policy by interacting with the physical or simulated environment that represents building systems, occupants, and the outside world. Yet, a large amount of data is needed to learn a near-optimal control policy in the physical building, which requires months or years to collect. Moreover, an agent's performance while training can be quite poor, causing occupant discomfort and additional costs. Learning in simulation does not have such real-world impacts, but differences between buildings, and indeed between simulation and physical buildings, potentially lead to poor performance when a policy learned in simulation is deployed in a physical building. This paper addresses part of the sim-to-real problem by training on one set of simulated (source) buildings and then deploying to a novel simulated (target) building. This approach significantly reduces the training cost of RL on the target building by 1) learning a large number of policies on prototype buildings, 2) evaluating these policies on historical data obtained from the target building's environment and selecting the best ones according to the evaluation result, and 3) using the best policies to control the target building while continuing to learn. The proposed approach involves learning a diverse population of control policies using a novel diversity-induced RL algorithm, and policy clustering, evaluation, and selection techniques. Three case studies show our approach assigns policies to the target building that outperform the default controller by 4.0–30.4%, without sacrificing thermal comfort. Similarly, they outperform policies that are learned only on the target building (i.e., without transfer) by 24.9–74.9% and 16.2–72.2% before and after 500 months of training, respectively. [ABSTRACT FROM AUTHOR]

Industry is stuck in reactive mode regarding the maintenance of their heating, ventilation and air conditioning (HVAC) systems, which leads to inefficient operation. To avoid wasteful use of natural resources a proactive approach is needed. To enable this transition combined knowledge-based and data-driven approaches such as the Industrial Data Analysis Improvement Cycle (IDAIC) framework have been proposed, but there is a lack of practical implementation in the literature. In this paper, we implement the IDAIC framework on an Air Handling Unit (AHU) in a real industrial facility to address the lack of practical and reproducible real world analysis. The lack of domain knowledge-driven data mining solutions is addressed primarily in two parts. Firstly, data and control issues are addressed to enable the application of knowledge-based techniques to detect faults that are easily identifiable. Secondly, data-mining based techniques are applied which focus on the detection of more subtle faults which are indicative of component degradation. This approach enabled the development of data-driven analysis that visualises the effect of the different modes of operation on the energy consumption of the AHU, which allow the onsite facilities team to make more informed decisions regarding the maintenance of this AHU. [ABSTRACT FROM AUTHOR]

Recent research efforts on modeling personal thermal comfort support the integration of personalized preferences in optimal building control and further implementation in real buildings. This paper presents the development and field implementation of personal preference-based thermal control (i.e., a controller that provides thermal conditions to satisfy personal preferences) in real offices, emphasizing the role of model predictive control (MPC) and low-cost local sensing. Probabilistic thermal preference profiles were developed from experiments collected in identical private offices with controllable VAV systems. A low-cost thermal sensing network and a MPC framework were integrated into a centralized building management and control system. Customized, preference-based HVAC control was then experimentally implemented in the offices to (i) quantify the personal comfort penalty when using conventional wall thermostat versus local sensing-based operation for two distinct thermal preference profiles; (ii) evaluate the impact of personalized MPC (dynamic setpoint) on energy use and personal comfort compared with personalized simple feedback control (static setpoint), using local sensing; (iii) compare the personalized MPC performance for two distinct thermal preference profiles under different weather conditions. The results indicate the comfort benefits of monitoring local thermal conditions (vs wall thermostats) for different preference profiles and showed 28–35% energy savings with personalized MPC (vs personalized static setpoint control). The overall personalized MPC performance (and energy consumption) depends on the personal thermal preference characteristics and outdoor conditions. [ABSTRACT FROM AUTHOR]

• The data requirements of quota methods are summarized, and a city-scale database containing 670 credible government office buildings is established. • By comparing the processes, the results and applicability, the selection basis of quota methods under different conditions is refined. • A flow chart of the formulation process and method selection of city-level building energy consumption quotas is constructed. • It is proposed that special attention should be paid to the outliers, and the characteristics of outliers in Chongqing are summarized. Energy consumption quota policies are effective management measures to promote energy conservation and emission reduction in China. Cities need to choose an appropriate method to formulate local energy consumption quota standards. This paper is based on the data of 670 government office buildings in Chongqing and the selected quantile method (QM), quota-level method (QLM) and multiple linear regression (MLR) method, which are widely used in the formulation of city-level energy quotas. By comparing their interpretability, evaluation, consistency and stability, the application conditions and applicability of each method are summarized. MLR has the strongest explanatory power but the highest requirements for the technology and information dimensions. The QLM is more explanatory and stable than the QM and is more suitable for the initial stage of energy quota management. When using the QLM, we should consider scientific classifications and reasonable quota levels. Special attention should be given to "outliers" with abnormal evaluations of the quota results. The outliers in Chongqing are characterized as buildings that have been used for more than 10 years and have an area of less than 5000 m2. Finally, this paper constructed a flow chart of the formulation process and method selection to help policymakers better understand how to choose the most appropriate method and identify inefficient buildings. [ABSTRACT FROM AUTHOR]

This paper illustrates an energy upgrading proposal of a historical building in Naples (Italy): Palazzo Fuga, better known as the Real Albergo dei Poveri which was built in the second half of the XVIII century. The proposal consists of energy efficiency interventions on the building envelope and its plants. As energetic and thermal aspects are concerned, simulations were performed in order to size a photovoltaic system, considering lighting aspects, and to evaluate the improvement of the building energy performance. Furthermore, thermal and lighting measurements were performed with a double aim: to verify the factual state of the building and to calibrate the calculation tools to the particular situation. In this paper obtained results concerning the feasibility of interventions will be presented and discussed. [ABSTRACT FROM AUTHOR]

