Search

This paper introduces a digital simulation workflow that calculates annual eye-level illuminance and energy demand from daylight and electric lighting sources for an array of view positions across 9-channels of spectral data. This Radiance-based workflow combines parts of the LARK spectral lighting code to offer better spectral resolution and accuracy when computing melanopic lx to evaluate the non-visual health potential for view positions within a digital model. The authors have implemented a series of annual climate-driven simulations and post-processed time-series of resulting data using the R statistical analysis software to compute Equivalent Melanopic Lux (EML) and energy demand (kWh) for an array of view positions and view directions in a digital model. This allows us to compute the energy demand of a given lighting and shading control scenario, which is optimized by hourly daylight availability to meet a recommended level of melanopic lx. This combined workflow uses digital models of an interior space to evaluate the annual potential of non-visual light exposure under different architectural, lighting, and shading control scenarios. This paper demonstrate the utility of this workflow by evaluating a luminaire-level lighting controls (LLLC) system to meet eye-level light exposure recommendations from the WELL Building Institute Feature L03 Circadian Lighting Design and compare the energy demand between targeted melanopic lux thresholds and shading control scenarios. [ABSTRACT FROM AUTHOR]

The use of Phase Change Materials (PCM) in different building applications is a hot topic in today's R&D activities. Numerical simulations of PCM-based components are often used both for research activities and as a design tool, although present-day codes for building performance simulation (BPS) present some shortcomings that limit their reliability. One of these limitations is the limited possibility of replicating the effects given by thermal hysteresis – a characteristic of several PCMs. In this paper, an original algorithm that allows hysteresis effects to be accounted for is described and the results from simulations are compared against experimental data. The algorithm is implemented in EnergyPlus™ and makes use of the Energy Management System (EMS) group, one of the high-level control methods available in EnergyPlus™. The algorithm enables the replication of PCM's different heating/cooling enthalpy curves in this BPS tool, which just recently was equipped with an integrated module for the replication of the effects of thermal hysteresis. A comparison between numerical results from the proposed model and from other methods implemented in BPS tools, and the experimental data provided, shows the impact of the algorithm in the simulation of heat transfer in a PCM layer intent for opaque walls. In general, it is shown that the proposed method presents a better agreement with experimental data than alternative modelling approaches, but it is also seen that all the tested numerical models are not fully able to replicating the behaviour of PCM layers if the PCM does not melt or resolidify completely (i.e. it remains in the phase change) during the charge/discharge cycle. A local sensitivity analysis complements this study and highlights the most relevant parameters that influence the results of a simulation carried out with the proposed model. Finally, the paper hypothesises that the general discrepancy between simulations and experimental data seen in the case of incomplete melting of the PCM layer can be due to unsuitable data for the thermophysical behaviour of the PCM, which are obtained through conventional characterisation procedures far from the physics of the PCM layer when in real building structures. [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]

Users’ behaviour and indoor climate are two leading aspects that must be taken into account if we want the retrofitting of the housing stock to contribute to CO 2 reduction, comfort improvement and reduction of living costs. The integrated facade module evaluated in this paper, which constitutes an approach to zero energy renovation, includes a preliminary study for the identification of target occupants and their characteristics and requirements that will guide the design decisions. The proposed strategy primarily focuses on the case of social rental multi-family housing stock in the Netherlands, but should provide insights in the application of the concept in Europe. This paper presents the analysis of the adaptability of this solution to the Mediterranean climate, taking into account the specific characteristics of the occupants of this climatic zone. The results showed an improved performance of building after the application of the evaluated solution in southern Spain, but with lower savings on the energy demand than in the Netherlands, so the economic investment should be reduced in this case. Also, the inclusion in the solution of some variables, such as the forced night-time ventilation for passive cooling and the insulation thickness reduction, were tested and proved to be an optimisation of its performance in the Mediterranean climate. Overall, the study concluded that the proposed refurbishment strategy has the potential to be implemented in different climates, particularly if certain modification in the facade operation is considered. [ABSTRACT FROM AUTHOR]

Empirical validation and code-to-code comparison of more than one building simulation tool has seldom been done in literature. Therefore this paper presents the first detailed comparison of 1D building energy simulation tools with 3D CFD simulation and measurement data from a test bed (Test-Box), exposed to natural environmental conditions. Dymola (library: IDEAS), EnergyPlus, IDA ICE and TRNSYS –four commonly used tools– were selected for comparison against highly accurate real measurement data and dynamic CFD results. This paper evaluates the performance of mentioned above 1D simulation tools and in particular their implemented Thermally Activated Building Systems (TABS) models. The mean error, RMSE, standard deviation and maximal error for the room air, the ceiling surface and concrete temperature of the Test-Box model results are acceptable for all tools. The time course of the room air temperature follows the measurement data relatively well for all four tools. However, the negative bias (Dymola -0.92 K, EnergyPlus -2.18 K, IDA ICE -0.37 K and TRNSYS -1.13 K) indicates a general overestimation for all tools. Furthermore, the paper reflects the properties of simulation tools regarding the modelling language, GUIs, co-simulation capabilities and their recommended field of application. [ABSTRACT FROM AUTHOR]

