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Various studies have assessed the energy performance alterations affected by the novel technology of Building-Integrated Photovoltaic in Double-Skin Facades (BIPV-DSF), while lighting performance tied to the BIPV-DSF has not received much attention. This paper provides numerical modelling to assess the effect of BIPV-DSF on both indoor visual condition and energy consumption for an office module under a typical climate in the United Kingdom. The proposed study was focused on the comparisons between a reference case (a DSF office module with both layers using clear double glazing) and a design case of the same office module with BIPV-DSF using semi-transparent Amorphous Silicon PV glazing. Results show a significant drop in maximum daylight illuminance of 73% by configuring the BIPV-DSF with reference to the regular DSF. It was also reported the resultant average and minimum daylight factors (0.65% and 0.00%) were not able to meet indoor visual comfort requirements for office environments. Furthermore, it was found that the use of BIPV-DSF resulted in a net increase of 8% in building energy consumption over the reference DSF. Therefore, it is concluded that in the present context the BIPV-DSF is not viable for a commercial installation under the UK's climate conditions. [ABSTRACT FROM AUTHOR]

This paper provides practical guidelines to the Bayesian calibration of building energy models using the probabilistic programming language Stan. While previous studies showed the applicability of the calibration method to building simulation, its practicality is still impeded by its complexity and the need to specify a whole range of information due to its Bayesian nature. We ease the reader into the practical application of Bayesian calibration to building energy models by providing the corresponding code and user guidelines with this paper. Using a case study, we demonstrate the application of Kennedy and O’Hagan’s (KOH) [1] Bayesian calibration framework to an EnergyPlus whole building energy model. The case study is used to analyze the sensitivity of the posterior distributions to the number of calibration parameters. The study also looks into the influence of prior specification on the resulting (1) posterior distributions; (2) calibrated predictions; and (3) model inadequacy that is revealed by a discrepancy between the observed data and the model predictions. Results from the case study suggest that over-parameterization can result in a significant loss of posterior precision. Additionally, using strong prior information for the calibration parameters may dominate any influence from the data leading to poor posterior inference of the calibration parameters. Lastly, this study shows that it may be misleading to assume that the posteriors of the calibration parameters are representative of their true values and their associated uncertainty simply because the calibrated predictions matches the measured output well. [ABSTRACT FROM AUTHOR]

Occupancy data is a critical input parameter for building energy simulation since it has a big impact on the precision and accuracy of building energy model performance. However, current approaches to get such data through the conventional occupancy detection technology require either implementation of a large-scale sensor network and/or sophisticated and time-consuming computational algorithms, which to some degree limits the application of the real-time occupancy data for building energy simulation. In the era of the mobile internet, the massive people position data, which is generated by smartphone users and stored on cloud servers, offers a potential to solve this important problem. Such mobile data source is precisely monitored, real-time updated, and accessible with affordable time and labor cost upon customer's agreements in some regions, and therefore could be one of the alternatives to traditional occupancy detection methods. This paper presents an investigation of whether and how the mobile-internet positioning data can benefit building energy simulation. This paper first summarizes the pros and cons of several mainstream occupancy detection methods. Then, the principle of the proposed mobile-internet-based occupancy detection method is introduced. The methodology of using such occupancy data for building energy simulation is developed. An energy performance model of a complex building in Shanghai with a whole building simulation software EnergyPlus is used as a pilot case study to demonstrate the effectiveness of the proposed methodology. A calibration is performed using the building automation system data and the mobile-internet-based occupancy data. The simulation results show that mobile-internet-based occupancy data can help improve the building model prediction accuracy. [ABSTRACT FROM AUTHOR]

The building sector offers huge potential for energy savings, which helps to achieve environmental objectives and social benefits. A good approach to determine both the energy consumption of new buildings and the energetic refurbishment of existing buildings is through thermal simulation. This paper studies how building energy simulation calibration can be improved using interpolated weather data to determine on-site meteorological parameters at the building location. The lack of precise meteorological data in the exact location of buildings means that data from nearby stations is generally used, not knowing how far the error spreads in the results of heating demands and loads. The novelty of this paper lies in the analysis of error propagation to the results of demands and loads of thermal simulation, as well as in the method used to reduce these errors by TPS interpolation. As an interesting conclusion, the average (CV(RMSE)) obtained in the simulation of the studied building, placed successively in each one of the 70 meteorological station locations, decreases from 74% when using the nearest neighborhood to each site to 26% using the TPS interpolation technique. The error in the building simulations is almost three times lower using the studied method. [ABSTRACT FROM AUTHOR]

