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Harvesting daylight for improving building energy performance has become an important issue in recent years. Most energy simulation tools use a Daylight Factor (DF) based algorithm to capture the complexity of daylight behaviors. However, DF has a limitation for fully accounting realistic daylight conditions, and several modifications or integrations with other supporting tools were developed to compensate for this limitation. This paper uses the Kriging method to predict daylight conditions for a whole year, and couples this method with energy simulation tools. Kriging is a group of geo-statistical techniques used to interpolate the value of a random field at an unobserved location from discrete observed values at nearby locations. In previous research, the Kriging method showed promising outcomes; that result was significantly better than current daylight modeling in energy simulation tool, and very close to the results from advanced physics-based daylight simulation tool. However, Kriging also comes with certain limitations that require further investigation. One major hurdle was Kriging’s long computational time compared to current daylight models in energy simulation tools. For this reason, this paper aims to find possibilities to reduce computational time to realistic levels that can be applicable in practice. Furthermore, it proposes a method to integrate Kriging-based daylight model with an energy simulation tool that can plot hourly indoor light distribution. This proposal obtains more realistic results than current energy simulation daylight models, while reducing computational expense. [ABSTRACT FROM AUTHOR]

Building energy models (BEMs) are typically used for design and code compliance for new buildings and in the renovation of existing buildings to predict energy use. The increasing adoption of BEM as standard practice in the building industry presents an opportunity to extend the use of BEMs into construction, commissioning and operation. In 2009, the authors developed a real-time simulation framework to execute an EnergyPlus model in real time to improve building operation. This paper reports an enhancement of that real-time energy simulation framework. The previous version only works with software tools that implement the custom co-simulation interface of the Building Controls Virtual Test Bed (BCVTB), such as EnergyPlus, Dymola and TRNSYS. The new version uses an open standard interface, the Functional Mockup Interface (FMI), to provide a generic interface to any application that supports the FMI protocol. In addition, the new version utilizes the Simple Measurement and Actuation Profile (sMAP) tool as the data acquisition system to acquire, store and present data. This paper introduces the updated architecture of the real-time simulation framework using FMI and presents proof-of-concept demonstration results which validate the new framework. [ABSTRACT FROM AUTHOR]

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

A major challenge facing the buildings sector is the absence of continuous commissioning and the lack of performance monitoring and evaluation leading to buildings energy performance gaps between predicted and actual measured performance. Aiming to better characterize, evaluate and bridge these gaps, the paper proposes an online building energy performance monitoring and evaluation tool ObepME, serving as a basis for fault detection and diagnostics and forming a backbone for continuous commissioning. A calibrated building dynamic energy model is developed and employed to automatically run on a daily basis and simulate the building transient performance for the previous day. The simulated energy consumption results form a baseline to which the actual collected data are compared to evaluate the dynamic energy performance gap. The OU44 University building in Denmark is considered as a case study to implement the proposed framework. A holistic energy model was developed in EnergyPlus and calibrated employing data from various building meters, collected weather conditions, generated occupancy schedules and systems operational parameters and set-points. The calibrated model was employed in the ObepME tool to automatically and continuously monitor and evaluate the OU44 building energy performance, on the level of the whole building and individual energy systems consumption, throughout the period from February to mid-March 2017. The reported dynamic energy performance gap was around −2.85%, −3.47% and 5.48% for heating, total electricity and ventilation system electricity consumption. In addition, specific observations were made on a daily basis in terms of the overall electricity, heating, lighting and ventilation energy consumption as highlighted by the ObepME tool. The ObepME tool is currently running automatically as a part of the OU44 building continuous commissioning and performance evaluation aiming to identify possible discrepancies and deviations paving the way for a methodical preventive fault detection and diagnostics process on various levels in the building. [ABSTRACT FROM AUTHOR]

Abstract: The main goal of this paper is to evaluate the possibility of application or replacement of radiators with low-temperature radiant panels. This paper shows the comparison results of operations of 4 space heating systems: the low-temperature radiant panel system without any additional thermal insulation of external walls (PH-WOI), the low-temperature radiant panel system with additional thermal insulation of external walls (PH-WI), the radiator system without any additional thermal insulation of external walls (the classical heating system) (RH-WOI), and the radiator system with additional thermal insulation of external walls (RH-WI). The operation of each system is simulated by software EnergyPlus. The investigation shows that the PH-WI gives the best results. The RH-WOI has the largest energy consumption, and the largest pollutant emission. However, the PH-WI requires the highest investment. [Copyright &y& Elsevier]