This paper presents a method to evaluate the thermal impact of complex fenestration systems on the energy balance of a building zone based on directional solar heat gain coefficients (DSHGC) and bi-directional scattering distribution functions (BSDF). The proposed methodology is presented as an alternative to layer-by-layer heat transfer models. An example of a layer-by-layer heat transfer model is the ISO15099 as implemented in the EnergyPlus building simulation program. The ISO15099 model relies on a number of assumptions that are not justified in the case of three-dimensional structures, air-permeable layers and deviations from ideal geometries, which are common features in commercial daylighting and solar-control systems. In this paper, the proposed methodology is implemented in the Fener simulation engine and compared with EnergyPlus for systems for which the assumptions of the ISO15099 model are valid. Then, the Fener program is used to simulate the thermal behaviour of a zone with a fenestration system that contains sawtooth-shaped retro-reflecting interior blinds, which cannot be directly modelled with the ISO15099 model. The impact of different control strategies on the thermal performance of the zone is demonstrated for this case study. [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]

The buildings sector is responsible for 37 % of global final energy consumption and nearly 40 % of total direct and indirect CO 2 emissions. This has led to promoting renovation efforts to decrease operational emissions of the existing building stock. With decreasing operational emissions, embodied emissions are becoming more important. In new buildings, embodied emissions account for about half of total emissions through the life cycle of a building. If both embodied and operational emissions are considered, renovation often outperforms new constructions, due to high embodied emissions of the structure - especially solid construction with a basement. Based on this, the decision if a building should be renovated or replaced is often not straight forward and depends on various factors. In this paper, a typical German building was considered as a case study, for which both renovation and replacement scenarios were considered, to identify the optimal solutions in terms of overall energy consumption and CO 2 emissions of the building in the use phase, the environmental impacts of the building along its entire life cycle, and related costs. Results show that the lowest CO 2 emissions during the lifetime of the analyzed building can be achieved with a sustainable replacement building (-2.05 kg CO 2 /m2/year) by using a heat pump with ground collector coupled with PV. This allows, compared to the existing reference building, reductions of 97 % and 101 % in terms of energy consumption and CO 2 emissions, respectively; while natural gas-based technologies are the least targeted and the most volatile to fuels' prices changes over the years. [ABSTRACT FROM AUTHOR]

Computer simulation for buildings has become affordable and possible in both research and industry during the last decades with rapid progress of computer industry as well as fundamental advancement of computational techniques. This paper provides an overview of such advancements achieved between 1987 and 2014, during which Professor Branko Todorovich served as Editor in Chief of Energy and Buildings Journal. The review, however, was not restricted to this journal only, in order to provide a fair snapshot on overall technique advances. Simulation and computational techniques cover a variety of aspects of building. This paper focuses specifically on six different topics including ventilation performance prediction, whole building energy and thermal load simulation, lighting and daylighting modeling, building information modeling, indoor acoustic simulation, and life cycle analysis of buildings. Major advances in each area are highlighted, as well as the trends for development and application. [ABSTRACT FROM AUTHOR]

To facilitate the integration of buildings to the power grid, battery-equivalent models have been proposed to characterize and quantify aggregate flexibility from building loads. An analytical method has been proposed to construct such a flexibility model for thermostatically controlled loads. Since this analytical method is derived from a simplified representation of building thermal dynamics, it is necessary to evaluate the resulting flexibility model in a more realistic context. This paper focuses on the validation of this analytical method for estimating aggregate flexibility from residential air conditioning (ACs) systems. A high-fidelity model is first developed to mimic thermal behaviors of a residential AC system through a co-simulation between Modelica and EnergyPlus. A population of virtual residential AC systems is then generated by randomizing parameters of the high-fidelity model. Finally, an aggregate flexibility model is constructed for the residential AC population using the analytical method and validated using results from simulation of the high-fidelity model. It is found that the flexibility estimated using the analytical method is with reasonable accuracy, but can be improved by explicitly incorporating heat gains into the method. [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: In this paper the energy and exergy performance of a social dwelling of a multi-family building from the 1960s in Bilbao (Spain) is presented and various improved energy concepts based on exergy principles are proposed and investigated. The aim of this paper is to explore and demonstrate the usefulness of the exergy approach in the assessment and development of an energy system for the dwelling under consideration. The total energy supply system is analysed, including the demand (space heating, domestic hot water and electricity), the system components (for conversion, storage and distribution) and the energy input from energy resources (primary energy and renewable resources). The study includes a comparison of the primary energy input of all cases considered and an analysis of the energy and exergy losses of each system component. The study has shown that the exergy analysis reveals thermodynamic losses that are not revealed using energy analysis and secondly, that the development of an improved energy system based on exergy principles has resulted in a significantly reduced primary energy input compared to the reference situation. [Copyright &y& Elsevier]