Combining cost-optimal solutions to reach nearly zero energy buildings (NZEBs) in compliance with European policies is an ongoing challenge. Energy consumption can be reduced evaluating different configurations at the design stage and implementing the most appropriate solutions according to the building and the location. This paper develops a simulation-based optimization framework of cost-optimal choices and energy efficiency measures for new buildings. It combines energy and cost simulations using a sequential search technique to find the most effective combination of energy efficiency and renewable energy measures starting from a base configuration. The model is applied to a residential building prototype, taking into consideration hourly climatic data, construction methods, cost data and energy consumption. A cost database and a library of potential measures, related to envelope, appliances and systems, have been established and used within the optimization process. The potential impact of climate change on the estimated cooling loads has been included in the calculations. The paper shows the feasibility of European requirements for new NZEBs located in different cities. It describes how to best achieve the NZEB design at the lowest cost in 14 locations across Europe. Results highlight how the cost-optimal measures vary with climate and how in each location final selected options differ. Insulation and building tightness appear essential in colder climates, while efficient appliances and lighting are key measures in warmer locations. A key finding of the research is that a source energy reduction of 90% and beyond is feasible for new constructions in all locations. Results also show how efficient lighting and appliances considerably impact the building energy performance. The importance of integrating renewables and energy efficiency measures is confirmed as crucial to reach the NZEBs target. [ABSTRACT FROM AUTHOR]

At present, dynamic simulation and analysis on building thermal environment and energy consumption achieved by computer software become a reality. However, it is found that defined gaps exist between simulation and on-site measurement. To explore the gaps, this paper takes typical air-conditioned university dormitories located in Chongqing as a case study. Simultaneously, the gaps are explored with a DeST-h building energy simulation model and on-site measurement. Indicators including indoor temperature, relative humidity and air conditioning energy consumption under different setting temperature are simulated and measured. Then, a comparison between simulation results and measured data is explored as well as the cause of these gaps. This paper studies thermal environment and energy consumption by combining on-site measurement and numerical simulation, and the conclusion is that the gaps can from occupant behaviour and physical parameters of the indoor and outdoor environment, which provides a reference for thermal environment of the dormitory and building simulation. [ABSTRACT FROM AUTHOR]

Uncertainty Quantification (UQ) employing a Monte Carlo Sampling (MCS) method in a building simulation domain has been widely used to account for risks of predicted outputs for robust decision making. However, the stochastic approach for UQ problems requires significant computational burdens compared to the deterministic approach. This paper addresses two surrogate models (Gaussian Process Emulator (GPE) and Polynomial Chaos Expansion (PCE)) which together can be regarded as a meta-model of a Building Performance Simulation (BPS) tool with a high-fidelity model. In the paper, the developed GPE and PCE with different model structures were compared in terms of a prediction capability under different amount of training data and number of inputs. The aim of the comparative study is to identify the relative prediction abilities and model flexibility of GPE and PCE. It was found that the GPE and PCE produce high performance qualities having fast computation speed compared to the developed basis model if new inputs having identical inputs and probability ranges were used. In terms of two-sample Kolmogorov-Smirnov (K-S) hypothesis test, mean values of the minimum p-values of the GPE and PCE were 0.999 and 0.569, respectively, if the number of samplings are over 30 cases. Otherwise, the PCE shows significantly reduced performance quality than the GPE. [ABSTRACT FROM AUTHOR]

Energy-related occupant behavior in buildings is difficult to define and quantify, yet critical to our understanding of total building energy consumption. Part I of this two-part paper introduced the DNAS (Drivers, Needs, Actions and Systems) framework, to standardize the description of energy-related occupant behavior in buildings. Part II of this paper implements the DNAS framework into an XML (eXtensible Markup Language) schema, titled ‘occupant behavior XML’ (obXML). The obXML schema is used for the practical implementation of the DNAS framework into building simulation tools. The topology of the DNAS framework implemented in the obXML schema has a main root element OccupantBehavior , linking three main elements representing Buildings , Occupants and Behaviors . Using the schema structure, the actions of turning on an air conditioner and closing blinds provide two examples of how the schema standardizes these actions using XML. The obXML schema has inherent flexibility to represent numerous, diverse and complex types of occupant behaviors in buildings, and it can also be expanded to encompass new types of behaviors. The implementation of the DNAS framework into the obXML schema will facilitate the development of occupant information modeling (OIM) by providing interoperability between occupant behavior models and building energy modeling programs. [ABSTRACT FROM AUTHOR]

Among the various factors that affect building performance, occupant behavior (OB) has received more and more attention and has become a research hotspot in building design and operation in recent years. However, due to the remarkable randomness and diversity of OB in buildings, it is difficult to accurately describe its characteristics using a fixed schedule, which is a normal approach of most energy simulation tools. DeST, a whole building energy simulation tool offers the function of stochastic OB simulation so that the simulation results can be more in line with the actual situation. In the newly released DeST 3.0, the OB modules are developed based on the actual observed OB data and are divided into two parts: occupancy and occupant action modules. The occupancy module is based on the Markov chain, which simulates the occupant movements among the spaces inside and outside the buildings. The occupant action modules are proposed with an environment and event-driven stochastic infrastructure based on a three-parameter Weibull distribution, such as occupant shading, window, and air-conditioning (AC)/heating control behavior modules. This paper presents the OB modules development for coupled simulation in DeST 3.0 and discusses the effects of integrated simulation of occupant behavior on building energy consumption. Firstly, an overview of the current modelling efforts of OB is given, from the trend to the existing foundation. Secondly, the development of OB modules for coupled simulation in DeST 3.0 is introduced in-depth, including co-simulation of AC/heating control behavior, window behavior and shading behavior. Lastly, two case studies that realize coupled simulation using the developed modules are shown, one is the comparison between existing occupancy schedules from GB standards and the developed occupancy simulation, and the other is the comparison between the result of simulating with or without using AC/heating control behavior modules in two bedrooms of the residential building. [ABSTRACT FROM AUTHOR]