Understanding of the buildings energy performance at the city level is necessary in order to form a city energy planning policy and make a strategic decision on which buildings should be thermally improved. Energy modeling of the building sector is methodology that estimates energy consumption in buildings and simulates the effects of energy efficiency improvements of building envelopes and systems. In this paper engineering bottom-up method was used for modeling the heating energy consumption of a multi-family residential building sector built before 2012 in the city of Kragujevac in Serbia. The first part of methodology presents the criteria based on which a multi-family residential building sector of the city was described and building types were defined. The second part of methodology investigates the selection of the real buildings, their modeling and heating energy consumption simulation. The modeling method was performed on staggered block buildings constructed between 1981 and 1990, and estimated heating energy consumption was compared to measured consumption. After the simulation of different thickness of polystyrene thermal insulation additions to external walls and simulation of the new windows installation, the reductions of the total annual heating energy consumption were obtained. [ABSTRACT FROM AUTHOR]

Energy consumption in buildings is a key factor when accomplishing national and global CO 2 emission goals. The economic savings per building are limited and are difficult to argue. However, the efforts can be aggregated on district or city level: the approach described in this paper is a large scale co-simulation environment that helps to leverage efficiency by providing quantitative information about the efficiency impact on large scale, while minimizing the efforts in modeling single buildings. It addresses the challenge of modeling thermal behavior of a large neighborhood of buildings in order to calculate energy demands, regarding weather conditions, shading and room temperature settings with a time resolution below one hour. A proof of concept applies the method to a demonstration site of residential buildings and shows the heating demand and CO 2 footprint of the neighborhood. [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]

Model predictive control (MPC) has been studied in the building science realm for about three decades. However, the following two aspects of the building control have not been studied thoroughly in MPC research. One is the impact of the mixed-mode cooling system on the active heating ventilation and air conditioning (HVAC) energy consumption, and the other is the differences of individual thermal comfort preference and its impact on energy. This paper proposes an occupant-oriented mixed-mode EnergyPlus predictive control system to optimize HVAC energy consumption while meeting the individual thermal comfort preference. A web-based dashboard is implemented in the test-bed building for three months to collect individual thermal comfort preference data. The data analysis results suggest that occupants have various tolerances and preferences about thermal comfort. The simulation results show that, during one week of a typical swing season, the mixed-mode system further reduces the active HVAC energy consumption, and the diversified occupant thermal comfort preference has significant impact on HVAC energy consumption. [ABSTRACT FROM AUTHOR]

This paper quantifies green roofs energy savings which is a challenging topic, namely in Mediterranean climate with distinct heating and cooling seasons. The thermal behavior of a green roof case study in Lisbon, Portugal, was assessed through an experimental campaign during heating and cooling periods of the year of 2013. These experimental results were then used to calibrate a building energy simulation in EnergyPlus. After validation, the numerical model was used to compare the energy performance of intensive, semi-intensive and extensive green roofs. The three green roof types lead to similar heating energy needs but extensive green roof solution shows higher cooling energy needs than semi-intensive and intensive ones, of 2.8 and 5.9 times more, respectively. Furthermore, the performance of each type of green roof and different insulation properties was compared to traditional roof solutions. With no thermal insulation, extensive green roofs require 20% less energy use than black roofs and show a similar annual behavior than white roofs. Semi-intensive and intensive green roofs energy use is 60–70% and 45–60% lower than black and white roofs, respectively. Well insulated roofs do not take full advantage of evapotranspiration cooling effects, which is particularly noticeable when comparing with high reflective white roofs. [ABSTRACT FROM AUTHOR]