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]

The paper aims to achieve the modelling of climate change effects on heating and cooling in the building sector, through the use of the available Intergovernmental Panel on Climate Change forecasted data. Data from several different climate models will be fused with regards to mean air temperature, wind speed and horizontal solar radiation. Several climatic models data were analysed ranging from January 2006 to December 2100. Rather than considering each model in isolation, we propose a data fusion approach for providing a robust combined model for morphing an existing weather data file. The final aim is simulating future energy use for heating and cooling of a reference building as a consequence of the expected climate changes. We compare results, in terms of robustness to overfitting, for two different fusion methodologies, based on the comparison between errors on punctual historical data or prediction models that can be obtained by each climate simulator and by the actual ERA-INTERIM data set. Finally, we map the new aggregated data into a prediction trace of heating and cooling energy requirements. The expected energy demand is in the range of the one provided by single climate models, with a variability that reaches up to the 10% of the overall energy requirements. The approach proposed is an advancement as it allows to achieve better fits with existing re-analysis data if compared to specific global circulation models output data. Thus a more reliable estimation of energy use for heating and cooling can be achieved. [ABSTRACT FROM AUTHOR]

This paper presents the development of a self-tuned HVAC controller that provides customized thermal conditions to satisfy occupant preferences (i.e., online learning) while minimizing energy consumption, and the implementation of this controller in a real occupied office space. The evolution of personalized thermal preference models and the delivery of thermal conditions with model predictive control (MPC) form a closed-loop. To integrate these two parts, we propose a new method that always provides a set of lower and upper indoor temperature bounds. Different from ad hoc rules proposed in previous research, the control bounds are based on a decision-making method that minimizes the expected cost. We implemented the self-tuned controller in an actual open-plan office space conditioned with a radiant floor cooling system with eight independently controlled loops. Localized operative temperature bounds in each radiant floor loop were determined based on occupants' feedback and personalized thermal preference models, developed using a Bayesian clustering and online classification algorithm. The self-tuned controller can decrease occupant dissatisfaction compared to a baseline MPC controller, tuned based on general comfort bounds. To generalize the findings of this work: (i) we integrated the self-tuned controller with local MPC into a building simulation platform using synthetic occupant profiles, and (ii) demonstrated a method for automatic system adjustment based on comfort-energy trade-off tuning. In this way, decisions resulting in energy waste or occupant dissatisfaction are eliminated, i.e., the energy is deployed where it is actually needed. [ABSTRACT FROM AUTHOR]

Abstract: This paper describes a new general method for building simulation programs which is intended to be used for the modelling of complex facades. The term ‘complex facades’ is used to designate facades with venetian blinds, prismatic layers, light re-directing surfaces, etc. In all these cases, the facade properties have a complex angular dependence. In addition to this, such facades very often have non-airtight layers and/or imperfect components (e.g. non-ideal sharp edges, non-flat surfaces, …). Therefore building planners often had to neglect some of the innovative features and to use ‘work-arounds’ in order to approximate the properties of complex facades in building simulation programs. A well-defined methodology for these cases was missing. This paper presents such a general methodology. The main advantage of the new method is that it only uses measureable quantities of the transparent or translucent part of the facade as a whole. This is the main difference in comparison with state of the art modelling based on the characteristics of the individual subcomponents, which is often impossible due to non-existing heat- and/or light-transfer models within the complex facade. It is shown that the new method can significantly increase the accuracy of heating/cooling loads and room temperatures. [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]

Abstract: A building is permanently in thermodynamic non-equilibrium due to changing weather, free gains and indoor temperature set-point. Load calculation in dynamic conditions is an essential goal of building energy simulation. This paper demonstrates that the load calculation is a control problem. Supposing that the thermal model of the building is linear and that the model of the building, the weather conditions and occupational program are known in the design stage, the paper proposes an unconstrained optimal control algorithm which uses feed-forward to compensate the weather conditions and model predictive programming (MPP) for set-point tracking. MPP is obtained by modifying the dynamic matrix control (DMC), a variant of model predictive control (MPC). The peak load depends on the set-back time of the indoor temperature: smaller the set-back time, larger the peak load, but smaller energy consumption. Then, the choice of the weighting coefficients in the model predictive programming may be done on economical considerations. [Copyright &y& Elsevier]