Abstract: Although absorption cooling has been available for many years, the technology has typically been viewed as a poorly performing alternative to vapour compression refrigeration. However, rising energy prices and the requirement to improve energy-efficiency is driving renewed interest in the technology, particularly within the context of combined cooling, heat and power systems (CCHP) for buildings. In order to understand the performance of absorption cooling, numerous models are available in the literature. The complexities involved in the thermodynamics of absorption chillers have, however, so far restricted researchers to creating steady-state or dynamic models reliant on data measurements of the internal chiller state, which require difficult-to-obtain, intrusive measurements. The pragmatic, yet fully dynamic model described in this paper is designed to be easily calibrated using data obtained from the measurements of inflows and outflows to a chiller, without resorting to intrusive measurements. The model comprises a series of linked control volumes featuring both performance maps and lumped mass volumes, which reflect the underlying physical structure of the device. The paper describes the modelling approach, theory and limitations, along with an example of the implementation of the model in the ESP-r code complete with calibration and an application to a specific example. [Copyright &y& Elsevier]

Highlights • Simulation environment for intelligent energy management in smart cities. • Fused urban energy simulator CitySim with machine learning library TensorFlow. • Applied deep reinforcement learning controller in two case studies. • Controllers first learn off-line from simple controller, then improve on-line. Abstract Buildings account for 35% of the global final energy demand. Efficiency improvements and advanced control strategies have a significant impact in the reduction of energy costs and CO 2 emissions. Building energy simulation is widely used to help planners, contractors, and building owners analyse diverse options regarding the planning and management of energy consumption in buildings. Furthermore, recent advances in data processing and computing have led to the development of sophisticated machine learning algorithms that can learn from large datasets, e.g., sensor data from buildings, and use them to develop building-specific adaptive and automatic energy controllers. Control algorithms, such as deep reinforcement learning can tune themselves, are model-free, and economical to implement. In this paper, we introduce an integrated simulation environment that combines CitySim, a fast building energy simulator, and TensorFlow, a platform for efficient implementation of advanced machine learning algorithms. The integration is achieved via Keras—an API for TensorFlow—and a set of text and csv files for data transfer between the applications. This new environment will allow researchers to investigate novel learning control algorithms, and demonstrate their robustness and potential for diverse applications in the built environment. We present two case studies for energy savings and demand response, respectively. [ABSTRACT FROM AUTHOR]

Abstract Numerous occupant behavior models, typically stochastic ones, have been built to quantify the effect of occupants on building energy simulation, which however are supposed to go through a rigorous validation, or evaluation process to claim that they are qualified to be used in the application. Current contributions relevant with occupant behavior modeling in literature have been resorting to statistical metrics rather than those orienting on their application, causing the disputation in interpreting the accuracy of the occupant behavior models when they are actually used in simulation. Furthermore, the stochasticity of occupant behavior models necessitates statistical evaluation of the model accuracy, instead of the comparison between actual data and a single-run simulated data. This paper proposes a methodological framework for model evaluation from an application-oriented perspective, with lighting behavior as a case study. The metrics for evaluating lighting behavior models are identified according to different application scenarios, while the comparison between the measurement and simulation is done by introducing the statistical hypothesis testing. Analogous analyses on other behaviors can be conducted in the future, which altogether would foster more rigor and application significance in occupant behavior model evaluation. [ABSTRACT FROM AUTHOR]

The paper explores the energy performances and environmental impacts of a prefabricated building module located in Messina (Sicily, Italy) through an approach that combines both the non-steady state building simulation and the Life Cycle Assessment methodology. The building uses renewable energy technologies and is usable in emergency situations or as simply temporary housing. Results show that the building module causes the emission of 1.5 t of CO 2eq /m 2 and consumes 29.2 GJ/m 2 of primary energy during its life cycle. The building achieves the Net Zero Energy Building target even if it has relevant environmental impacts in the materials production stage (72% on average of the total impacts while the use stage reaches the 23% on average). The construction and the end-of-life stages give a marginal contribution to the total impacts, since they account for the 1% and the 3%, respectively. [ABSTRACT FROM AUTHOR]

