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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 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]

• A two-step framework for grey-box modeling of building thermal dynamics. • Parameter identification under fluctuating data from direct expansion system. • Determined optimal complexity and data frequency in case study. • New evaluation metric that considers accumulated energy value for load prediction. Thermal dynamics model plays a crucial role of advanced control in the intelligent building. Grey box method is widely used in the modeling of building thermal dynamics based on the operation data of air-conditioner. The performance of direct-expansion (DX) system fluctuated severely during its operation, which leads to the difficulties in identification and evaluation of the building thermal model. In this paper, a two-step identification framework for second-order thermal resistance-capacity network of building based on DX system data is proposed. A case study is conducted on an office building equipped with variable refrigerant flow system in Foshan, China. The results show that the proposed framework can identify the parameters rationally. The model achieves high accuracy for both indoor temperature (Mean absolute errors of 0.74°C) and cooling load prediction (Mean absolute errors of 4453.86W) in a short time interval of 10 minutes. Additionally, the best complexity of the grey-box model for case building and the data acquisition interval is determined. Finally, an improved set of evaluation metrics for thermal load evaluation is proposed and discussed. [ABSTRACT FROM AUTHOR]

Abstract Neighbourhood form imposes complex effects on the local microclimate. The overlook of neighbourhood microclimate will lead to miscalculation of building energy performance. In this paper, a neighbourhood-scale building simulation coupled between building energy simulation and CFD is proposed to assess the impact of external convective heat transfer coefficient (CHTC) to the building thermal performance. The co-simulation is run for different types of buildings inside the neighbourhood with different combinations of neighbourhood form parameters. The developed simulation scheme allows the iterations of neighbourhood design parameters and simulation model generation in CFD and EnergyPlus. Five empirical methods are used to predict CHTC, and the results of CHTC values, heating and cooling load intensity are compared in detail with the proposed CFD coupled method. The results suggest that the orientation and multiplier are crucial to the building load intensity in Chicago and the neighbourhood form can affect the thermal load of residential building up to about +27.1% and −18.6%, and +17.2% and −7.7% for office buildings. The proposed method and framework in this paper is capable of generating design guidelines for the optimal energy performance of the buildings in the neighbourhood. Highlights • Neighbourhood-scale building block energy simulation coupled with CFD. • Iterated various neighbourhood design parameters and building model generation. • Empirical methods in evaluating CHTCs are evaluated and compared with CFD method. • Neighbourhood form can affect certain type of building's energy use up to ± 20%. • Neighbourhood design guideline for optimal building thermal load is suggested. [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]

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]

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]

Increased environmental and economic awareness, coupled with local government incentives, means that solar energy applications in buildings are crucial to sustainable urban development and commercial energy investment. Considering the large available areas on building rooftops and walls, and the application of building-integrated photovoltaic products, previous research has paid extensive attention to solar radiation estimation for roofs and vertical walls. This paper proposes a developed pixel method integrated with Net Present Value (NPV) analysis and applies the measurement to 3D visualisation of the revenue distribution of potential PV projects on building surfaces, including vertical and non-vertical facades. A building model derived from a real student accommodation building is then chosen as a case study in this paper. The developed algorithm extracts data built in SketchUp, exports instantaneous shadow images and estimates solar energy potential over building surfaces for each pixel unit. Coloured 3D radiation maps illustrate the distribution of solar energy potential on roofs and facades. 3D contour lines with NPVs are introduced to visually conduct PV installation strategies with desirable revenues over project lifetimes. The results reveal that facade installation is also worthwhile, as the corresponding project revenues are competitive with rooftop installation during specific periods. This study will benefit designers and construction managers in determining the optimal solution for implementing solar rooftop and facade systems in the design and construction phases. [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]