Abstract: To maintain ultra-low particle concentrations, cleanrooms can require several hundred air changes per hour. These ventilation rates make cleanrooms 30–50 times more energy intensive than the average U.S. commercial building. There are an estimated 12millionm2 of cleanroom space in the U.S., consuming over 370PJ of energy each year. This paper explores opportunities to improve the energy efficiency of cleanrooms while maintaining or improving operating conditions. This paper documents the modeling of a 1600m2 cleanroom in upstate New York. The TRNSYS model includes TMY2 weather data; building geometry and material properties; empirical data on occupancy, lighting and process equipment; and sophisticated HVAC systems. The model was validated based on metered steam, chilled water and electricity usage. Under 8% error was achieved in all fields. Four strategies were simulated: a heat recovery system for exhaust air, resulting in an 11.4% energy reduction with a 2.7-year simple payback; solar preheating of desiccant dehumidifier regeneration air (2.4% energy reduction, 11.5-year payback); improved lighting controls (0.3% energy reduction, 1.5-year payback); and demand-controlled filtration (4.4% energy reduction, 3.1-year payback). Implementation of recommended strategies is predicted to save 9TJ, 862tonnes of CO2, and $164k annually. [Copyright &y& Elsevier]

Abstract: The efficiency of heating systems is increasingly important because of the increasing need to save energy and protect the environment. This paper presents an innovative technique for modeling multi-zone hydronic heating systems in buildings in an easier and more user friendly manner. An original approach is described in which the thermal and hydronic systems are solved simultaneously by simulating the dynamic behavior of pipe water flow rates, water temperatures, and room air temperatures. The simulation uses mathematical models that apply a Newton–Raphson method (NRM) developed in Matlab© and Simulink© environments. A user friendly graphical user interface (GUI) enables the designer to analyze any multi-zone hydronic heating system from the point of view of improving its efficiency. A laboratory heating system was used to validate this modeling technique. A series of trials were conducted in a multi-zone laboratory heating system located within the R&D division of a leading Italian boiler manufacturer, and the results presented in this paper validate the model. [Copyright &y& Elsevier]

This paper summarizes a PhD-project that is currently under completion at Delft University of Technology, Faculty of Architecture, Building Physics Group. The general problem addressed in this project is the integration of building simulation tools and building design. This problem has been narrowed down to computational support for one specific type of building design decision: the selection and integration of one or more energy saving building components like solar walls, advanced glazing systems, sunspaces and photovoltaic arrays into a given building design.The paper provides an overview of the main research efforts that were carried out during the research [Computational Support for the Selection of Energy Saving Building Components, PhD thesis (in preparation), Building Physics Group, Faculty of Architecture, Delft University of Technology, 2004]. Based on results and on views that have been described in earlier publications (e.g. [Beter ontwerp door gebouwprestatie-simulatie? in: Proceedings of the Third IBPSA-NVL Conference, Petten, The Netherlands, December 2001]), the paper then will identify relevant, challenging issues for future research in the area of integration of building simulation tools into the building design process. Specific attention will be paid to issues related to the use of different categories of tools (simulation tools as well as other instruments) for the development of new, energy-efficient building design concepts. [Copyright &y& Elsevier]

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]

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]

Australia’s commercial building stock exceeds 134 million m 2 of net lettable area, with retail buildings contributing 35% to this sector’s energy use. The energy intensity of retail buildings in hotter climates is higher than the national average, as is the energy intensity of smaller buildings (under 1500 m 2 ) that are not considered ‘high-end’ commercial properties. Little attention has been paid to improving the energy efficiency of these types of buildings through regulation (for new buildings) or through market mechanisms (for retrofitting). As many of these buildings are single storey ‘warehouse’ type buildings, their predominant heat load comes through the roof, and thus are well suited to benefit from cool roof technology. Despite this, there remains a deficiency in quantifying the benefit of such technology in the context of single-storey retail buildings in Australia. This paper reports on an experimentally validated numerical study aimed at addressing this deficiency. Results show that application of cool roof technology to a warehouse type building in a subtropical environment increases the energy efficiency by shifting space temperature towards the design set point (21–23 °C), and thus reducing cooling energy demand. This study also indicates an energy saving every month with the application of cool roof, with the largest saving in hotter months and no heating penalty in cooler months. Application of cool roof technology on warehouse style buildings across Australian buildings indicates energy savings can be achieved in all broad Australian climatic zones, with the greatest energy reduction associated with tropical, subtropical and desert environments. [ABSTRACT FROM AUTHOR]

• Changes in peak demand are often larger than changes in annual demand. • Ventilation rate is the parameter with the largest contribution for HVAC demand. • Equipment and lighting density have substantial contributions for total demand. Previous dynamic building simulation studies have not often focused on analysing the sensitivity of peak loads to input parameters. However, these peak loads may have a critical impact on system design capacities and power network operation. This study aims to examine the sensitivity of cooling demand related results (total electricity demand, HVAC end-use and space cooling) in a large office building using two global sensitivity analysis methods: Morris elementary effect and Sobol indices. More specifically, this paper examines the implications of different type of climates to the uncertainty in these different cooling output results and the sensitivity of the different parameters for each result. Moreover, this paper investigates the difference between the effects of annual and peak analysis for cooling demand of office buildings, which can provide insight on cooling demand from the perspectives of total cooling energy and system capacity for building cooling systems, respectively. This study has found that generally, the changes are more significant for peak demand than for annual demand. The coefficient of variation for the total peak demand is around 25% and 21% for total annual demand. This study identifies that the ventilation rate is the parameter that contributes the largest for the uncertainty in electricity demand of the HVAC end-use, between 50% and 70% of the change (S T), both for annual and peak demand. Regarding the effect on total electricity demand, ventilation rate is still one of the most critical factors, but equipment and lighting densities also become a significant contributor to the sensitivity of the total demand. [ABSTRACT FROM AUTHOR]