This paper recognizes the synergetic relationship and inextricable linkage between conservation and sustainability. Heritage preservation maximizes the use of existing materials and infrastructure, reduces waste, and preserves the historic character of older towns and cities. This paper also presents technical advances in BPS/BES (Building Performance Simulation/Building Energy Simulation), and in co-simulation for energy efficiency optimization, RES integrated historic building's greening and historic neighborhoods, settlements, as well as whole cities urban re-planning. Further, the paper presents initial results of the Aviation Museum in Belgrade preliminary energy refurbishment study focusing results obtained via BPS and co-simulation. Foreseen is the deep energy refurbishment aimed to reach the “total building performance optimization” encompassing multifunctional optimization of building's skin – glazing replacement with the semi-transparent PV glazing and day-lighting control with reference to the artifact preservation, as well as the IEQ (Indoor Environment Quality) control analysis. This study shows that the RES integrated refurbishment can result in the Museum's Zero CO 2 emission and even in the sustainable Energy + status (producing more than using electricity). Finally, needs for most current ICT knowledge use and refurbishment technologies development, as well as integrated design, including particularly relevant benchmarking and rating system development are outlined. [ABSTRACT FROM AUTHOR]

This paper introduces a temperature-based approach – called the Days Exceeding Threshold-Toa (DET-Toa) method to detect persisting small increase or decrease in the normal building energy consumption. This method identifies an abnormal energy consumption fault when the deviation between the measured and simulated consumption is greater than one standard deviation of the residuals in the baseline period and persists for at least 20 days which are consecutive when ordered according to increasing or decreasing outside air temperature. The fault detection capability of the method is evaluated with simulation tests with two on-campus buildings. Ten synthetic control changes were assumed to happen and lasted for one year for each building. In the test, the DET-Toa method showed superior capacity for detecting abnormal building energy consumption compared with the DET-Date method. It successfully detected 19 synthetic control changes in the 20 simulation test cases. The reason for the better performance of the DET-Toa method is discussed and demonstrated in an example case. The smallest faults that could be identified by the DET-Toa method in the two analyzed buildings and their related energy consumption impact statistics are also provided in the paper. [ABSTRACT FROM AUTHOR]

In this paper a model for choosing the proper modular configuration of a point-focus concentrating thermo-photovoltaic (CPV/T) system according to the domestic user energy demands, is presented. The main aim is to evaluate several modular configuration solutions in terms of energy and economic performances and space occupied. First of all, the main CPV/T system components are chosen: optics, solar cells and tracking system. A point-focus system with parabolic mirrors, triple-junction cells and dual-axis tracking system is considered. The choice of the proper CPV/T system configuration is realized considering an equations system that defines the electric and thermal performances in terms of solar radiation, cells number and concentration factor. The choice model presented in this paper allows to configure, referring to different Italian zones, the CPV/T system under several working conditions minimizing spaces and costs. It is observed that CPV/T modules with a lower cells number allow high flexibility to satisfy the loads. The high concentration level determines interesting solutions related to a domestic application from the energy and economic point of view, above all in southern Italy, during the CPV/T system life cycle. Finally, CPV/T systems allow a significant reduction of CO 2 emissions due to the primary energy savings. [ABSTRACT FROM AUTHOR]

This paper summarises the results of a study to identify and quantify the potential impact of measures and policies to reduce the energy consumption of air conditioning in European countries over a 10-year period. The market penetration of air conditioning in Europe has increased steadily over several decades. On a “business as usual” basis, its energy consumption would increase by over 50% by 2020. Measures to restrict this increase are therefore important, but the savings potential straddles several overlapping areas which are subject to different constraints including those imposed by the replacement rates of systems and products, and the refurbishment rates of existing buildings. This work reported in this paper is based on the use of information from a variety of sources in a highly disaggregated model to assess the “realisable” savings over a 10-year period, taking into account relevant constraints. The paper summarises results at a European level, focussing on those cases that generate the greatest realisable savings. Savings are possible in three areas: improved equipment and system efficiency, reduced cooling loads, and more effective operation. The cases that offer the largest savings provide the basis for recommendations for policy measures, often using existing policy instruments. In addition, areas where further work is needed are identified. More detailed results and information about the study and additional recommendations can be found at http://www.bre.co.uk/searchresults.jsp?category=5&q=energy+management [1] . [ABSTRACT FROM AUTHOR]

Growing demand for urban built spaces has resulted in unprecedented exponential rise in production and consumption of building materials in construction. Production of materials requires significant energy and contributes to pollution and green house gas (GHG) emissions. Efforts aimed at reducing energy consumption and pollution involved with the production of materials fundamentally requires their quantification. Embodied energy (EE) of building materials comprises the total energy expenditure involved in the material production including all upstream processes such as raw material extraction and transportation. The current paper deals with EE of a few common building materials consumed in bulk in Indian construction industry. These values have been assessed based on actual industrial survey data. Current studies on EE of building materials lack agreement primarily with regard to method of assessment and energy supply assumptions (whether expressed in terms of end use energy or primary energy). The current paper examines the suitability of two basic methods; process analysis and input–output method and identifies process analysis as appropriate for EE assessment in the Indian context. A comparison of EE values of building materials in terms of the two energy supply assumptions has also been carried out to investigate the associated discrepancy. The results revealed significant difference in EE of materials whose production involves significant electrical energy expenditure relative to thermal energy use. [ABSTRACT FROM AUTHOR]

In this paper, a mathematical model is formulated to quantify the social impact an individual has on his/her community when he/she performs any energy efficiency project and transmits that information to his/her neighbours. This model is called the expected power savings model; it combines direct and indirect expected power savings of the energy efficiency project for each individual within the network. The indirect savings are quantified through the social interactions people in the network. The example used in this paper illustrates the effectiveness of the model by identifying the households who should have free solar water heaters installed in their residential houses based on their influence through interactions in their community. Two case studies are considered in this paper, single and multiple sources case studies. In the multiple source case study, the results show that it is not necessarily the people with the highest connections who provide the maximum expected power savings. [ABSTRACT FROM AUTHOR]