Solar energy applications in buildings are crucial to urban sustainable development and commercial energy investment. Increased environmental and economic awareness, coupled with local governmental incentives, inevitably causes a growth in construction of photovoltaic (PV) projects. Previous studies extensively explored and developed life cycle energy assessment and costing analysis for payback times of rooftop PV systems. This paper presents a new methodology that combines a life cycle cost (LCC) approach and a pixel method for visualising economic performances of constructing PV projects. The time of recovering the expenditure invested in a PV project is then considered a main economic indicator and is formulated mathematically in the pixel unit representing an actual area of 3 cm × 3 cm in an empirical study. A building model derived from a real student accommodation is then chosen as a case study in this paper. The 3D visualisation demonstrates a potential investment return for placing each solar panel on a specific site over roofs and facades. The proposed contour lines of economic returns also illustrate the optimum ranges for constructing potential PV projects with desirable payback periods during their lifetimes. Results reveal that the façade installation is sometimes competitive with the roof-top installation. This study will benefit designers and construction managers in determining the optimal solution of implementing the rooftop and façade PV systems in the phases of design and construction. [ABSTRACT FROM AUTHOR]

Urban morphology and surface thermal properties are two key factors that determine the local-level urban heat island effect. Among different strategies that help in mitigating this effect, applying high reflective cool coating on urban surfaces is one of the most effective ways for changes in both the daytime and night-time urban air temperatures. In this paper, advances in the EnviBatE microclimate model for district-level numerical simulations are presented and the results are compared against full-scale experimental measurements conducted in Singapore. Key aspects of the EnviBatE tool along with their mathematical models are demonstrated. The experimental site consists of two side-by-side street canyons located at an industrial estate in the west of Singapore: one canyon with conventional and the other with cool surfaces for side-by-side comparison. The results suggest that the drop in street-level canyon air temperature varies from 1 to 4 °C with the application of the cool surface coating on roofs, walls and roads in comparison with conventional canyon under various scenarios. By analysing the effects of the three types of built-up surfaces separately, the cool roads scenario appears to have the greatest impact on canyon air temperature (at 3 m above ground), followed by the cool roofs scenario. The cool walls scenario produces negligible cooling impact. The advantages of the cool coating application on urban surfaces in reducing air temperature are statistically analysed and demonstrated. The pedestrian comfort in terms of the Universal Thermal Climate Index (UTCI) is proposed to be integrated with EnviBatE and discussed using the simulation results. • Simulations using EnviBatE microclimate model are compared with field experiments. • Canyon air temperature reduction up to 2 °C is observed due to cool coating. • Microclimate study with cool surfaces is expanded to a district scale model. • Integration of Universal Thermal Climate Index (UTCI) with EnviBATE is presented. [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]

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]

In this paper, CFD analysis was used to determine the internal air temperature over long periods of four full scale housing test modules in Newcastle, Australia exposed to a moderate climate. The influence of different time steps (i.e. 15, 20 30, 35, 40 45, 60, 80, 100, 120, 150, 180 minute intervals) on the discrepancies in peak temperature for the CFD simulations is discussed in the paper. It was found that the average discrepancies in time for internal peak temperatures were between one and five hours compared with the real responses of the testing modules. [ABSTRACT FROM AUTHOR]

This paper presents a thermodynamic and exergoeconomic analysis of a recently-retrofitted Passivhaus non-domestic building. The selected case study, a Community Centre located in London, underwent a deep-energy retrofit in 2011, becoming the first ‘non-domestic Passivhaus’ retrofit in the country. As the building was retrofitted per Passivhaus standards, which is based solely on First Law analysis, a thermodynamic investigation can provide a novel means by which to assess its exergy efficiency and cost-effectiveness. As such, the aim of this paper is to conduct a comprehensive exergy and exergoeconomic analysis, presenting novel performance indicators for the pre-retrofit and post-retrofit Passivhaus building. First law outputs show that the improvement presents high levels of energy savings (75.6%), reductions in carbon emissions (64.5%), and occupant thermal comfort improvement (28.8%). Second law outputs present a reduction in primary exergy input reduction of 56.4% and exergy destructions of 60.4%, leading to improve building exergy efficiency from 9.8% to 18.0%. Nevertheless, exergoeconomically the building did not perform as expected due to high capital cost and exergy destructions cost rates. These results give an insight into the thermodynamic impact of the Passivhaus approach, providing a critical assessment of the strengths and limitations of the standard under both thermodynamic laws. [ABSTRACT FROM AUTHOR]