Buildings consume a significant amount of energy worldwide in maintaining comfort for occupants. Building energy management systems (BEMS) are employed to ensure that the energy consumed is used efficiently. However these systems often do not adequately perform in minimising energy use. This is due to a number of reasons, including poor configuration or a lack of information such as being able to anticipate changes in weather conditions. We are now at the stage that building behaviour can be simulated, whereby computer programs can be used to predict building conditions, and therefore enable buildings to use energy more efficiently, when integrated with BEMS (i.e. simulation assisted control [1]). In this paper we demonstrate a low cost BEMS that uses building simulation to predict optimised electrical heating startup control points. Those that use electricity for heating in Scotland, where this study was based, tend to be fuel-poor, hence there is a strong case for optimisation, particularly when electricity costs nearly three times as much as the equivalent unit of gas for heating applications. The proposed system demonstrated a 50% energy saving in the reduced heating time compared to scheduling when retrospectively evaluated. [ABSTRACT FROM AUTHOR]

Most building energy simulation (BES) tools need detailed inputs for the modeling process because of the nature that building is a complex system and evaluating its performance usually involves many factors and uncertainties. In this research, a lightweight building energy simulation tool – SimBldPy, is proposed. The proposed simplified hourly method in this paper includes additional thermal resistances of the zone internal floor and internal walls coupled with adjacent zone temperature. Two DOE reference building types (one residential and one office) located in Philadelphia and San Francisco are used in the research to verify the performance of the tool. Its performance and validity in simulating hourly energy use with different heating and cooling set points in each zone, under various climate conditions, and with multiple ECMs being applied to the building, have been tested and verified with EnergyPlus. This tool and modeling method could be used for rapid modeling and assessment of building energy for a variety of ECM options. By using this tool in modeling an on-campus building in University of Pennsylvania, it is shown that the calibrated building model in SimBldPy can be used to model the energy use of an existing building. With low cost in computation, the tool is able to provide rapid modeling and assessment of building energy use (BEU) for parametric studies. [ABSTRACT FROM AUTHOR]

Developing BEPS models which predict energy usage to a high degree of accuracy can be extremely time consuming. As a result, assumptions are often made regarding the input data required. Making these assumptions without introducing a significant amount of uncertainty to the model can be difficult, and requires experience. Even so, rules of thumb from one geographic region are not automatically transferrable to other regions. This paper develops a methodology which can be used to determine useful guidelines for defining the most influential input data for an accurate BEPS model. Differential sensitivity analysis is carried out on parametric data gathered from five archetype dwelling models. The sensitivity analysis results are used in order to form a guideline minimum set of accurately defined input data. Although the guidelines formed apply specifically to Irish residential dwellings, the methodology and processes used in defining the guidelines is highly repeatable. The guideline minimum data set was applied to practical examples in order to be validated. Existing buildings were modelled, and only the parameters within the minimum data set are accurately defined. All building models predict annual energy usage to within 10% of actual measured data, with seasonal energy profiles well-matching. [ABSTRACT FROM AUTHOR]

Building retrofits provide a large opportunity to significantly reduce energy consumption in the buildings sector. Traditional building retrofits focus on equipment upgrades, often at the end of equipment life or failure, and result in replacement with marginally improved similar technology and limited energy savings. The Integrated System (IS) retrofit approach enables much greater energy savings by leveraging interactive effects between end use systems, enabling downsized or lower energy technologies. This paper presents a case study in Hawaii quantifying the benefits of an IS retrofit approach compared to two traditional retrofit approaches: a Standard Practice of upgrading equipment to meet minimum code requirements, and an Improved Practice of upgrading equipment to a higher efficiency. The IS approach showed an energy savings of 84% over existing building energy use, much higher than the traditional approaches of 13% and 33%. The IS retrofit also demonstrated the greatest energy cost savings potential. While the degree of savings realized from the IS approach will vary by building and climate, these findings indicate that savings on the order of 50% and greater are not possible without an IS approach. It is therefore recommended that the IS approach be universally adopted to achieve deep energy savings. [ABSTRACT FROM AUTHOR]

With increased use of variable refrigerant flow (VRF) systems in the U.S. building sector, interests in capability and rationality of various building energy modeling tools to simulate VRF systems are rising. This paper presents the detailed procedures for model calibration of a VRF system with a dedicated outdoor air system (DOAS) by comparing to detailed measured data from an occupancy emulated small office building. The building energy model is first developed based on as-built drawings, and building and system characteristics available. The whole building energy modeling tool used for the study is U.S. DOE’s EnergyPlus version 8.1. The initial model is, then, calibrated with the hourly measured data from the target building and VRF-DOAS system. In a detailed calibration procedures of the VRF-DOAS, the original EnergyPlus source code is modified to enable the modeling of the specific VRF-DOAS installed in the building. After a proper calibration during cooling and heating seasons, the VRF-DOAS model can reasonably predict the performance of the actual VRF-DOAS system based on the criteria from ASHRAE Guideline 14-2014. The calibration results show that hourly CV-RMSE and NMBE would be 15.7% and 3.8%, respectively, which is deemed to be calibrated. The whole-building energy usage after calibration of the VRF-DOAS model is 1.9% (78.8 kWh) lower than that of the measurements during comparison period. [ABSTRACT FROM AUTHOR]