This paper considers energy conservation practices in Qatar with special emphasis on commercial buildings. Energy conservation approaches are classified into five main areas and an Energy Conservation Matrix (ECM) database for buildings is developed. The ECM maps the energy conservation techniques/technologies to models versus their application domain. Three scenarios (building envelope design, change in customer behavior, and consideration of renewable energy supply) are analyzed to study different alternatives for efficiency improvement. The analysis is done in a case hotel. The analysis shows that, the energy conservation potential of using envelope redesign for the case study is about 7.5% while conservation through behavior change ranges between 2.74% and 15.80%. The conserved energy potential ranges between 10% and 24.12% of the site energy in the combinatorial scenario that integrated envelope design alternatives with customer behavior change. The renewable energy (RE) scenario conserves energy, indirectly, by using green energies generated from renewable sources. The output shows that due to changes as per the three scenarios, total CO 2 emissions of the building are also reduced. The analysis shows that adoption of 30% RE alternative can reduce emissions by about 27% with respect to the reference scenario. It is believed that the scenarios developed in this paper and the results obtained will motivate the designers to consider alternative designs or redesigns in the large scale commercial buildings. [ABSTRACT FROM AUTHOR]

The energy consumption in a multi-chiller system is very significant and its consumption will be increased by inappropriate operating of the chillers. Accordingly, the economic dispatch in multi-chiller systems becomes an important issue for saving energy. Such issue is known as optimal chiller loading, which is solved by applying different optimization methods. This study employs exchange market algorithm to obtain optimal set points of the economic dispatch problem on chiller plants, which is inspired by trading the shares on a stock market. Exchange market algorithm is proposed for solving continuous non-linear optimization problems and it is comprised of two searching and two absorbing operators. The decision variable of the optimal chiller loading is partial load ratio of each chiller and the objective function of the optimal chiller loading problem is minimizing the power consumption while satisfying the cooling demand. This paper adopts three case studies to evaluate the performance of exchange market algorithm and compares it with other optimization methods applied on the optimal chiller loading problem. Results show that exchange market algorithm is an efficient method for solving optimal chiller loading problem in chiller plants, and is adaptable for usage in air-conditioning systems. In addition, the comparison results show that the exchange market algorithm provides better solutions than previous optimization methods applied on the optimal chiller loading method in terms of convergence speed and total consumption of electrical energy. [ABSTRACT FROM AUTHOR]

A building energy benchmark indicates the reasonable level of energy demand, and it facilitates the policy making when facing with uncertain information at the planning stage. Traditional benchmarks are established based on statistical indicators or physical simulations of prototype buildings, while the diversified consumption levels of various buildings are rarely considered carefully. Pointing to this, a data-driven benchmarking methodology is explored based on the Lorenz curve, which is a specific measurement tool of income inequality. In this paper, the Lorenz curve is used to identify the distribution inequality of energy consumption among buildings. In particular, the Lorenz curves of energy consumption were firstly derived from 195 buildings of three building types in Beijing. Afterwards, a variable was extracted from the fitting Lorenz curve, in order to quantify the nonlinear distribution principles of energy use intensity along with the accumulative building area. Finally, empirical benchmarking formulae for the three building types were achieved together with stepwise applications. The validation showed that relative errors of accumulated energy consumption for offices, hotels and shopping malls were −5.27%, 3.57% and −3.56% respectively. This data-driven approach is developed based on the limited information, and it attempts to assist energy-policy planners in establishing energy benchmarking targets and promoting energy efficiency. [ABSTRACT FROM AUTHOR]

The use of energy in the livestock sector will rise in the years to come due to the increasing demand for animal proteins from a word population that will reach 9.15 billion people before 2050. Livestock houses for intensive animal farming are characterized by high energy consumption due to climate control that is needed to guarantee animals’ welfare and to maximize their production. Currently, there are neither international standards calculation models nor commercial tools for the estimation of this energy consumption. To fill this gap, this paper presents a simulation model (based on a customization of the hourly model of ISO 13790) for the estimation of the energy consumption due to climate control of broiler houses. This model provides the energy consumption for heating, cooling and ventilation and the main indoor environmental parameters for the evaluation of the animals’ welfare. The model was validated comparing its outputs with a dataset obtained through a monitoring campaign carried out in a broiler house during a production cycle. This research has several practical application: it can increase the knowledge about energy consumption in the livestock sector and the model can be also a useful tool for agricultural engineers and farmers. [ABSTRACT FROM AUTHOR]

The use of Maisotsenko Cycle (M-Cycle) has enhanced the domain of evaporative cooling technologies to sub-wet bulb temperature cooling while ensuring moisture control. Several studies have demonstrated the use of cross-flow heat & mass exchanger (HMX) offers higher cooling capacity; however, it has lower cooling effectiveness and Energy Efficiency Ratio (EER). In contrast, a counter-flow (HMX) offers high cooling effectiveness with lower cooling capacity. In this paper, the performance of counter-flow HMX is enhanced by addition of alumina nanoparticles in feed water due to enhanced heat and mass transfer characteristics of nanofluids compared to original base fluid. Here, a mathematical model is formulated by incorporating the nanofluids in a selected control volume. The developed model is solved numerically on a discretized HMX length. Initially, the model is benchmarked against previously published results using water as base fluid. A comparison between HMX performance using water and alumina nanofluid is performed in terms of Performance Enhancement Ratio (PER). PER indicates 1–18% increase in cooling effectiveness, 18–43% increase in cooling capacity and 9–19% increase in EER by using alumina in water when working air temperature is increased from 20   °C to 45   °C. Similarly, an increase in PER is also observed by changing air velocity. Increase of 41% is observed in cooling capacity and 18% increase in EER is observed by changing particle volume fraction from 0 to 2%. This research identifies ways to reduce the carbon emissions of a building by increasing the energy efficiency of existing evaporative cooling technology using nanofluids. [ABSTRACT FROM AUTHOR]

