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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]

[Display omitted] • An architect-friendly rapid evaluation for the adaptive facades (AFs) is presented. • The method eliminates uncertainty in AFs selection during the early design stage. • Optimal hourly values are obtained via co-simulation with EnergyPlus and Python. • AFs strategy is characterized by climate and optimal values using a white-box model. • U-value strategy reduces consumption by up to 6.6%, SHGC&τ v strategy by 17.0%. Adaptive façades (AFs) are technologies with great potential to reduce energy consumption by changing their properties to adapt to variable climatic conditions. This paper proposes an architect-friendly computational approach for the exploration of strategies for the selection of the thermal parameters of AFs based on co-simulation and white-box models in the early design stage. In the case study, the proposed approach is applied to obtain strategies with variable U -values and variable SHGC& τ v values for high-rise office buildings in seven cities. The energy consumption analysis of five scenarios is carried out to investigate the energy-saving potential of the AFs and to propose design strategies for transparent façades, and the accuracy of the approach is verified. It is concluded that the highest match of the variable U- value strategy with the optimal hourly U- values is 85.3%, while that between the variable SHGC& τ v strategy and the optimal hourly SHGC& τ v values is only 71.0%. Regarding the energy-saving potential, the highest energy-saving rate is 6.6% for the variable U- value strategy. However, for the variable SHGC& τ v strategy, the highest energy-saving rate is greater than 10.8% in seven cities. The difference between the energy-saving rates of the proposed strategy and the optimal hourly strategy is less than 0.4%, and it is 0.8% for the AFs with variable U- values and variable SHGC& τ v values, respectively. This architect-friendly approach represents a widely used design strategy for AFs that can directly provide recommended thermal parameters based on climatic conditions, and is an effective way to demonstrate the potential application of AFs in the early design stage. [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]

[Display omitted] • The occupancy modeling approaches with a particular focus on UBEM are discussed. • Occupant-related inputs required for UBEM and the correlation between them are emphasized in the review. • Challenges in accommodating the variations in occupancy at the urban scale are reviewed. • Potential data sources for extracting building occupancy at urban scale are discussed. • UML diagram and co-simulation required to represent occupancy are discussed. Occupant-related inputs are significant parameters that influence energy simulation accuracy at both the building and urban levels. In most previous research works, fixed occupant schedules were used in urban-scale building energy modeling. The main reason is the lack of data availability to model the dynamic occupancy schedules. In recent years, urban data sets and modern estimation and detection techniques were introduced to increase the availability of occupant-related data sets. Yet, using these data to model the detailed occupancy at the urban scale is challenging and not much explored. Also, it is unclear how detailed the input regarding the occupancy should be. Addressing these research questions is the main objective of this study. This paper presents a comprehensive review of the occupant behavior (OB) modeling approaches, occupant-related input parameters with particular focus on the occupancy schedule, lighting, appliances use schedule, temperature set-point schedule, and domestic hot water usage for urban building energy modeling (UBEM). Strategies to consider the occupancy sub-models as co-simulation connecting to urban building energy simulation are discussed. Some potential datasets that could be used to derive occupant-related inputs at the urban scale are presented and highlighted. Further, the correlation between occupants' activity level, plug, and lighting loads is discussed in detail. Finally, the limitations and challenges of the occupant-related data connecting to building energy modeling are discussed, along with the research gaps and future directions of occupant-centric UBEM. [ABSTRACT FROM AUTHOR]