Sensitivity analysis has been widely used in building energy assessment to provide reliable analysis of importance ranking for input variables in buildings. Most of previous studies used one single batch simulation models to obtain sensitivity indicators. To conduct robust analysis, this paper implements a sequential sensitivity analysis to assess the convergence of sensitivity indicators for two global sampling-based sensitivity analysis methods (standardized regression coefficients and meta-modelling Sobol) in an office building. The results indicate that this sequential method can be more effective and robust. In contrast, a single batch sensitivity method for building energy assessment may lead to non-convergence results due to smaller number of simulation models or over-convergence results due to larger number of simulation models. It is also found that the convergence rate for standardized regression methods are slower than that for the meta-modelling Sobol method. [ABSTRACT FROM AUTHOR]

The role of occupants is important as building energy consumption is significantly attributed to their behaviour. Given the diverse activities within, and high levels of personal control over the indoor environment, occupants’ behaviour is one of the key uncertainties in predicting energy use in the residential sector. With an aim to better understand residential adaptive thermal comfort behaviours, longitudinal field observations were conducted with smartphone surveys and simultaneous temperature measurements in a sample of Australian homes (n = 42). This paper derives statistical models to enable predicting of the percentage of adaptive strategies in use (e.g. operation of air-conditioners, heaters, fans and windows), as a function of temperature. The analysis on our Sydney sample indicated that an outdoor temperature of 25 °C was the most favourable condition, inclusive of all seasons investigated throughout the 2-year monitoring period, maximising the use of natural ventilation and simultaneously minimising the householders’ dependence on their home air-conditioning system. This study also revealed personal and demographic characteristics that can have a significant impact on the householder’s decision to use their air-conditioning system. [ABSTRACT FROM AUTHOR]

Pitched-roof buildings make up a considerable proportion of architectural roof styles. Precise estimation of solar energy potential on pitched roofs is thus crucial to the sustainable development and renewable energy consumption of human habitats. Conventional solar radiation measurements usually adopt Light Detection and Ranging (LiDAR) data, which can only be extracted from existing buildings. There has been relatively little research assessing solar radiation on pitched roofs. This paper develops a pixel-based approach to estimation of solar energy potentials over pitched roofs based on the pretext of architectural design drawings. A typical Australian house with nine roofs is then chosen for implementation through a case study. The solar radiation on a certain cell of a shadow map is mathematically formulated for each pixel unit. Its yields over a certain time period are calculated by considering multiple instantaneous solar irradiances and visually presented through image processing. The resulting solar radiation maps, especially a coloured 3D map, reveal the roofs’ radiation distribution including effects from objects on the roofs such as chimneys. Radiation contour lines are mapped to obtain installation ranges for solar devices. This study will benefit commercial energy investors, residents and urban planners in the efficient use of renewable energy sources through accurate prediction of solar radiation potential and identification of areas receiving high radiation over sloping roofs. [ABSTRACT FROM AUTHOR]

Occupant behavior has significant impacts on building energy performance and occupant comfort. However, occupant behavior is not well understood and is often oversimplified in the building life cycle, due to its stochastic, diverse, complex, and interdisciplinary nature. The use of simplified methods or tools to quantify the impacts of occupant behavior in building performance simulations significantly contributes to performance gaps between simulated models and actual building energy consumption. Therefore, it is crucial to understand occupant behavior in a comprehensive way, integrating qualitative approaches and data- and model-driven quantitative approaches, and employing appropriate tools to guide the design and operation of low-energy residential and commercial buildings that integrate technological and human dimensions. This paper presents ten questions, highlighting some of the most important issues regarding concepts, applications, and methodologies in occupant behavior research. The proposed questions and answers aim to provide insights into occupant behavior for current and future researchers, designers, and policy makers, and most importantly, to inspire innovative research and applications to increase energy efficiency and reduce energy use in buildings. [ABSTRACT FROM AUTHOR]

The area of projected shadow on building facades may play a role of paramount importance in the prediction of solar energy conversion and building thermal and energy performance. Algorithms for solar shading calculations may directly affect the accuracy of results obtained by building energy simulation tools. In addition, the consideration of uncertain design parameters imply an estimative of a confidence interval of solar fraction results that determine the reliability as well as the improvement of shading calculation models. In this way, this paper presents a methodology for uncertainty and sensitivity analysis applied to the prediction of sunlit area on building exterior surfaces with shading devices. From a single building model, the solar fraction was investigated for two cities and four typical days. According to the obtained results, more than 20% of all simulation time steps have uncertainties equal or higher than 5% on the solar fraction calculation at 95% confidence level. The maximum values of the uncertainties occur in warmer months, which could significantly impact the building energy balance. Three inputs have higher influence on the uncertainties on solar fraction results: (1) latitude, (2) orientation and (3) width of overhang fins. Perturbation of only 5° on the building orientation may imply important uncertainties on solar fraction, which is normally neglected by designers. The results highlight the importance of assessing the uncertainty of inputs in algorithms such as the one to calculate the surface solar fraction that is an important prerequisite of the final building energy balance. [ABSTRACT FROM AUTHOR]