The focus of carbon management is shifting from operational carbon to embodied carbon as a result of the improved operational energy efficiency of buildings. Measuring and managing embodied carbon right from the early stages of projects will unlock a range of opportunities to achieve the highest possible emissions reduction which could not be achieved otherwise during the latter stages. However, measuring embodied carbon during the early stages of design is challenging and highly uncertain due to the unavailability of detailed design information. Therefore, the research presented in this paper addresses this problem in a structured and an objective way. A parametric embodied carbon prediction model was developed using regression analysis to estimate embodied carbon when only minimal design information is available and with less uncertainty. The model was developed by collecting historical data of office buildings in the UK from four different data sources and estimating embodied carbon by combining several estimating techniques. Wall to floor ratio and the number of basements were identified as the model predictors with a model fit of 48.1% (R 2 ). A five-fold cross-validation ensured that the model predicts within the acceptable accuracy range for new data. The developed model had an accuracy of ±89.35% which is within the acceptable accuracy range for an early stage prediction model. In addition, the need for standardising embodied carbon measurements and to develop embodied carbon benchmarks to facilitate embodied carbon estimating throughout the project lifecycle were emphasised. [ABSTRACT FROM AUTHOR]

Greenhouse gas emissions have been recognized as one of the major cause of the global warming phenomena. The built environment accounts for more than 40% of the overall energy consumption and 36% of the overall CO2 emissions in Europe. Recent studies show that housing is one of the most responsible sector for world ecological impacts. The European Parliament developed the concept of Nearly Zero Energy Buildings (NZEB), characterized by a very low energy demand and a high renewable energy on-site production. In fact, energy efficiency is the first step towards the ambitious aim to reduce of 80% by 2050 the EU carbon emissions. The zero-energy building target is an achievable goal, which relies on a careful design that encompasses a synergy between passive and low-energy strategies. However, considering the whole life cycle of buildings, NZEBs reduce the operational energy close to zero, increasing the relevancy of the embodied energy, which occurs during the construction phase. Balancing the values of the operational and embodied energy is necessary to minimize buildings footprint on the environment. In this paper the renovation and re-use of the Atika building, a demonstrative energy-efficient building, is presented as case study of an environmental efficient methodology for energy retrofitting. The case relies on the methodology developed by Active House, a holistic vision for sustainable buildings labeling. [ABSTRACT FROM AUTHOR]

The use of heat pumps in district heating is one of the most promising technologies for enhancing the efficiency of systems and for meeting the 2030 and 2050 European energy and climate targets. It is essential to assess the available data, the practice and experience of planners and engineers to determine heat pump placement, connection and operational modes in district heating networks, as the systems ought to be capable of covering residential heat requirements for all year around operation. The main selection criteria for the appropriate heat pump system are the heat source, the heat pump technology and the heat requirements. These parameters form a technical triangle, which should be used as a comprehensive instrument to enable heat pump integration into residential district heating. The triangle allows the definition of the bidirectional interdependences and the design of a high efficiency, sustainable heat pump based system. This paper presents the solution to scenarios of how to integrate heat pumps into district heating, in terms of heat pump placement, connection and operational modes based on available heat sources and heat requirement profiles and their environmental impact. [ABSTRACT FROM AUTHOR]

Some highly energy efficient window attachment products are available today, but more rapid market adoption would be facilitated by fair performance metrics. It is important to have validated simulation tools to provide a basis for this analysis. This paper outlines a review and validation of the ISO 15099 center-of-glass zero-solar-load heat transfer correlations for windows with cellular shades. Thermal transmittance was measured experimentally, simulated using computational fluid dynamics (CFD) analysis, and simulated utilizing correlations from ISO 15099 as implemented in Berkeley Lab WINDOW and THERM software. CFD analysis showed ISO 15099 underestimates heat flux of rectangular cavities by up to 60% when aspect ratio (AR) = 1 and overestimates heat flux up to 20% when AR = 0.5. CFD analysis also showed that wave-type surfaces of cellular shades have less than 2% impact on heat flux through the cavities and less than 5% for natural convection of room-side surface. WINDOW was shown to accurately represent heat flux of the measured configurations to a mean relative error of 0.5% and standard deviation of 3.8%. Several shade parameters showed significant influence on correlation accuracy, including distance between shade and glass, inconsistency in cell stretch, size of perimeter gaps, and the mounting hardware. [ABSTRACT FROM AUTHOR]

Heat recovery devices can help ventilation systems conserve energy; However, they cause a pressure drop in the system, and hence the system incurs an electricity consumption. In the present paper, the maximum allowable pressure drop of the recovery units is achieved from thermodynamic and economic points of view. The novelty of the research is that, unlike the existing criterion which does not differ for different cities, the introduced criteria can be calculated for each city with individual weather, economic, power plants, and energy conditions. Although the methods presented to calculate the criteria consider a broad range of effective parameters, there is no need to use complex energy simulation methods. Moreover, for each city, four types of criteria are introduced, which allows the city authorities to choose the best ones according to their city conditions, and then calculate the chosen criteria by the data of the city. In order to gain the criteria, modified enthalpy-hours and bin method variables are introduced to calculate sensible and latent energy consumption due to ventilation. The criteria are numerically calculated for Kerman city in Iran, and the results show that the recovery type and the chosen viewpoint has a considerable effect on the allowable pressure drop. The sensitivity of the results to different variables is analyzed in the end. [ABSTRACT FROM AUTHOR]

Large scale smart meter deployments have resulted in popularization of sensor-based electricity forecasting which relies on historical sensor data to infer future energy consumption. Although those approaches have been very successful, they require significant quantities of historical data, often over extended periods of time, to train machine learning models and achieve accurate predictions. New buildings and buildings with newly installed meters have small historical datasets that are insufficient to create accurate predictions. Transfer learning methods have been proposed as a way to use cross-domain datasets to improve predictions. However, these methods do not consider the effects of seasonality within domains. Consequently, this paper proposes Hephaestus, a novel transfer learning method for cross-building energy forecasting based on time series multi-feature regression with seasonal and trend adjustment. This method enables energy prediction with merged data from similar buildings with different distributions and different seasonal profiles. Thus, it improves energy prediction accuracy for a new building with limited data by using datasets from other similar buildings. Hephaestus works in the pre- and post- processing phases and therefore can be used with any standard machine learning algorithm. The case study presented here demonstrates that the proposed approach can improve energy prediction for a school by 11.2% by using additional data from other schools. [ABSTRACT FROM AUTHOR]