Built environments contribute significantly to mitigating climate change. However, existing buildings, which form most urban infrastructures, do not typically meet contemporary stringent energy efficiency standards. They are naturally continuously deteriorating, making them a continuous negative contributor to their surrounding environments, which keeps getting worse. Therefore, there is a need to develop performance diagnosis frameworks and approaches for accurate Building Energy Model (BEM) simulations to develop impactful retrofitting design solutions that could make existing buildings perform closer to current efficiency measures. This paper reports on research that focuses explicitly on calibrating envelopes of existing BEMs using drones equipped with thermography sensors. The study specifically focuses on the automation of on-the-fly envelope U-value estimations and verification of calibrated envelope BEMs. The paper examines a renovated campus building in Boston, MA., representing material degradation, thermal bridging, and insulation failures using thermal imaging. A BEM is then calibrated, and post-renovation metered and modeled wintertime heating energy are compared. Goodness of fit measures showcase BEM performance improvement from 21.8%to 0.9%, which demonstrates the utility of the proposed framework. Further research is recommended to expand the focus on anomalies in the envelope and increase the scope from the building scale to the neighborhood scale. [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]

This paper explores the potential of thermotropic (TT) windows as a means of improving overall building energy performance. Capitalising on their ability to dynamically alter solar and visible light transmittance and reflectance based on window temperature, they have the ability to reduce solar heat gains and subsequently reduce cooling loads when the external conditions exceed those required for occupant comfort. Conversely when the external conditions fall short of those required for comfort, they maintain a degree of optical transparency thus promoting opportunities afforded by passive solar gains. To test their overall effectiveness, thermotropic layers made of varying hydroxypropyl cellulose (HPC) concentrations (2 wt.%, 4 wt.% and 6 wt.%) were firstly synthesised and their optical properties measured. Building performance predictions were subsequently conducted in EnergyPlus for four window inclinations (90°, 60°, 30° and 0° to the horizontal) based on a small office test cell situated in the hot summer Mediterranean climate of Palermo, Italy. Results from annual predictions show that both incident solar radiation and outdoor ambient temperature play a significant role in the transmissivity and reflectivity of the glazing unit. If used as a roof light, a 6 wt.% HPC-based thermotropic window has a dynamic average Solar Heat Gain Coefficient (SHGC) between 0.44 and 0.56, this lower than that of 0.74 for double glazing. Predictions also show that in the specific case tested, the 6 wt.% HPC-based thermotropic window provides an overall annual energy saving of 22% over an equivalent double glazed unit. By maintaining the thermotropic window spectral properties but lowering the associated transition temperature ranges, it was found that the lowest temperature range provided the smallest solar heat gains. Although, this is beneficial during periods where cooling may be needed, in those periods where heating may be required, opportunities gained through passive solar heating are limited. In addition, with lower solar heat gain, there is a possibility that artificial lighting energy demand increases resulting in additional energy consumption. [ABSTRACT FROM AUTHOR]

The variable air volume (VAV) system is the most popular form of heating, ventilation, and air-conditioning (HVAC) system used in commercial buildings. Researchers and engineers often use VAV systems as a reference when evaluating new technologies and systems or comparing design options. However, VAV system performance varies significantly, in part because of variations among VAV system controls, so, when analyzing use cases, it is critical to accurately represent system controls in order to accurately define system performance. Unfortunately, no existing literature documents standard VAV system controls for this purpose. This paper aims to remedy this omission by characterizing the variations in VAV system controls and proposing an approach to representing VAV system baseline performance. We used EnergyPlus to model variation among VAV system controls. We use the medium-size office reference-building model developed by the U.S. Department of Energy to demonstrate the impact of variations among controls in two U.S. climate zones and sort system performance into “good,” “average,” and “poor” categories. [ABSTRACT FROM AUTHOR]

In this paper a new dynamic simulation model for the building energy performance analysis of multi-enclosed thermal zones, where rigid air temperature and humidity conditions must be kept, is presented. The model was implemented in a suitable computer code (DETECt 2.3.1) developed for research scopes. Such simulation model allows the hygrothermal analysis of buildings with multi-enclosed thermal zones surrounded by larger ones (e.g. display glass cases with valuable artefacts in museum halls, neonatal intensive care units for premature and full-term newborn babies in hospitals wards, etc.). For this purpose, a novel control algorithm, based on a model reference adaptive control scheme, enabling the online adaptation of the control gains, is implemented. Rigid air temperature and humidity conditions can be guaranteed also in case of sudden and rapid variations of hygrothermal loads. Through such new tool innovative techniques and operative strategies for obtaining energy efficiency and indoor comfort of special building spaces can be studied. In order to show the capabilities of the tool and the robustness of the adaptive algorithm, as well as the potentiality of the proposed multidisciplinary approach to the energy-related behaviour in buildings (based on building energy modelling and simulation and control theory), two meaningful case studies have been developed. In particular, they refer to the museums and hospital indoor spaces where enclosed thermal zones have to be kept under stringent hygrothermal conditions. [ABSTRACT FROM AUTHOR]