Through a case study building equipped with an advanced monitoring system, and toward the development of a general historic-building retrofit strategy, this paper presents a contribution to the calibration of building thermal models, characterized by massive and thick walls, common features of historic Italian architecture. Model calibration is performed to assess the values of parameters which are most representative of the measured building behaviour. The approach introduces sensitivity analysis for factor fixing, harnessing hourly data and metrics that take into account surface temperatures. With the measured data and simulations, the relative importance of model parameters is inferred. Different error metrics and models configuration are presented and compared in order to determine the best approach allowing the detailed calibration of building and overcoming the model limitations within EnergyPlus to characterize the heat capacity of radiators. The procedure is verified by evaluating heating energy consumption comparing different models to measured data. The resulting “prebound” effect is lowered from 64% to 3.5%. The temperature root mean square error of indoor air is lowered to below 0.5 K in both summer and winter. New error metrics increase the overall model reliability. The methodology is implemented in an open-source software tool chain that may be used in other building types. [ABSTRACT FROM AUTHOR]

Uncalibrated building energy models, as well as models calibrated only on a single performance indicator such as energy consumption or indoor temperature, can be significantly unreliable regarding model parameters and other performance indicators. The risk of obtaining a calibrated model whose parameters are far from the actual values is particularly high in historic buildings because of the increased uncertainty about the building construction. In this paper, we propose a calibration methodology aimed at reducing this risk and apply it on a medieval building. The building was modeled in EnergyPlus based on an energy audit. A sensitivity analysis was performed to identify significant parameters affecting the errors between simulated and monitored indoor air temperatures. The model was calibrated on the hourly indoor air temperatures in summer by minimizing the root mean square error averaged over the building using a particle swarm optimization algorithm. A second calibration was performed by varying the parameters of a representative room. By comparing the results from these two calibrations, we obtained indications about the accuracy of the model parameters. Finally, the model was validated on hourly indoor air and surface temperatures in winter where temperature root mean square errors ranged from 0.4 to 0.8 K. [ABSTRACT FROM AUTHOR]

The paper describes a study developed to estimate the energy savings potential of a radiant cooling system installed in a commercial building in India. To evaluate thermal performance and energy consumption, simulations were carried out using FLUENT and EnergyPlus software, respectively. The building model was calibrated using the measured data for the installed radiant system. Then this calibrated model was used to simulate the energy consumption of a building using a conventional all-air system to determine the proportional energy savings. For proper handling of the latent load, a Dedicated Outside Air System (DOAS) was used as an alternative to Fan Coil Unit (FCU) that was installed in conjunction with the radiant cooling system. A comparison of energy consumption calculated that the radiant system was 17.5% more efficient than a conventional all-air system and that a 30% savings was achieved by using a DOAS system compared with a conventional system. The Computational Fluid Dynamics (CFD) simulation showed that a radiant system offers more uniform temperatures, as well as a better mean air temperature range, than a conventional system. To further enhance the energy savings in the radiant system, different operational strategies were analyzed based on thermal analysis using EnergyPlus. [ABSTRACT FROM AUTHOR]

Heating and cooling loads are the major reasons for energy use in buildings. Buildings are usually subject to schedules and set-points which are not optimized in response to the dynamic weather conditions, internal loads, and occupancy patterns. The thermal network model has been widely applied for real-time building load estimation, which is crucial for optimizing the operation of the HVAC system. However, there has been limited exploration of the capabilities of the thermal network model due to constraints imposed by the solution method adopted. In this paper, the exponential matrix method was adopted to simplify the state space equations and solve the thermal network model analytically. This enhances the applications of a simplified thermal network model for investigation of multiple scenarios of HVAC system operations and equipment sizing, and for more accurate estimation of heating and cooling loads. This study also proves that the analytical solution method is asymptotically stable regardless of time step. A typical office was used as a case study and the predicted building loads are compared with measured data and numerical results from EnergyPlus. For the case study, the model demonstrated better accuracy and is seen to be robust for thermal load estimation for cooling season. [ABSTRACT FROM AUTHOR]

The recast version of the Energy Performance of Buildings Directive (2010/31/EU) proposes a comparative methodology aimed at defining the energy performance of buildings “with a view to achieving cost-optimal levels”. Really, how can the cost-optimal technologies be detected? Moreover, how can the most proper packages of energy efficiency measures be chosen in order to obtain the cost-optimality? This paper would provide answers to these questions, by proposing a new methodology for the evaluation of the cost-optimality, by means of the multi-objective optimization of energy performance of buildings and indoor thermal comfort. The optimization procedure is developed by means of the coupling between MatLab and EnergyPlus, by implementing a genetic algorithm, and it allows the evaluation of profitable and feasible packages of energy efficiency measures applied to buildings. Then, following the adoption of these packages, the global cost over the lifecycle of the building is calculated, in order to identify the cost-optimal solution. After the presentation of the methodology, the developed method is proposed for the optimization of the energy retrofit of an existing building. Furthermore, the method can be applied also for new architectures, by considering reference buildings. [ABSTRACT FROM AUTHOR]