HVAC manufacturers make important investments to develop more efficient technologies for space heating (SH) and domestic hot water generation (DHW). Among these technologies, this article focuses on Double service air-to-water-heat-pumps (AWHP). The control of AWHP strongly impacts their performances, so that manufacturers of AWHP need to optimize the control of their systems to increase their performances. This paper presents the implementation of a Hardware-In-the-Loop – HIL – real time simulation test bench for AWHP. The new test bench proposed uses real weather conditions and simulates building’s and occupants’ response thanks to a virtual model. This test bench allows R&D departments and manufacturers to test several heating systems in parallel as well as different control options. It may also limit the need for field testing. It thus has the potential to reduce the cost and time required for AWHP development-to-market process. The test bench presented in this paper is used for a specific AWHP and a typical building in France. The analysis of the impact of the heat pump’s control on the system dynamics and on the indoor thermal comfort enables to highlight control issues related to the heating curve, the DHW management and the defrost process of AWHP. Optimized control parameters throughout time are proposed in view of reaching lower consumption and better performances. This case study shows the potential of the HIL test benches for manufacturers to optimize the development of their products controls. [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]

• High-rise design in dense urban areas is addressed using artificial intelligence. • Part 1 presents background, methodology, setup, and machine learning results. • Two design scenarios focus on daylight metrics using parametric modelling. • 31 models reported very high and 9 models reported high prediction accuracies. • The importance of surrogate modelling is discussed for sustainable metropolises. Designing high-rise buildings is one of the complex tasks of architecture because it involves interdisciplinary performance aspects in the conceptual phase. The necessity for sustainable high-rise buildings has increased owing to the demand for metropolises based on population growth and urbanisation trends. Although artificial intelligence (AI) techniques support swift decision-making when addressing multiple performance aspects related to sustainable buildings, previous studies only examined single floors because modelling and optimising the entire building requires extensive computational time. However, different floor levels require various design decisions because of the performance variances between the ground and sky levels of high-rises in dense urban districts. This paper presents a multi-zone optimisation (MUZO) methodology to support decision-making for an entire high-rise building considering multiple floor levels and performance aspects. The proposed methodology includes parametric modelling and simulations of high-rise buildings, as well as machine learning and optimisation as AI methods. The specific setup focuses on the quad-grid and diagrid shading devices using two daylight metrics of LEED: spatial daylight autonomy and annual sunlight exposure. The parametric model generated samples to develop surrogate models using an artificial neural network. The results of 40 surrogate models indicated that the machine learning part of the MUZO methodology can report very high prediction accuracies for 31 models and high accuracies for six quad-grid and three diagrid models. The findings indicate that the MUZO can be an important part of designing high-rises in metropolises while predicting multiple performance aspects related to sustainable buildings during the conceptual design phase. [ABSTRACT FROM AUTHOR]