With the growing concern of global climate change in the future, how building energy use pattern would change in response is of great interest and importance in drafting future building performance regulations and codes. In this paper, the outputs from global climate model (GCM) are integrated to typical meteorological year weather file to downscale and predict local hourly weather data in the context of U.S. climate regions using a “morphing” methodology. The “morphed” future hourly weather data is then used by EnergyPlus to predict future energy use pattern for residential building in the United States. Case studies in four representative cities in the U.S. show that climate change is to have great impacts on residential and office building energy use during the year of 2040–2069. The change of annual energy use is predicted to range from −1.64% to 14.07% for residential building and from −3.27% to −0.12% for office building under A2 scenario (a carbon emission scenarios defined by IPCC) in different regions. The research results suggest that the climate change will narrow the gap of energy use for residential buildings located in cold and hot climate regions in the U.S. and generally reduce office building energy use in the future. It is also found that the energy use of lightings and fans will slightly decrease in the future. Moreover, the growing peak electricity load during cooling seasons is going to exert greater pressure for the future grid. [ABSTRACT FROM AUTHOR]

• Novel methodology for calculating cost-optimized refurbishment roadmaps is presented. • Coupling of building model for refurbishment and system model for heating technologies. • Refurbishment roadmap include timing and refurbishment measures for specific buildings. • Applying the optimization achieves cost savings of 6–16 % and CO 2 savings of 6–59 % • Heat pumps play a central role in the context of CO 2 reduction. A novel methodology for calculating optimized refurbishment roadmaps is developed in this paper. The aim of the roadmaps is to determine when and how should which component of the building envelope and heat generation system be refurbished to achieve the lowest net present value. The integrated optimization approach couples a particle swarm optimization algorithm with a dynamic building simulation of the building envelope and the heat supply system. Due to a free selection of implementation times and refurbishment depth, the optimization method achieves the lowest net present value and high CO 2 reduction and is therefore an important contribution to achieve climate neutrality in the building stock. The method is exemplarily applied to a multi-family house built in 1970. In comparison to a standard refurbishment roadmap, cost savings of 6–16 % and CO 2 savings of 6–59 % are possible. The sensitivity of the refurbishment roadmap measures is analyzed on the basis of a parametric analysis. Robust optimization results are obtained with a mean refurbishment level of approx. 50 kWh/m2/a of the building envelope. The preferred heat generation system is a bivalent brine-heat pump system with a share of 70 % of the heat load being covered by the electric heat pump. [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]

The objective of this paper is to review the topics related to the optimized operation of intelligent buildings with respect to occupant comfort and energy consumption. To simultaneously optimize energy costs and indoor environmental quality, intelligent buildings should consider several continuously changing inputs including energy exchange processes across the building, sets of indoor and outdoor environmental parameters, energy prices, occupants' presence, preferences, and behavior inside the building. Therefore, a well-structured framework supported by computational intelligence and optimization methods, environmental monitoring, and behavior modeling techniques, as well as comfort, productivity, and behavioral studies, are required to make optimal decisions for the indoor environment. In this paper, the main concepts, challenges, the latest studies, findings, and developments related to the six topics of (1) Occupant comfort conditions; (2) Occupant productivity; (3) Building control; (4) Computational optimization; (5) Occupant behavior modeling; (6) Environmental monitoring and analysis, in offices, commercial and residential buildings are reviewed. Moreover, future directions and challenges related to the optimized operation of intelligent buildings are discussed. [ABSTRACT FROM AUTHOR]

The capability to dynamically plan, predict, and control indoor conditioning allows to adapt to individual preferences of inhabitants and enables demand side management. While former mainly improves thermal comfort of inhabitants so does latter unlock ecological and financial opportunities mostly for energy utilities. Commonly, dynamic indoor conditioning is based on piece-wise constant indoor temperature constraints. This paper's contribution is the presentation of additional constraints: in particular ones expressed relative to the nominal behavior of a hydronic heating system. This allows to simultaneously harness the relevant process variables in particular during the pre-loading and post-loading phases of a load reduction. The findings are based on data sets acquired on 10 inhabited, residential buildings in Stockholm over a whole year. One of the findings is that building models need to be adaptable if predictive control is applied in practice. This adaptability is assured by a novel concept, i.e. a so called model manager on which the control is relying for the selection of the most accurate model. Centralized optimal control of buildings connected to a district heating network is challenging in practice due to a high computational load. In order to reduce it, the herein presented method elaborates plans only every hour instead of at every control step for optimal control. Since these plans cannot be optimal due to the lack of regular update a hitherto unknown cascaded control logic has been developed that corrects planning errors and other disturbances. Capabilities are demonstrated and compared to conventional controllers in dynamic simulations of a multi-zoned building. The herein presented method is to our knowledge the first to provide all flexibility desired by energy utilities and inhabitants alike through harnessing the consequences of transitions. [ABSTRACT FROM AUTHOR]