Radiant panels are known to be energy efficient sensible heating systems and a suitable fit for low-energy buildings. The main goal of this paper is to optimize the thermal insulation layers of house heated by using the radiant heating systems. This work is conducted to study four radiant systems: floor-heating system, wall-heating system, ceiling-heating system and newly developed floor-ceiling heating system located in a net-zero-energy house in Kragujevac, Serbia. The operation of these panels is simulated by software EnergyPlus. For the optimization of the optimal length of the thermal insulation software GenOpt is used. [ABSTRACT FROM AUTHOR]

In recent years, a large number of building energy consumption prediction models, with various intended uses, have been proposed. The majority of these models have either taken a data-driven or a physical modeling approach, with each approach having its own strengths and limitations. Towards leveraging the strengths and reducing the limitations of each approach for improved prediction performance, this paper presents a hybrid machine-learning approach for occupant-behavior-sensitive energy consumption prediction. The proposed approach is composed of three constituent models: (1) a machine-learning model that learns the impact of outdoor weather conditions from simulation-generated data, (2) a machine-learning model that learns the impact of occupant behavior from real data, and (3) an ensemble model that predicts cooling energy consumption based on the outputs of the first two models. The simulation-generated data were created through simulating a set of reference buildings in EnergyPlus. The real data were collected from an office building in Pennsylvania. The proposed hybrid model was validated on an unseen real dataset. It achieved 0.73 kWh RMSE and 9.07% CV in hourly cooling energy consumption prediction, which indicates that the proposed approach is promising. [ABSTRACT FROM AUTHOR]

Abstract: This paper reports investigations of energy, environmental, and economic performances of floor, wall, ceiling, and floor–ceiling heating. It is found that the floor–ceiling heating has the best performances: the lowest energy and exergy consumption, exergy destruction, CO2 emissions (compound, direct, and avoidable), and operation costs. In addition, it uses boiler of the lowest power. The worst system by all these parameters is the classical ceiling heating. The comparison of the room operative air temperatures and the operative air temperatures set by thermostats shows that all systems give satisfactory results without significant deviations. The largest deviation and the lowest values of the surrounding inner surfaces of the exterior walls are found for the floor–ceiling ceiling panel system. [Copyright &y& Elsevier]

Abstract: The possibility of storing thermal energy within the building structure to reduce building energy consumption has been studied. Hollow core panels used for a building structure are able to pass air through core cavities and be utilized as thermal storage. Previously, thermal storage models for ventilated slab system using the panels have been developed but it has typically focused on the system development and performance evaluation under particular conditions. As a result, it has been difficult to understand the effect of the system on whole building energy performance. This study develops a computational model for a ventilated slab system and investigates its implementation in a whole building energy simulation program, EnergyPlus. The system has two major components: an auxiliary air handling unit (AHU) and a radiant slab with hollow core panels. The auxiliary AHU includes an outside air mixer, coils, and a fan to produce either conditioned or unconditioned air depending on the user''s input. The air is then controlled by a defined delivery method, after passing through the cavity of the panel. This paper illustrates the new model development for the building energy simulation program and provides some evaluation with an existing model from another study. [Copyright &y& Elsevier]

Abstract: In recent years, calibrated energy modeling of residential and commercial buildings has gained importance in a retrofit-dominated market. Accurate weather data play an important role in this calibration process and projected energy savings. It would be ideal to measure weather data at the building location to capture relevant microclimate variation but this is generally considered cost-prohibitive. There are data sources publicly available with high temporal sampling rates but at relatively poor geospatial sampling locations. To overcome this limitation, there are a growing number of service providers that claim to provide real time and historical weather data necessary for building modeling at 15–40km2 grid across the globe; common variables such as temperature and precipitation have been constructed on ∼1km2 grids . Unfortunately, there is limited documentation from 3rd-party sources attesting to the accuracy of this data. This paper compares provided weather characteristics with data collected from a weather station inaccessible to the service providers. Monthly average dry bulb temperature; relative humidity; direct normal, diffuse and global solar radiation; wind speed and wind direction are statistically compared. Moreover, we ascertain the relative contribution of each weather variable and its impact on building loads. Annual simulations are performed for three different building types, including a closely monitored and automated energy efficient research building. The comparison shows that the difference for an individual variable can be as high as 90%. In addition, annual building energy consumption can vary by ±7% while monthly building loads can vary by ±40% as a function of the provided location''s weather data. [Copyright &y& Elsevier]