• Part 2 presents optimisation problems, algorithms, results, and method validation. • Three optimisation algorithms are employed with five replications for two scenarios. • Results are obtained in 4 days instead of 17 years using surrogate models of Part 1. • Optimised designs outperformed 8748 and 5832 regular high-rise scenarios. • The importance, potential, and optimisation with surrogate models are discussed. High-rise building optimisation is becoming increasingly relevant owing to global population growth and urbanisation trends. Previous studies have demonstrated the potential of high-rise optimisation but have been focused on the use of the parameters of single floors for the entire design; thus, the differences related to the impact of the dense surroundings are not taken into consideration. Part 1 of this study presents a multi-zone optimisation (MUZO) methodology and surrogate models (SMs), which provide a swift and accurate prediction for the entire building design; hence, the SMs can be used for optimisation processes. Owing to the high number of parameters involved in the design process, the optimisation task remains challenging. This paper presents how MUZO can cope with an enormous number of parameters to optimise the entire design of high-rise buildings using three algorithms with an adaptive penalty function. Two design scenarios are considered for quad-grid and diagrid shading devices, glazing type, and building-shape parameters using the setup, and the SMs developed in part 1. The optimisation part of the MUZO methodology reported satisfactory results for spatial daylight autonomy and annual sunlight exposure by meeting the Leadership in Energy and Environmental Design standards in 19 of 20 optimisation problems. To validate the impact of the methodology, optimised designs were compared with 8748 and 5832 typical quad-grid and diagrid scenarios, respectively, using the same design parameters for all floor levels. The findings indicate that the MUZO methodology provides significant improvements in the optimisation of high-rise buildings in dense urban areas. [ABSTRACT FROM AUTHOR]

The building sector is one of the largest consumers of natural resources and energy in the world. Strategies to improve the energy efficiency are a solution to improve the performance of a building. However, in most cases, such strategies are assessed only at the operational phase of a building. The life-cycle energy analysis can be an important tool to allow choosing the best strategy. The objective of this paper is to assess the energy performance of four window shading systems in a house using life-cycle energy analysis. Life-cycle cost analysis was also used to determine not only the most energy-efficient strategy, but also the most economically feasible one. A house located in Florianópolis, southern Brazil, was evaluated based on a 63-year lifespan. The four window shadings analysed were: punctured concrete blocks, aluminium double sliding shutters, PVC roller shutters and wooden double open shutters. The EnergyPlus computer programme was used to estimate only the annual energy consumption for air-conditioning with and without the shading strategies. National and international databases were used to perform the life-cycle analysis. For the life cycle cost analysis, the cost of each strategy was obtained through a survey in local stores. The average inflation rate in the last ten years in Brazil was used to estimate the future costs. The results showed that the use of wooden double open shutters and PVC roller shutters are the most suitable solutions for window shading, both in terms of energy and costs, if they are not replaced during the lifespan of the building. This paper pointed out the need of using the proposed methodology to assure the right choice of strategies for energy efficiency in buildings. [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]

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]

In this paper simulation results of two typical houses in St. John's are presented using BEOpt (Building Energy optimization) software. Simulation is done using one-year weather data, typical thermostat settings and occupation of two houses. BEopt provides capabilities to evaluate residential building designs and identify cost-optimal solution at various levels of whole-house energy savings along the path to zero net energy. It was noticed that the annual energy consumption determined using simulation in BEopt is almost same as the actual energy consumption, logged with a sampling time of 2 minutes for both the houses. This simulation, analysis and a comparison is necessary to validate the thermal model of houses. The developed model of the houses could be used to design a renewable energy system or study human behaviour impact on the house energy consumption. Simulation results and a detailed analysis of the logged data are presented in the paper. [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]

Abstract: Metal mesh grids and perforated screens are used in new and renovated buildings as an external second skin for the building envelope; to enhance architecture design, to filter daylight and to reduce solar gains. Their effectiveness as shading devices depends on their geometry, texture and application. In the first part of the paper six types of shadings with similar transparencies, depending on geometry and openness factor of metal mesh grid and perforated screen, were investigated. Integrating sphere measurements were collected to obtain spectral optical properties of the shading devices. The optical properties were measured for incidence angles between normal and 60°with a step of 15°. The data collected were integrated to obtain visible and solar transmittance values in accordance with ISO 9050. In the second part of the paper a parametric study on spacing and thread dimension of metal mesh grids was done using Window 6.2 models to evaluate their shading potential, defining annual different shading profiles depending on shading geometry, latitude and orientation. A conventional office unit with a curtain wall façade with a metal mesh grid shading system was modeled in TRNSYS to evaluate the solar gain profiles and their effects on the energy balance of the office unit, under different weather conditions. [Copyright &y& Elsevier]