This paper proposed a hybrid Logarithmic Mean Divisia Index (LMDI) model that decomposes the change of total energy consumption (TEC) in the building sector to three different dimensions: 1) key influencing effects that include area effect, structure effect, population intensity, value intensity, and energy intensity; 2) three phases that include material production, construction, and operation; and 3) geographically distributed regions. Then, the model was validated in China's building sector based on the data collected from 30 provinces during 2007–2015. The key findings show that: (1) the increased final energy consumption in the building industry accounted for 53.3% of that in China. (2) Building material production contributed the most (69.2%) to the energy changes, followed by building operation (29.6%) and construction (1.2%). (3) The change of TEC was attributed the most to the area effect (90.4%) and the energy intensity effect (42.4%). (4) Zhejiang, Jiangsu, Hubei, Hebei, Fujian and Shandong contributed the most (47%) to the increase of China's building energy use. These findings provide an insightful understanding of the building energy changes and also a quantifiable justification for making building-specific energy policies, which are discussed at the end of the study. [ABSTRACT FROM AUTHOR]

In this paper, a free running centrifugal fan inside a rectangular duct is investigated via measurements and CFD. A rectangular cuboid-shaped body is mounted downstream of the free running centrifugal fan, covering most of the space next to the fan wheel, leaving only a small channel for the air near the duct walls to pass through. We call this cube a ‘pressure regain unit’ (PRU). The cube increases the fan efficiency by reducing vortices downstream of the fan and tranfering a larger part of the kinetic energy into static pressure. Experiments are conducted with several PRU geometry variations. The measurements show that an increase in efficiency of 10% is possible compared to a free running fan in an empty duct. Subsequently, a numerical analysis is performed to analyze the effect which leads to efficiency increase. Comparing the installation with and without PRU shows that the main difference is a recirculation area behind the fan wheel when no PRU is installed. Higher level of turbulence and strong shear layers in the empty duct cause efficiency losses. The turbulence level and the amount of shear layers are reduced by the PRU. Both experimental and numerical data show an energy saving potential by optimizing the downstream region of the free running centrifugal fan. [ABSTRACT FROM AUTHOR]

With the world rapidly urbanizing, addressing the energy intensive urban built environment is becoming increasingly important. Cities across the United States and the world are turning to energy benchmarking as a means of understanding the relative energy efficiency of their building stock and identifying potential opportunities to reduce energy usage. Benchmarking utilizes building characteristics and energy use data to measure a building’s energy consumption against a performance baseline and derive a level of energy efficiency. Over twenty cities in the United States and many others across the world have passed laws mandating the collection and disclosure of energy use data to enable benchmarking and pinpoint potential energy saving opportunities. However, municipalities are struggling to convert this data into actionable insights and identify which buildings are prime candidates for energy efficiency interventions. Although an extensive body of work exists on benchmarking building energy performance, previous works are limited in their ability to leverage such emerging data streams and conduct analysis at the city scale. Moreover, previous methods are largely based on black-box models that limit the interpretability of results and in turn hinder the ability of policy-makers to employ such models in their policy design and decision-making processes. In this paper, we propose DUE-B, a new Data-driven Urban Energy Benchmarking methodology based on recursive partitioning and stochastic frontier analysis. To test DUE-B, we evaluate its performance using real energy and building data from over 10,000 buildings in New York City, and we compare the results to other common benchmarking models using the Kendall tau-b correlation coefficient. Results indicate that DUE-B is more robust than conventional benchmarking methods in respect to identifying subsets of efficient and inefficient buildings. Furthermore, we highlight how results from DUE-B can be utilized by municipal officials and other policy-makers to target inefficient buildings for energy efficiency interventions, incentives, and programs. Specifically, we indicate how DUE-B can be utilized by municipalities to target the most inefficient buildings for subsidized onsite energy audits and less inefficient buildings for less capital-intensive energy efficiency strategies such as incentives and educational programs. In the end, more robust benchmarking methods like DUE-B have the potential to enhance the efficacy of municipal energy efficiency programs and help transition cities to a more sustainable energy future. [ABSTRACT FROM AUTHOR]

This paper presents the results of the validation of an experimental method for studying the thermal benefits of green façades. The main aim is to evaluate the impact of plants occultation on energy performance of a vertical wall in the temperate climate of the city of Lille in northern France. An experimental platform consisting of three identical prototypes (three boxes as thermal labs) has been designed and instrumented. Each prototype has been highly insulated from each face except from the vertical southern wall which was realized with a poor thermal conductivity material. The following measurements were taken: ambient temperature (outside and inside thermal labs), external and internal surface temperatures of southern walls, heat fluxes transferred through south verticals walls (reference and vegetalized walls), solar incident radiation and wind velocity variation under instantaneous real climatic conditions. This enabled the analysis of the variation of energy balance according to the presence or not of a shading effect on the south vertical walls as well as their coverage rate. Several tests were performed on the three experimental thermal labs by imposing a leaf coverage rate which differs during each measurement. The results have shown that masking reduces the outside temperature and enhances the interior thermal comfort especially during summer. [ABSTRACT FROM AUTHOR]