The paper describes a systematic framework that uses exergoeconomic theory integrated into ‘building energy retrofit’ (BER) design. An exergoeconomic module, based on the SPECO method, has been embedded into ‘EXRETOpt’, a recently developed retrofit-oriented exergy simulation tool based on EnergyPlus. Both active and passive technologies were analysed using two calibrated archetype non-domestic buildings as case studies (an office and a primary school). A novel cost-benefit indicator which accounts for building exergy destruction cost, retrofit annual capital cost, and project annual revenue is presented. This indicator is employed to account for best exergoeconomic performance technologies and to further develop deep BER packages. Compared to typical practice, exergoeconomics combined with cost-benefit provides a powerful tool for exploration and design improvement of building energy systems. In both cases, final product cost for heating and cooling processes were substantially reduced. In addition, the office case presented improvements in energy use by 67%, CO2 emissions by 53%, thermal comfort by 22%, exergy destructions by 42%, and the overall building exergy efficiency was improved from 14.8% to 20.0%. The school case presented similar results with an improvement of building exergy efficiency from 8.2% to 11.1%, and the potential to generate income due to current government incentives. [ABSTRACT FROM AUTHOR]

Building energy consumption and carbon emissions must be reduced to decrease the rate of global warming. To improve the energy efficiency of historic buildings, energy retrofitting can serve as an effective strategy. Retrofitting historic buildings is difficult due to the conflict between the historical value and usefulness of the building, but a modern scientific approach can help ensure that valuable historical records and artifacts remain protected and available. In addition, the retrofit of historic buildings can contribute, although a small part, to reducing carbon emissions nationally, and can have a symbolic meaning as a combination of history and modernity. This paper proposes a package of appropriate measures to improve energy efficiency, reduce the energy consumption of buildings, and preserve regularly used historic buildings for 100-year-old wooden truss and masonry composite structured historic buildings in Korea. The proposed measures are applied to most historic buildings in Korea built in the 1920s and have the potential to improve building performance. A new approach to applying energy technology to historic buildings is proposed. The energy application suitability analysis process was established by integrating three analysis techniques: the energy-saving effect analysis technique, historical value impact evaluation technique, and economic analysis technique. To achieve an appropriate retrofit plan, the condition of a building was evaluated through on-site diagnosis. Thermal transmittance rate assessments, temperature, and humidity monitoring, and infrared thermal imaging analyses were performed to evaluate the building's condition. Consequently, thermal transmittance rates for the wall and roof of the historic building were 1.18 W/m2K and 1.67 W/m2K, respectively, which indicated a risk of excessive energy consumption and condensation. Then, the suitability of each energy technology was evaluated by analyzing its economic feasibility and impact on the historical value and by simulating the reduction in energy consumption of the building. Thereafter, based on the analyses, the energy technologies deemed suitable were integrated to form an energy-efficient retrofit package, and an appropriate solution was elucidated. In conclusion, the package with the lowest impact on the historical value and the highest energy reduction decreased the total energy consumption by 15.9%. According to the results of this study, the process and package solutions proposed in this study are appropriate measures for historic buildings built in the 1920s in Korea, and can be a strategy with the potential to reduce carbon emissions and energy. • A retrofit plan for a historic building in the 1920s in Korea was studied. • Retrofit packages were applied to reduce energy consumption. • A suitability assessment integrating three analysis methods has been proposed. • The appropriate retrofit scenario was derived in consideration of the historical value. • Applying the optimal retrofit scenario could realize an energy saving of 15.9%. [ABSTRACT FROM AUTHOR]

Variable refrigerant flow (VRF) systems vary the refrigerant flow to meet the dynamic zone thermal loads, leading to more efficient operations than other system types. This paper introduces a new model that simulates the energy performance of VRF systems in the heat pump (HP) operation mode. Compared with the current VRF-HP models implemented in EnergyPlus, the new VRF system model has more component models based on physics and thus has significant innovations in: (1) enabling advanced controls, including variable evaporating and condensing temperatures in the indoor and outdoor units, and variable fan speeds based on the temperature and zone load in the indoor units, (2) adding a detailed refrigerant pipe heat loss calculation using refrigerant flow rate, operational conditions, pipe length, and pipe insulation materials, (3) improving accuracy of simulation especially in partial load conditions, and (4) improving the usability of the model by significantly reducing the number of user input performance curves. The VRF-HP model is implemented in EnergyPlus and validated with measured data from field tests. Results show that the new VRF-HP model provides more accurate estimate of the VRF-HP system performance, which is key to determining code compliance credits as well as utilities incentive for VRF technologies. [ABSTRACT FROM AUTHOR]

