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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]

Abstract: Many systems used in buildings for heating, ventilating, and air-conditioning waste energy because of the way they are operated or controlled. This paper explores the application of model predictive control (MPC) to air-conditioning units and demonstrates that the closed-loop performance and energy efficiency can be improved over conventional approaches. This work focuses on the problem of controlling the vapor compression cycle (VCC) in an air-conditioning system, containing refrigerant which is used to provide cooling. The VCC considered in this work has two manipulated variables that affect operation: compressor speed and the position of an electronic expansion valve. The system is subject to constraints, such as the range of permissible superheat, and also needs to regulate temperature variables to set points. An MPC strategy is developed for this type of system based on linear models identified from data obtained from a first-principles model of the VCC. The MPC strategy incorporates economic measures in the objective function as well as control objectives. Tests are carried out on a simulated VCC system that is linked to a simulation of a realistic building that is developed in the U.S. Department of Energy Computer Simulation Program, EnergyPlus. The MPC demonstrated significantly better tracking control relative to conventional approaches (a reduction of 70% in terms of the integral of squared error for step changes in the temperature set-point), while reducing the VCC energy requirements by 16%. The paper describes the control approach in detail and presents results from the tests. [Copyright &y& Elsevier]

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]