Abstract: The importance of light not only as a therapeutic tool but as an essential element of healthy living has been highlighted by the recent discovery of a specialized photoreceptor in the eye responsible for synchronizing our internal circadian pacemaker. This pigment, melanopsin, differs from visual receptors in several characteristics, here simplified into a blue-shifted spectral sensitivity and a dose–response curve established from night-time studies. While a vast range of tools has been developed to simulate the amount of light in lux or lumens falling on a static, horizontal surface, corneal exposure estimates are needed for modelling the biological responses to light in space, which require a vertical sensor that can rotate and translate as a human eye does. This paper examines the effects of housing design upon the amount of daylight available for maintaining synchronization of the human circadian system considered in conjunction with human movement, using historic Boston row houses as a case study. Based on a series of simulations taking into account the two above-mentioned characteristics of the non-visual system, this paper proposes a preliminary workflow for suggestions regarding lighting restoration and opens new perspectives on future variables to include. This study found that even modest renovations like painting the space a lighter colour have a noticeable impact on the light received by a moving sensor. More aggressive design choices, such as not using the basement floor of the house for apartments, raise the amount and timing of light received to nearly the level of the best-case scenario. [Copyright &y& Elsevier]

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]

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]

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 When integrated photovoltaics into building windows, the solar cells will absorb a fraction of solar radiation that hit on the window surface to generate electrical power and thus obstruct the solar energy and natural daylight that would have penetrated into inside space. In this paper, a window system integrated with thin film CdTe solar cells with 10% transparency was electrically characterised by Sandia model. The annual energy performance of a typical office with this PV window applied to different façade design was investigated using EnergyPlus under five typical climatic conditions in China. The dynamic daylight performance of the office has been assessed using RADIANCE. The result shows that when compared to a conventional double glazed system, the application of PV window can result in significant energy saving potential if the office has a relative large window-to-wall ratio (i.e. WWR≥45%). The simulation results also show that this PV window offers better daylight performance than conventional double-glazing. [ABSTRACT FROM AUTHOR]

Abstract This paper presents the results of the physical measurements, thermal comfort survey and building simulation considered in different units of a development in the Southeast of England. The study investigates the winter performance and cold stress in buildings built with prefabricated structural insulated panels (SIPs). The study employed the measurements of the parameters, administration of the questionnaire supplemented with interviews, discussions, and observations, as well as building simulations to collect data for analysis. The results showed 87% of the responses on thermal sensation ranged from slightly cool to slightly warm while 84% preferred no change to the thermal environment. On thermal acceptability, 93% of the participants highlighted that the thermal environment was right for them. For the measurement, the results showed a mean temperature of 18.6 °C. The neutral and preferred temperatures in the units are 21.5 °C and 24.5 °C respectively. The temperatures are found to be within the comfort range in winter. The findings revealed that prefabricated timber buildings perform better in winter than summer and occupants are thermally comfortable in winter. The study showed that a combination of variables like humidity, temperature, dew-point could influence the stress indexes. Regarding the assessment of cold stress indexes, the study recommends the WBGT of 14.3 °C and UTCI of 17.9 °C as the stress thresholds for the period of physical measurements in the winter. For the meteorological winter months (December–February), the research proposes the WBGT of 13.4 °C and UTCI of 16.6 °C for London Islington TRY while it recommends the WBGT of 13.3 °C and UTCI of 16.5 °C for St Albans TRY. The study revealed the possibility of slight cold stress for occupants of the buildings and further adaptive measures are required to reduce exposure of occupants to cold stress in the future. Highlights • The responses on thermal sensation ranged from slightly cool to slightly warm. • The responses on thermal sensation ranged from slightly cool to slightly warm. • The WBGT of 14.3 °C and UTCI of 17.9 °C are computed as the stress thresholds for the measured period (Jan-Feb). • For the winter months (Dec-Feb), the research proposes WBGT of 13.4 °C and UTCI of 16.6 °C (London Islington-TRY). • It recommends the WBGT of 13.3 °C and UTCI of 16.5 °C for St Albans-TRY. • The study revealed the possibility of slight cold stress for occupants of the buildings. [ABSTRACT FROM AUTHOR]