Traditionally, in building energy modeling (BEM) programs, occupant behavior (OB) inputs are deterministic and less indicative of real world scenarios, contributing to discrepancies between simulated and actual energy use in buildings. This paper presents a new OB modeling tool, with an occupant behavior functional mock-up unit (obFMU) that enables co-simulation with BEM programs implementing functional mock-up interface (FMI). The components detailed in the development of the obFMU include an overview of the DNAS (drivers-needs-actions-systems) ontology and the occupant behavior eXtensible Markup Language (obXML) schema, in addition to details on the creation of the obFMU that contains the co-simulation interface, the data model and solvers. To demonstrate functionality of the tool, three examples of occupant behaviors were simulated, including: (1) turning on and off lights, (2) opening and closing windows, and (3) turning on and off the air conditioners. The obFMU can be used via co-simulation with all building simulation programs that implement the FMI, thus users are not limited to a particular tool. Another advantage is the use of obXML schema to represent occupant behavior, standardize the description of occupant behavior enabling information exchange. [ABSTRACT FROM AUTHOR]

Occupant behavior is now widely recognized as a major contributing factor to uncertainty of building performance. While a surge of research on the topic has occurred over the past four decades, and particularly the past few years, there are many gaps in knowledge and limitations to current methodologies. This paper outlines the state-of-the-art research, current obstacles and future needs and directions for the following four-step iterative process: (1) occupant monitoring and data collection, (2) model development, (3) model evaluation, and (4) model implementation into building simulation tools. Major themes include the need for greater rigor in experimental methodologies; detailed, honest, and candid reporting of methods and results; and development of an efficient means to implement occupant behavior models and integrate them into building energy modeling programs. [ABSTRACT FROM AUTHOR]

The currently developing concept of a Net Zero Energy Building introduces new challenges and research problems. The calculation of a net zero energy balance is heavily influenced by the energy carrier weighting factors that are chosen, which can deeply influence the future energy market towards adopting specific energy technologies. The following paper proposes an analysis of different definitions and conventions for Net Zero Energy Buildings that employ different calculation methodologies and apply different weighting factors to an Italian case study. The case study, which is called “the Leaf House”, is one of the first examples of a nearly net zero energy building in Italy. A building simulation and model calibration were performed using monitored data. Energy balances were calculated for the case study. Scenarios for a building redesign were then proposed, with the aim of reaching an electricity target of net zero energy. Reaching a primary energy building balance of net zero is easier when using symmetric weighting because it allows renewable energy to account for avoided national mix energy generation. The results show that symmetrical balances are nearly fulfilled for the existing building and asymmetrical ones are below zero. In the redesign scenarios, the symmetrical weighting scenarios exceed the zero target by 34.10 and 20.83 MWh/year. The asymmetrically weighted balances show a variable trend in that auto-consumption and high load-matching are highly favourable because the theoretical ‘zero auto-consumption’ scenario would yield the worst result and would be the only one below the Net Zero threshold. Although symmetric weighting approaches could be viewed as robust physical approaches for renewables (e.g., the primary energy conversion factors for PV are usually close to 1), they can become a heavy burden during the market development of renewable energy technologies, highly favouring auto-consumption and energy storage to minimise energy import from the grid. [ABSTRACT FROM AUTHOR]

The paper presents the results of the analysis of the impact of various ventilation systems on the energy consumption performed for one Polish museum building that was built in 1929–1930. Simulations were carried out with the use of two computer codes: CONTAM and ESP-r. Multi-zone models including the exhibition rooms and the staircase were prepared. The simulations were made of synthetic weather data for one of the Polish towns for two months of the heating season. Twenty-four hour variability of internal heat gains was taken into account. The results show clearly that the natural ventilation system (which is currently used in the building) enables the air exchange with fresh air on the first floor only. The air infiltration on the upper levels is close to zero. Rebuilding the ventilation system generates changes in the energy demand of the building. It is presented how the heat demand increases with the increase of the ventilation air flow and what is the impact of the air infiltration on the heat demand for different variants of ventilation. [ABSTRACT FROM AUTHOR]

The paper presents the results of 1-year measurement of indoor environment parameters (temperature, relative humidity, CO 2 concentration) in exhibition rooms of one Polish museum in various periods of the year, and the results of simulation on thermal model of this building. The influence of a temperature control scheme in museum rooms on heating and cooling demand was analysed. The actual state (poor control of internal temperature) and ideal control of internal temperature were analysed – two cases in the winter and two cases in the summer. Simulations were performed by ESP-r program. The multi-zone model of the building was validated based on measurement data. Measurements and simulations revealed significant variations in the temperature in the rooms of the museum. The analysis showed that the maintaining internal temperature at constant level of 20 °C in winter reduces monthly heat demand in most of the exhibition rooms. Obtained effect is 5–16% depending on the premises. Provision the raise of a temperature by 1 K results in the increase of heat consumption by 6%. Maintenance of a desired internal temperature, which does not exceed 24 °C in the summer, requires the provision of coolness to the museum premises. [ABSTRACT FROM AUTHOR]