Due to the inherent complexity of multi-dimensional solutions for transient conduction in a multilayered building element, most heat balance-based algorithms assume that conduction occurs only in one dimension—through the building element between the inside and outside environments. Usually, since the length and width of the elements are much greater than the thickness of the element, this is a valid assumption. However, there are some cases such as hydronic radiant slabs where this assumption may not lead to an accurate estimate of the energy consumption of the system. In a hydronic radiant slab, water tubes are embedded in a slab, and hot or chilled water is circulated to meet any heating or cooling loads. The energy consumption of such a system will depend highly on the temperature of the water being sent to the slab (i.e., produced by the boiler or chiller). The temperature of the water that must be sent to the slab will depend on the spacing of the tubes, the multilayer construction of the building element, and the boundary conditions to which the construction is exposed. Thus, to accurately predict the energy consumption of such a system, a transient two-dimensional heat conduction model must be integrated with a heat balance-based simulation. This paper demonstrates the potential loss of accuracy associated with the one-dimensional radiant system models by providing a comparison of one- and two-dimensional solutions using EnergyPlus. [ABSTRACT FROM AUTHOR]

It is known that building performance usually involves different criteria such as the energy consumption, thermal comfort, thermal loads and internal temperatures, which can make its assessment more complex. Thus, this paper aims to describe a decision-making process to improve some criteria related to the energy efficiency of residential buildings considering a multicriteria approach. Computer simulation using EnergyPlus v8.3-2014 was performed to assess the thermal and energy performance of a residential building for four different climates in Brazil (Belém, Campo Grande, Curitiba and Florianópolis). The performance criteria were the degree-hours and energy consumption for air-conditioning (both for heating and cooling) in different decision makers’ preference scenarios. The performances of eight constructive systems were evaluated considering uncertainties related to the operational variables of the building, obtained through field survey. The results showed that some systems had statistically equivalent performance considering the range of uncertainty. The system of masonry with double massive brickwork performed better for three climates; for Belém, however, an adequate system highly depends on the preference scenario. Nonetheless, different preference scenarios would indicate different adequate systems for all climates. This study used a method of decision-making which enables the possibility for future analyses of other performance alternatives, design variables, and performance criteria. [ABSTRACT FROM AUTHOR]

The paper is concerned with the development and evaluation of control algorithms for the implementation of demand response strategies in a smart-grid enabled all-electric residential building. The dwelling is equipped with a 12 kW heat pump, a 0.8 m 3 water storage tank, a 6 kW photovoltaic (PV) array, solar thermal collectors for domestic hot water heating and an electric vehicle. The building, located in Ireland, is fully instrumented. An EnergyPlus building simulation model of the dwelling was developed and calibrated using monitored data from the building. The developed model is used to assess the effectiveness of demand response strategies using different time-of-use electricity tariffs in conjunction with zone thermal control. A reduction in generation cost (−22.5%), electricity end-use expenditure (−4.9%) and carbon emission (−7.6%), were estimated when DR measures were implemented and compared with a baseline system. Furthermore, when the zone control features were enabled, the efficiency of the control improved significantly giving, an overall annual economic saving of 16.5% for the residential energy cost. The analysis also identified an annual reduction of consumer electricity consumption of up to 15.9%, lower carbon emissions of 27% and facilitated greater utilisation of electricity generated by grid-scale renewable resources, resulting in a reduction of generation costs for the utility of up to 45.3%. [ABSTRACT FROM AUTHOR]

Integrated building design is inherently a multi-objective optimization problem where two or more conflicting objectives must be minimized and/or maximized concurrently. Many multi-objective optimization algorithms have been developed; however few of them are tested in solving building design problems. This paper compares performance of seven commonly-used multi-objective evolutionary optimization algorithms in solving the design problem of a nearly zero energy building (nZEB) where more than 1.6 10 solutions would be possible. The compared algorithms include a controlled non-dominated sorting genetic algorithm with a passive archive (pNSGA-II), a multi-objective particle swarm optimization (MOPSO), a two-phase optimization using the genetic algorithm (PR_GA), an elitist non-dominated sorting evolution strategy (ENSES), a multi-objective evolutionary algorithm based on the concept of epsilon dominance (evMOGA), a multi-objective differential evolution algorithm (spMODE-II), and a multi-objective dragonfly algorithm (MODA). Several criteria was used to compare performance of these algorithms. In most cases, the quality of the obtained solutions was improved when the number of generations was increased. The optimization results of running each algorithm 20 times with gradually increasing number of evaluations indicated that the PR_GA algorithm had a high repeatability to explore a large area of the solution-space and achieved close-to-optimal solutions with a good diversity, followed by the pNSGA-II, evMOGA and spMODE-II. Uncompetitive results were achieved by the ENSES, MOPSO and MODA in most running cases. The study also found that 1400–1800 were minimum required number of evaluations to stabilize optimization results of the building energy model. [ABSTRACT FROM AUTHOR]