Material characteristics of Ground Heat Exchangers (GHE) greatly influence the heat transfer efficiency of Ground Source Heat Pump (GSHP) system, i.e. polyethylene (PE) and steel for the current study. Most of the relevant studies applied numerical simulation or small-scale experiments, which either simplified or neglected the effects of different GHE design on the energy consumption during the operation process of GSHP system. This paper aimed to investigate the seasonal performance (winter and summer) for both PE and steel GSHP systems by using the methods of full-scale experiment monitoring (buried tubes with the depth of 100 m) and life cycle assessment (LCA), which includes energy benefits, economic and environmental impacts. Heat exchange performance was evaluated by heat exchange per tube depth (Q L ) and real-time monitoring of system power consumption. Energy benefits, economic and environmental impacts were further indicated through four indices including coefficient of performance (COP), energy payback time (EPT), cost payback time (CPT) and CO 2 reduction ratio (CRR). In general, we found the seasonal performance of steel system was superior to PE one in the current study. We also noticed that both PE and steel systems showed better performance in summer than winter. For both winter and summer, the annual averaging inlet and outlet temperature difference of steel tubes was 1.4–1.7 °C higher than PE one and the Q L of steel tubes increased by 70%. The energy consumption and cost of steel system were decreased by 43.6% and 24.6%, respectively in the whole life cycle compared to PE one. The EPT, CPT and the CRR of steel system (compared to PE system) were calculated as 0.24 year, 1.83 years and 0.45 accordingly. This work further shows future promise of steel GHE application for GSHP systems. [ABSTRACT FROM AUTHOR]

Accurately predicting energy usage in buildings is of great importance in various efforts on improving building energy efficiencies such as fault detection and diagnostics, building-grid interactions, and building commissioning. Data-driven approach and first-principle approach are two commonly used methods in developing models for predicting building energy use. In this paper, several data-driven methods including multiple linear regression, adaptive linear filter algorithms (least mean square (LMS), normalized least mean square (nLMS), and recursive least square (RLS)), and Gaussian mixture model regression (GMMR) are employed to predict hourly energy usages in two buildings. One building is a synthetic large-size office building from DOE reference building models. The hourly building energy consumption was predicted using the energy simulation model for one year under Chicago climate. The other building is an existing office building located in Des Moines, Iowa. The actual hourly building energy consumption of the existing building was obtained through building submeters. The accuracies of these data-driven models for predicting energy usages of the two buildings are compared. The GMMR models outperform the adaptive filter methods in this study. Both the GMMR and adaptive filter methods meet the model calibration criteria defined by the ASHRAE Guideline 14. [ABSTRACT FROM AUTHOR]

This paper presents test methods and results of assessment data necessary for determination of building energy consumption, based on measurements in an occupied typical single-family house in Poland. Comprehensive on-site measurements included the building envelope, heating system, domestic hot water system, ventilation system with heat recovery and indoor environment quality. A new wireless measurement system was used. The results obtained from the thermal diagnostics were used to analyze the energy consumption of the house in four different successive building modernization stages. The steps undertaken included improvement of wall and window insulation, installation of mechanical ventilation with heat recovery, improvement of house airtightness and replacement of the boiler and the hot water storage vessel with more efficient ones. Such steps are typical activities undertaken without a prior energy analysis by owners of single-family houses. Before thermal renovation the indicator of non-renewable primary energy was 243 kWh/(m 2 y) and after renovation this decreased to 147 kWh/(m 2 y). Proper selection of thermo-modernization measures would allow to reach the lower value of this indicator. However, it still would be higher than the required for newly built single-family houses in Poland. The execution of thermal diagnostics both before and after thermal renovation of the house enable the correct performance of that process and the achievement of greater energy savings. [ABSTRACT FROM AUTHOR]

Configuring the number and size of chillers in a multiple-chiller plant properly is an efficient way to improve the plant energy efficiency. At the design stage, the optimal configuration can be achieved through matching the capacity to load as closely as possible across the full-load profile. However, in spite of the fact that current literature offers practical recommendations, a systematic method to optimize the configuration of multiple-chiller plants is lacking. Due to the lack of accurate information at the design stage and only limited knowledge of the eventual realization it is hard to predict the building’s cooling load. Moreover, there is no operational data to predict the system performance. Both explain the existence of uncertainty in the HVAC plant design process. This paper, therefore, proposes a strategy to optimize the configuration of multiple-chiller plants, which takes account of the load side uncertainty as well as the COP uncertainty and selects the optimal configuration through a life-cycle analysis. Both the load side uncertainty and the COP uncertainty are quantified using statistical distributions. To facilitate applications, the distributions of the cooling load profile of different types of buildings under different weather conditions are investigated and are classified into four categories, and the optimal configuration schemes under each type of cooling load distribution are analyzed and summarized in a tabulated form. [ABSTRACT FROM AUTHOR]

The increased demand of energy in domestic applications necessitates the development of innovative engineering solutions in building heating, ventilating, and air conditioning (HVAC) systems. As the largest energy intensive sector is domestic buildings, more focus is currently directed to reduce air conditioning energy consumption. Double-pipe heat exchangers are considered one of the practical solutions in today’s HVAC industry. Nevertheless, a few studies focus on using double-pipe heat exchangers in air conditioning applications. This paper experimentally investigates the usage of double-pipe condenser and evaporator in an air conditioning system serving a 45 m 3 balanced calorimeter of 2.24 kW heat load. Deionized water (DIW) was used as the secondary heat transfer working fluid for both the evaporator and condenser units, and R-22 was used as the AC system refrigerant. Experimental results of the double-pipe heat evaporator/condenser setup showed a promising reduction in the compressor work and an increase in the system coefficient of performance (COP). The collected data showed that the system efficiency depends more on the evaporator DIW flowrate than on the condenser DIW flowrate. By increasing the DIW flowrate in the evaporator, the compressor work was shown to decrease, while the COP was shown to increase. In comparison with a standard rated air conditioning unit, using a double-pipe evaporator and condenser units with the maximum DIW flowrates resulted in a decrease of about 53% in the compressor work and a similar percentage of increase in the system COP. [ABSTRACT FROM AUTHOR]