Abstract Due to financial and construction limitations, existing museum buildings often do not allow the retrofit of complex HVAC systems. Thus, an attractive way to improve the quality of the microclimates in the museum rooms is to improve the control of systems that are already installed. This paper presents novel proven control strategies to drastically reduce the temperature and relative humidity fluctuations in the exhibitions halls of a Polish museum in a moderate climate. The methods comprise improved control of existing indoor temperature and ventilation airflow control systems, without humidification and dehumidification devices. Simulations were performed with EnergyPlus software; the multi-zone model of the building was calibrated and verified with existing measured data. A warm period of the year was simulated, and six weeks of it were experimentally validated. The existing heating and cooling systems comprised radiators and in some areas, fan-coils. Through improved control schemes and the addition of outdoor air fans, the period with the small relative humidity fluctuations (<±5%) increased from 2% to 88% in the season. Highlights • Control microclimate strategies in the exhibitions halls are presented. • Combination temperature control together with ventilation is analyzed. • When hybrid ventilation is used, de- and humidification devices are not needed. • The proposed methods have significantly greater impact on microclimate in summer. • The proposed systems are not expensive and easy to implement in existing museums. [ABSTRACT FROM AUTHOR]

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: In building simulations it is common practice to use standardized occupant behavior and internal gains. Although this is a valid approach for designing systems, the probabilistic nature of these boundary conditions influences the energy demand and achieved thermal comfort of real systems. This paper analyzes the influence of occupant behavior on the energy performance and thermal comfort of a typical office floor equipped with a thermally activated building system (TABS). A multi-zone TRNSYS model with 10 adjacent zones per orientation for a typical moderate Belgian climate is set up. First, the energy performance and thermal comfort of thermally activated building systems (TABS) are compared with the performance of idealized cooling with standardized user behavior. TABS are able to deliver good thermal comfort but show to have a higher energy demand. Secondly, probabilistic occupant behavior was implemented in the TABS simulations. The influence of the occupancy rate, the shading device use and switching of the lights are analyzed by defining user profiles. It is shown that occupant behavior may have an important influence on the cooling demand and thermal comfort. However, as long as good solar protection is foreseen and operated in a correct way, TABS are able to cope with different user behavior modeled in this paper. In this case, normal daily stochastic processes do not considerably affect the cooling demand and thermal comfort during summer. [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]