Urban heat island effects are already known for decades to result in increased urban outdoor temperatures as compared to the surrounding countryside. At the same time, recent years have witnessed growing concern about climate change's impact upon office buildings' performance in regard to indoor thermal comfort and the energy consumption needed to safeguard this comfort. Thus, it has to be expected that buildings in urban areas are especially effected by increased outdoor temperatures and the effects these may cause for indoor thermal comfort. A vicious circle of raising outdoor temperatures and consequently increasing CO 2 emissions associated with raising energy demands for cooling during summer heat waves is anticipated in this respect. This paper builds upon regionally downscaled weather data from future climate scenarios and applies these to dynamic thermal simulation of four sample office buildings in Vienna, Austria, at urban locations ranging from central business district to green outskirts of the city. Values of both heating and cooling demands under current and future conditions are calculated: while heating demands slightly diminish, cooling requirements generally rise significantly. Distinct differences in energy performance of buildings from different periods of construction can be observed. The impact of location within the city is considerable. [ABSTRACT FROM AUTHOR]

With the pressing regulatory requirement to increase energy efficiency in our built environment, significant researching efforts have been recently directed towards energy optimisation with the overall objective of reducing energy consumption. Energy simulation and optimisation identify a class of applications that demand high performance processing power in order to be realised within a feasible time-frame. The problem becomes increasingly complex when undertaking such energy simulation and optimisation in large scale buildings such as sport facilities where the generation of optimal set points can be timing inefficient. In this paper we present how a modular based optimisation system can be efficiently used for running energy simulation and optimisation in order to fulfil a number of energy related objectives. The solution can address the variability in building dynamics and provide support for building managers in implementing energy efficient optimisation plans. We present the optimisation system that has been implemented based on energy saving specifications from EU FP7 project - SportE2 (Energy Efficiency for Sport Facilities) and evaluate the efficiency of the system over a number of relevant use-case scenarios. [ABSTRACT FROM AUTHOR]

Abstract: This paper aims to validate and improve three cross and single-sided natural ventilation models implemented in a whole-building hygrothermal and energy simulation program. The tested models are the British Standard for cross ventilation, the de Gids and Phaff's and Larsen's for single-sided ventilation. Airflow rates obtained by those models have been compared to the measurements performed in two full-scale buildings: one single room house located in a wind tunnel facility and one real three-storey building. Results show a large variation of airflow rates provided by the different models. The Larsen's model can be improved if coupled to the CPCALC algorithm, providing better results for both wind tunnel and on-site experiments. [Copyright &y& Elsevier]

Abstract: Passive Houses have gained popularity the last 10years as a way of improving the energy efficiency in the housing stock. The combination of a highly insulating building envelope and large solar heat gains summertime can, however, result in excessive temperatures. The aim of this paper is to evaluate a comfort cooling strategy for attaining good indoor climate summertime while maintaining good energy efficiency. The studied strategy is a night cool storage with phase change material (PCM). To evaluate this, a Matlab code was used for the analysis of climate files along with the thermodynamical properties of PCM storages and then used with IDA Indoor Climate and Energy for building simulations. The building model is based on an actual Passive House in the district of Lambohov in Linköping, Sweden, where continuous logging of temperatures are available. Without comfort cooling the simulations show excessive temperatures summertime, which is consistent with the field measurements from the actual house. The results show that the PCM can remove a substantial share of the degree hours with excessive temperatures. [Copyright &y& Elsevier]

• Global Forecast System (GFS) estimations outperform data from nearest weather station. • Kriging of GFS results performance is second only to kriging of weather stations. • The nature of the GFS grid sets an upper limit to nearest neighbour errors. • GFS is a viable weather data source for thermal heating building simulation. Thermal simulations are a commonly used tool for energy efficiency analysis of buildings. Regional meteorological station networks are a prime source of weather data inputs, required for building thermal simulations. However, local measurements from weather stations are not always available, and when they are, accessing these data can be expensive. This paper analyses a novel use of a numerical weather prediction mesoscale model, the Global Forecast System (GFS) sflux model, as a source of input data for transient thermal simulations. Two interpolation techniques (nearest neighbour and universal kriging) were used to generate local weather datasets from GFS outputs at 27 locations spread over an area of 29,574 km2 in Galicia (northwest Spain). The performance of the GFS estimations was tested against weather measurements obtained from a governmental weather agency. A reference building with the most common features was selected for running thermal simulations in the TRNSYS environment, focusing on heating demand, with estimated weather data as input. The results highlight that GFS-interpolated datasets consistently performs better than using measured data from the nearest weather station. This clearly supports the use of GFS as an appropriate weather source for building simulations, providing good-quality, free and global-scale local weather inputs. [ABSTRACT FROM AUTHOR]