The proposition of Net Plus-energy Buildings (NPEB) leads to the need to carry out studies of load matching in contrast with the grid impacts of distributed generation (DG). This paper performs simulations with the EnergyPlus software tool concerning a NPEB operating in four Brazilian metropolitan areas. The analyses include photovoltaic (PV) performance parameters and Load Matching and Grid Interaction indicators (LMGI). New grid impact indicators are defined in order to study the impacts of DG in the power grid. In the second stage, the work investigates economic aspects under net metering supporting. Results show the annual amount of electrical demand covered by PV varies from 29 to 51% with more potential in situations with higher PV production and higher cooling load, and the annual PV electricity that supplies the loads varies from 24 to 36% according to the seasonal variations of PV-load correlation. The levels of exported electricity into the grid are high in Brazil with annual mean power peaks surrounding 0.7 but can surpass 0.8 in the sunniest periods. The economy demonstrates the building achieves grid parity from 6 to 18% discount rates and the payback time is given for different scenarios of investment costs, discount rates and electricity tariffs. [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]

Climate change would affect the building energy demand in the future. Building simulation is a feasible way to quantitatively evaluate this impact. Since the detailed weather is dispensable for the building simulation, it is important to predict the weather data for the future. Given that the uncertainties and limitations of GCMs on regional-scale weather prediction, an alternative method of future weather data generation for future building energy demand simulation is proposed in this paper. Based on the long-/short-term climate periodicity analysis, a Dual-Periodic Time Series Model is established to predict the future monthly temperatures in Shanghai. From the fitting results and the preliminary assessment analysis, it is observed that the alternative forecasting method and the corresponding Dual-Periodic TSM has better capability of characterizing and predicting performance for both recent and future temperature trends in Shanghai than GCM under RCP4.5. In this case, this method can be used as an alternative and supplementary way to the widely used GCM. With consideration of three composite uncertainty scenarios, we convert the predicted monthly temperatures into hourly TMYs by using Morphing method. Using the future TMYs as the weather input of prototypical building models of Shanghai, we can see that the simulated building energy demand presents fluctuant trends in the future periods, different from the continuous uptrends of that using IPCC RCP4.5. [ABSTRACT FROM AUTHOR]

This paper describes the development and focus in the analysis of the Brazilian metamodels used in the residential labeling system. The metamodels represent the thermal behavior of naturally ventilated and artificially air-conditioned residential buildings. A computational simulation experiment using the EnergyPlus program was conducted taking into account naturally ventilated and artificially air-conditioned residential buildings. Several parameters were considered for parametric simulations such as thermal transmittance, thermal solar absorptance, shading device, ventilation opening factor. The multi-linear regression method was used to predict the cooling degree hours and the annual energy consumption for heating and cooling for dormitories and living room. An assessment on the accuracy of the Brazilian metamodels for residential buildings was analyzed and showed that the regressions include standard errors that may influence the metamodels accuracy. Therefore, artificial neural networks (ANNs) are presented as an alternative to improve the metamodels used in the Brazilian regulation. Comparing both statistical method multi-linear regression and artificial neural network method it can be seen that ANN presented lower results for standard deviation end root mean square errors. Therefore, ANNs are recommended to improve the accuracy of the prediction in further behavior associated with the labeling system. [ABSTRACT FROM AUTHOR]

Lightweight structures have suffered long from low thermal inertia making them more vulnerable to climatic conditions. The adoption of phase change material (PCM) can potentially reduce this climatic deficiency. In order to evaluate its thermal potential, computational modeling has shown to be promising design tools. In this paper, a new TRNSYS type is developed and validated for simulating PCM-enhanced walls. Using the validated module, it is found that the best PCM's configuration is when placed in direct contact with the indoor controlled environment. Additionally, a wide range of PCM's thermal properties were simulated under typical U.S. climates to evaluate the thermal performance and identify the optimal thermal properties. The results show that a maximum saving of 0.8–15.8% is achieved on annual cooling load depending on the climate. For heating dominated climates, the savings on annual heating load is insignificant being less than 4%. The saving in peak loads was found to show more potential than annual loads for some climates. The maximum savings in peak cooling load range from 6.8 to 13.3% while savings in peak heating load range from 7 to 10.5%. For maximum savings in zonal loads, the optimal thermal properties of PCM are found to hover around the operational thermostat setpoints. [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]