The purpose of air-conditioning systems is to provide a suitable indoor environment with respect to temperature, humidity and fresh air. Liquid desiccant dehumidification constitutes an effective method for extracting moisture from humid air with less energy consumption. Current studies mainly focus on dehumidification system under certain heat and mass transfer ability (number of mass transfer units NTU m ). This paper will investigate NTU m of counter-flow packing tower and its influence on system performance. An experiment including a counter-flow packing tower was conducted. When the height of tower was 0.7 m, the volumetric mass transfer coefficient was between 1–4 kg/(m 3 ·s) and NTU m presented a range between 0.9–1.4, when air flow rate changes from 1 kg/(m 2 s) to 2.5 kg/(m 2 s). When the height changed to 2.1 m, the NTU m of the tower presented a variation from 3.3 to 4.3. Furthermore, a simulation model is developed, which is validated by experimental data and past research results. The influence of NTU m on total circulation flow path is mainly caused by heat and cold offset and an NTU m value approximately of 4 constitutes a suitable value of increasing heat and mass transfer ability. Comparing three typical flow paths, when NTU m is lower than 3.5, inter-stage circulation presents improved performance in system COP. When NTU m is large, total circulation is efficient. The system COP cross points of flow paths are caused by heat and cold offset and concentration difference of the solution circulation between the dehumidifier and the regenerator, which can be quantified by loss coefficient ε and χ respectively. The heat and mass transfer ability of the system should be considered in the designing process of flow path configuration to obtain an efficient performance for different range of NTU m . [ABSTRACT FROM AUTHOR]

A new photovoltaic loop heat pipe/solar assisted heat pump (PV-LHP/SAHP) water heating system was introduced in this paper. With the combination of solar energy, LHP and heat pump technology, the composite system could operate in the PV-LHP mode, the solar/air source heat pump (SASHP) mode and the air source heat pump (ASHP) mode. The mathematical model of the system was constructed to simulate operating performances in typical working conditions and in long-term run. The influence of main structure and operating parameters were also analyzed. To validate the accuracy of the built model, an outdoor test rig of the PV-LHP mode was established in Qinhuangdao City. Investigation results showed that the overall photothermal efficiency of the system is comparable to traditional photovoltaic/thermal (PV/T) water heating systems. The monthly average power consumption per liter hot water is 0.009 kW h/L, and the monthly average COP of heat pump modes is about 3.10. Besides, the annual solar heating ratio of the system is up to 57.8%. Compared with the traditional ASHP system, the life cycle cost of the PV-LHP/SAHP system could be reduced by 29.6%. [ABSTRACT FROM AUTHOR]

Addressing the large energy consumption of hotels requires an understanding of the factors that drive this consumption. This enquiry is crucial for South Africa which has experienced significant strain in meeting its domestic energy demand. This has occurred alongside increases in international tourists, adding to the pressure on already strained resources. This paper tests hypotheses on drivers of energy consumption in hotels using a novel panel dataset which presents daily consumption data for 22 hotels across South Africa. Findings from various specifications of the Dynamic Random Effects Model suggest that the number of rooms in a hotel, the services and facilities offered, and climatic conditions are strong drivers of consumption. While the role of occupancy could not be robustly ascertained due to severe data limitations, findings indicate that price regulation plays a significant role in curtailing electricity consumption, even in high-end hotels. Results further suggest that in the design of guidelines for energy efficiency in South African hotels, the energy consumption of the facilities and services offered should be the first point of call, and the strenuous impact of extreme weather conditions on energy consumption needs to be factored in at the phase of building design and construction. [ABSTRACT FROM AUTHOR]

With the increased complexity of air conditioning systems, the computational load and complexity of real-time optimization appear to be challenging for applications. To overcome the challenge, multiplexed optimization has been proposed in which multiple set-points are sequentially optimized and updated one by one. In such a way, it can achieve equivalently-good energy performance but with exponentially reduced computational load if compared with those traditional methods that optimize all set-points simultaneously. In fact, the time instant for each set-point optimization and the set-point optimization sequence have impacts on the overall performance of the multiplexed optimization. Considering such impacts, this paper proposes a new multiplexed optimization method for enhanced performance. In the proposed method, optimization is activated as the system energy efficiency is low. The set-point optimization sequence is selected according to their impacts on the system energy performance. The one with the most energy saving potential will be firstly optimized. The case study results demonstrate the improved performance of the proposed method in terms of energy, computation load and system stability when compared with the conventional multiplexed optimization. The proposed method can be applied in complex air conditioning systems for energy saving, computation load reduction and system stability improvement. [ABSTRACT FROM AUTHOR]

In the context of environmental policy, the EU has launched a series of initiatives aimed at increasing the use of energy efficiency, as it has pledged to reduce energy consumption by 20%, compared with projected levels of growth of CO 2 emissions into the atmosphere by 2020. In Greece CO 2 emission levels in the atmosphere have risen significantly over the past two decades [1] . For the year 2011, CO 2 emissions per person in Greece correspond to 7.56 t. According to the data, this increase in emissions is reflected to a 151.2% above from the levels of 1980 and a 756% increase from 1960 levels. The building sector consumes the largest amount of energy in Greece, therefore constitutes the most important source of CO 2 emissions. The energy upgrade of the building sector produces multiple benefits such as reduced energy consumption, which is consistent with the reduction of air pollution. Additionally, there is a significant improvement at the interior comfort conditions of the building, which promotes productivity and occupant health. Moreover, because of the large number of educational buildings in the country, the energy consumption of them present a significant amount of the country's total energy consumption and simultaneously has the effect of increasing the costs paid by the state budget for the operation and maintenance of public buildings. The investigation of alternative methods to reduce energy consumption in educational buildings is an important approach for sustainability and economic development of the country over time. The purpose of this paper is to study and evaluate the energy saving methods of a university building in Mediterranean climate with significant energy consumption. Additionally, through Building Information Modeling (BIM) and Computational Fluid Dynamics (CFD) software, studies considering the contribution of passive heating and cooling techniques were conducted, in order to minimize energy consumption in pursuit of desirable interior thermal comfort conditions. [ABSTRACT FROM AUTHOR]

