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• 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]

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]

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]

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]