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Highlight • New on/off controller for residential applications validated using minute-level field data. • On/off controller improves the HVAC accuracy around 19% in terms of NMBE. • On/off cycling makes the operation and associated energy use more realistic. Abstract Heating, ventilation, and air-conditioning (HVAC) systems are sized using design conditions, conditions that occur only a small percentage of time each year. As a result, HVAC systems frequently operate at part-load conditions since their capacity is larger than necessary except in the most extreme weather conditions. Most HVAC systems for residential and small commercial buildings in the U.S. use on/off controls, however, most energy modeling software tools do not simulate the on/off nature of this type of HVAC controls. This paper presents the development and validation of an on/off controller for residential applications in EnergyPlus using a custom EMS (energy management system). This controller is validated using minute-level field data collected for a house located in Sacramento, California with simulated occupancy and internal loads. The results obtained from EnergyPlus with and without the use of the developed on/off controller are also compared. The application of the developed on/off controller improves the HVAC energy use results accuracy around 19% in terms of the Normalized Mean Bias Error (NMBE) at the one-minute level compared to the results without the application of the on/off controller. It also makes the nature of operation and associated energy use signal of the HVAC system more realistic in terms of the on/off nature of the residential direct expansion coils. Furthermore, in this paper, the accuracy improvement of the results dominated by the internal loads is investigated by applying the one-minute schedule compared to the usual hourly schedules for the internal thermal loads. [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]

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]

Nowadays, energy simulation in many respects assists researchers and engineers in the design phase, as well as in the buildings operational phase. Reliability of the integrated simulation model and the knowledge and experience of the user are the cornerstones of good quality simulation prediction result. This study was preceded by a detailed and long-term experimental analysis of the building with a double-skin ventilated façade [10] . The paper presents results of the measurement, fine-tuning process and validation of the numerical simulation model. The simulation software tool, EnergyPlus in combination with Airflow Network Algorithm algorithm was used for the cavity air temperature and velocity prediction. When the model was verified, the criterion of eligibility were defined with the recommended statistical indicators. In order to achieve the better assessment, the validation process was done trifold: for winter, transitional and summer season. Overall, the research results highlight a very good agreement and a high level of matching between measured values and simulated results, which is a confirmation of the applied energy simulation tool. [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]

This paper demonstrates the use of benchmarking methods for residential buildings in an energy rich economy. Three bottom-up methods were used, ordinary least square (OLS), support vector machines (SVM), and engineering modelling (EM). EM highlighted the energy inefficiency in these buildings, where the EM simulations showed the potential of halving the average measured consumption of 20.5 MWh/year. SVM could demarcate buildings and households with consumption of >16.0 MWh/year which are considered to be inefficiency. OLS is simple to use and the non-linear least square, showing R 2 > 0.8, performs better than linear least square fit. This work allows two EUI benchmark measures to be determined and they have average values of 2035 kWh/person/year and 56 kWh/m 2 /year. [ABSTRACT FROM AUTHOR]

Abstract: The evaluation of building energy consumption usually requires building energy profiles on an hourly basis. Computer simulations can be used to obtain this information but generating simulations requires a significant amount of experience, time, and effort to enter detailed building parameters. This paper presents a simple methodology to estimate hourly electrical and fuel energy consumption of a building by applying a series of predetermined coefficients to the monthly energy consumption data from electrical and fuel utility bills. The advantage of having predetermined coefficients is that it relieves the user from the burden of performing a detailed dynamic simulation of the building. The coefficients provided to the user are obtained by running EnergyPlus Benchmark Models simulations; thus, the simulation process is transparent to the user. The methodology has been applied to a hypothetical building placed both in Atlanta, GA, and in Meridian, MS, and in both cases, errors obtained for the estimated hourly energy consumption are mainly within 10%. [ABSTRACT FROM AUTHOR]