• A model to simulate interactions between a building and its outdoor conditions. • The model co-simulates EnergyPlus, OpenFOAM, and lumped thermal models. • The model provides proper estimates of outdoor conditions at the urban microscale. • Waste heat releases from the HVAC system can be simulated from the model. • Indirect and direct effects of green roofs can be considered by the model. This paper introduces a physically-based model to simulate interactions between a building and its outdoor conditions at the urban microscale. In the model, the building is simulated with EnergyPlus while its outdoor conditions are assessed from OpenFOAM. Furthermore, the model simulates the street pavement and surrounding buildings based on the lumped thermal theory. All components of the model are quasi-dynamically co-simulated. Through simulations of the model, waste heat releases from a cooling system can be observed with a higher resolution than this achieved by most urban microclimate models in the literature. Unlike several methods in the literature, the model evaluates a countermeasure to urban heat islands by considering direct and indirect effects simultaneously. To validate the model, measurements were collected during a field experiment in an university campus. Comparing measurements to estimates, the model seems to properly approximate the outdoor temperature and the air motion. However, a discrepancy is observed between estimates and measurements of the surface temperature. The discrepancy could be minimised if a better consideration of the net-longwave radiation was possible when co-simulating EnergyPlus. After some improvements, the model could become a support tool to mitigate urban heat islands and climate change in different regions of the world. [ABSTRACT FROM AUTHOR]

• A local sensitivity analysis of double skin facades' (DSFs) features is presented. • Simulations are carried out with two building energy simulation tools and compared. • In-built models for DSF (climate façade) are compared with experimental data. • The ranking and magnitude of sensitivity indices are similar for the two tools. • Glazing thermal and optical transmittance are the most relevant variable in DSF design. Double skin façades (DSFs) are considered façade technologies that can reduce energy use and improve occupant comfort due to their advanced features. Their design requires reliable simulations due to their complex thermophysical behaviour, which are often carried out by practitioners using building energy software (BES) tools. Using an exhaust-air façade (also called climate façade) case study, the paper analyses the sensitivity of in-built DSF models in two popular BES tools (EnergyPlus and IDA ICE) for different orientations and climates. Small variations in input variables were considered to identify the parameters that the designer should pay most attention to during the design of the DSF according to different performance indicators. The results show that, regardless of the climate or orientation, the optical properties of the system (glazing and shading) were the most important in determining its performance, followed by the thermal properties of the glazing, while the geometrical, airflow and frame characteristics were less relevant. The model validation process also showed how differences in the in-built models (i.e. the use of a capacitance node for the glazed layers) lead to a difference in the reliability of the two BES tools. [ABSTRACT FROM AUTHOR]

The occupants’ health, comfort, and productivity are important objectives for green building design and operation. However, occupant behavior also has “passive” impact on the building indoor environment by generating heat, CO 2 , and other “disturbances”. This study develops an “indirect” practical data mining approach using office appliance power consumption data to learn the occupant “passive” behavior. The method is tested in a medium office building. The average percentage of correctly classified individual behavior instances is 90.29%. The average correlation coefficient between the predicted group schedule and the ground truth is 0.94. The experimental result also shows a fairly consistent group occupancy schedule, while capturing the diversified individual behavior in using office appliances. Compared to the occupancy schedule used in the Department of Energy prototype medium office building models, the learned schedule has a 36.67–50.53% lower occupancy rate for different weekdays. The heating, ventilation, and air conditioning (HVAC) energy consumption impact of this discrepancy is investigated by simulating the prototype EnergyPlus models across 17 different climate zones. The simulation result shows that the occupancy schedules’ impact on the building HVAC energy consumption has large variations for the buildings under different climate conditions. [ABSTRACT FROM AUTHOR]