Highlights • Assess the impact of design factors of semi-transparent PV window on building performance. • Evaluate an office performance with integrated STPV window using innovate approach. • Apply window integrated PV resulting in a reduction in energy consumption of up to 73%. • Selected PV window offers better daylight performance than conventional double glazing. Abstract When integrating photovoltaics into building windows, the photovoltaic glazing modules inhibit the function that glass performs, with the additional function of energy production. Semi-transparent Photovoltaic (STPV) glazing will absorb part of the solar radiation incident on the window surface to generate electrical power. In turn, this affects the overall solar energy and natural daylight penetrating into the indoor space. Various factors determine the building energy performance and indoor comfort level as a result of adopting STPV glazing. The factors regarding window design alone (window size, PV glazing coverage ratio and PV glazing placing position) require rigorous study. In this paper, an innovate model (combined optical, electrical and energy model) was developed to comprehensively evaluate the performance of an office equipped with STPV window and firstly analyse the effect of window design on overall energy efficiency. A double-glazing unit integrated with thin film CdTe solar cells with 10% transparency was electrically characterised by Sandia Array Performance Model (SAPM). The annual energy performance of a typical office served by window integrated STPV glazing was investigated through EnergyPlus simulation for various window designs under five typical climatic conditions in China (using weather files of Harbin, Beijing, Shanghai, Guangzhou and Kunming for representation). The optical performance (defined by a Bidirectional Scattering Distribution Function) of this STPV glazing was also obtained using a ray-tracing technique. Then, the annual daylight performance of the porotype office was assessed using RADIANCE. We found that when compared to a conventional double-glazed system, the application of PV window can result in considerable energy saving if the office has a relatively large window-to-wall ratio (i.e. ≥ 45%), while the position of placing STPV glazing has significant influence on the lighting energy consumption. In the specific climates under test, the optimal design scenario of applying window integrated PV can result in a reduction in energy consumption of up to 73%. The simulation results also show that this PV window offers better daylight performance than conventional double glazing and effectively reduces the possibility of glare. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents an experimental study of indoor thermal environment near a full-scale glass facade with different types of shading devices under varying climatic conditions in winter. Interior glazing and shading temperature, operative temperature and radiant temperature asymmetry were measured for façade sections with roller shades and venetian blinds at different positions. Interior glass surface temperatures can be high during sunny days with low outdoor temperature. Shading systems significantly improved operative temperature and radiant temperature asymmetry during cold sunny days, depending on their properties and tilt angle. During cloudy days the impact was smaller, however the shading layers could still decrease the amount of heat loss through the façade. A transient building thermal model, which also calculates indoor environmental indices under the presence of solar radiation, was developed and compared with the experimental measurements. Part II of this paper uses this validated model with a transient, two-node thermal comfort model (including transmitted solar radiation) for assessment of indoor environmental conditions with different building envelope and shading properties, façade location and orientation. [Copyright &y& Elsevier]

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]

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]

In order to meet the ambitious emission-reduction targets of the Paris Agreement, energy efficient transition of the building sector requires building retrofit methodologies as a critical part of a greenhouse-gas (GHG) emissions mitigation plan, since in 2050 a high proportion of the current global building stock will still be in use. This paper reviews current retrofit methodologies with a focus on the contrast between data-driven approaches that utilize measured building data, acquired through either 1) on-site sensor deployment or 2) from pre-aggregated national repositories of building data. Differentiating between 1) bottom-up approaches that can be divided into white-, grey- and black-box modelling, and 2) top-down approaches that utilize analytical methods of clustering and regression, this paper presents the state-of-the-art in current building retrofit methodologies; outlines their strengths and weaknesses; briefly highlights the challenges in their implementation and concludes by identifying a hybrid approach - of lean in-situ measurements supplemented by modelling for verification - as a potential strategy to develop and implement more robust retrofit methodologies for the building stock. • A state-of-the art review on data-driven building modelling techniques is presented. • The models are classified into top-down and bottom-up approaches. • Comparative discussion on white-, grey- and black-box models is included. • An outlook on the latest building data collection technologies is also included. [ABSTRACT FROM AUTHOR]

Highlights • A component-based method of machine learning for performance prediction in engineering. • Components instead of one monolithic model extend reusability and generalization. • Flexible design support by machine learning for early design phases. • Internal parameters between components allow insights in the “black box” of machine learning. • Good prediction accuracies (error < 3.9%) for test cases different from the training model. Abstract Machine learning is increasingly being used to predict building performance. It replaces building performance simulation, and is used for data analytics. Major benefits include the simplification of prediction models and a dramatic reduction in computation times. However, the monolithic whole-building models suffer from a limited transfer of models and their data to other contexts. This imposes a vital limitation on the application of machine learning in building design. In this paper, we present a component-based approach that develops machine learning models not only for a parameterized whole building design, but for parameterized components of the design as well. Two decomposition levels, namely construction level components (wall, windows, floors, roof, etc.), and zone-level components, are examined. Results in test cases show that, depending on how far the cases deviate from the training case and its data, high prediction quality may be achieved with errors as low as 3.7% for cooling and 3.9% for heating. [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]