Thermally activated building system (TABS) is a hydronic cooling system, in which cold water is circulated through pipes embedded in the building structure to remove the heat from the indoor space. This paper proposes a hybrid passive cooling system, in which TABS is integrated with a cooling tower. The performance of this integrated system greatly depends on the climatic conditions of the geographical area. Hence, the feasibility of the proposed system to provide thermal comfort in twelve Indian cities spread over five different climatic regions is investigated using COMSOL Multiphysics. Different cooling scenarios like Roof and Floor cooled TABS (RF) and All Surfaces cooled TABS (AS) are compared. The proposed system performs well in arid and semi-arid regions, and the RF scenario is sufficient to achieve thermal comfort. The system is able to reduce the indoor operative temperature by 14 °C for the arid climatic conditions of Jaipur. In the other three regions, i.e., humid subtropical, tropical wet and dry and tropical wet regions, the performance of the proposed system is relatively low and the AS scenario is required to achieve thermal comfort. [ABSTRACT FROM AUTHOR]

Airtightness testing is widely undertaken to assess the as-built performance of dwellings, in support of achieving energy and ventilation strategies. Mandatory schemes operate in some countries, such as the UK, to ensure that dwellings are built in accordance with their design air permeability. However, testing is only useful if the results give a true picture of the airtightness of the building. Previous literature has investigated factors which could influence airtightness test results but has not questioned data quality, despite the pressure on builders to achieve design targets. This paper presents air permeability results from the largest UK dataset, comprising 144,024 dwellings tested under the Air Tightness Testing and Measurement Association (ATTMA) scheme. The data show an unexpected distribution of test results with narrow peaks just within test targets. Such results were not expected theoretically but do reflect findings in other fields where performance-based targets are in place. Such a close match between design and tested airtightness may be achieved by remedial works taking place during the test rather than afterwards. Recommendations are made with respect to quality assurance systems, design guidance and on-site sealing practices to increase the likelihood of long-term airtight buildings being constructed first time. [ABSTRACT FROM AUTHOR]

District heating system brings with it the problem of achieving a fair billing service for heating of apartment buildings in the case of uneven temperature in them. The problem is most pronounced when the individual apartments are completely excluded from the heating system of the building. The model of an existing building with 24 apartments was simulated in the software EnergyPlus, for different characteristics of the thermal envelope and different conditions of use, when the apartment with the lowest energy consumption is excluded from the heating system. It was simulated the exclusion of this apartment in which people are present and the lights and electrical appliances are turned on and in which there is no presence of people and electricity consumption (the apartment is empty). As a measure of energy flow between unheated and adjacent apartments, the parameter “stolen” energy is defined. The results of the conducted simulations show that the stolen energy is reduced by the exterior and interior insulation of the building. In this paper, data on energy flow when there is interior insulation in apartments, in particular according to the current regulations in Serbia, are especially analyzed. The results show that, depending on the case and scenario, the stolen energy ranges from 27.23 to 7.97 kWh/m 2 . The insulation of interior walls of the building can significantly mitigate the problem of energy flow due to temperature differences between apartments. [ABSTRACT FROM AUTHOR]

This paper presents the results of a long-term, detailed electricity end-use analysis of hotel guest rooms in order to provide a better understanding of the electricity use patterns in guest rooms and their explanatory factors. To accomplish this, a one-year of sub-hourly electricity measurements for an entire floor and major electricity end uses of fourteen guest rooms in a case-study hotel in Washington, D.C. were analyzed along with coincident weather and occupancy data collection. A regression analysis was performed on the monthly and daily end-use electricity data to identify key variables affecting different electricity end uses of the guest rooms. As a result, it was found that a regression model with a single temperature variable did not fully describe the electricity use behaviors of hotel guest rooms. The addition of the occupancy variable significantly improved the model fit for both monthly and daily total floor electricity use and was statistically significant. A review of the electricity end uses showed strong correlations between outdoor temperature and the electricity end uses for space conditioning, especially the DOAS unit and the VRF outdoor unit. However, for other end uses such as plug loads, lighting, and the VRF indoor units, the observed correlations to outdoor temperature were not significant but were moderately correlated to occupancy. [ABSTRACT FROM AUTHOR]

The main purpose of this research is to suggest a virtual water flowmeter applicable in hydraulic systems with low-cost sensors. Practical procedures for realizing virtual water flowmeters for constant flow pumps (i.e., Cases 1 and 2) and variable flow pumps (i.e., Cases 3 and 4) are proposed in this paper. In Case 1, for measuring the water flow rate through a constant flow pump, the pump head was measured and the water flow rate was estimated using the pump characteristic curve obtained from the manufacturer or by deriving an empirical model based on the field measurement data. In Case 2, the water flow rate through a valve with a constant flow pump was estimated by measuring the pressure head between the valve and the valve opening position. The valve characteristic curve can be obtained by deriving an empirical model based on the initial field test results. The water flow rate through the variable flow pump was calculated, using the pump affinity laws and an empirical model, by measuring the frequency (i.e., Case 3) or input power (i.e., Case 4). The initial setup and test procedures for virtual flowmeters are also proposed in this research for practical use in the field. To verify the proposed procedures, experiments were conducted using constant and variable flow pumps in a pilot system. It was found that the virtual water flowmeters showed 2.9% to 6.7% of root mean square error from Case 1 to Case 4, compared with an ultrasonic water flowmeter, through long-term experimental verification. [ABSTRACT FROM AUTHOR]

In the case of highly efficient buildings the solar and internal gains have a higher impact on the energy balance than on classical constructions, with lower insulation. In this context, a model was developed which considers the three dimensional heat transfers through the walls. It simulates the transient behavior of rooms with the use of a refined spatial and temporal discretization and considers the projection of solar radiation through a window onto interior walls, referred to as sun patch. Validation of the model was carried out using experimental data from a low energy cell operating in a natural climate. Shorter sampling steps seem necessary to consider accurately the fluctuations of the weather data and the short dynamics of the systems such as the regulated heaters. In this paper, simulations with weather data at different time steps (1 min, 10 min and 1 h) are going to be analyzed. The impact of the contribution regarding the adaptive and variable time step of the differential equation solver will also be shown. Finally, the impact of the different time steps on the accuracy of the low energy cell’s temperatures and heating loads will be discussed. [ABSTRACT FROM AUTHOR]