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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]

Abstract: Recent results show that a predictive building automation can be used to operate buildings in an energy and cost effective manner with only a small retrofitting requirements. In this approach, the dynamic models are of crucial importance. As industrial experience has shown, modeling is the most time-demanding and costly part of the automation process. Many papers devoted to this topic actually deal with modeling of building subsystems. Although some papers identify a building as a complex system, the provided models are usually simple two-zones models, or extremely detailed models resulting from the use of building simulation software packages. These are, however, not suitable for predictive control. The objective of this paper is to share the years-long experience of the authors in building modeling intended for predictive control of the building''s climate. We provide an overview of identification methods for buildings and analyze their applicability for subsequent predictive control. Moreover, we propose a new methodology to obtain a model suitable for the use in a predictive control framework combining the building energy performance simulation tools and statistical identification. The procedure is based on the so-called co-simulation that has appeared recently as a feature of various building simulation software packages. [Copyright &y& Elsevier]

• 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]

Facade integrated ventilation systems have the potential to improve local air quality and occupant comfort, reduce building energy consumption and provide economic retrofit opportunities. However, existing modelling and evaluation methods are not able to sufficiently address the technology's potential capabilities. In this paper, a new model is developed specifically adapted to facade-integrated air handling units interacting with ventilated fenestration systems. The new modelling approach is based on the co-simulation of two different models through a Functional Mock-Up Unit (FMU) Interface. An airflow network model of the air handling unit is developed in Modelica and exported as an FMU based on this specification. The FMU is then simulated within an updated version of the building simulation program Fener that includes a physical model that calculates the window heat fluxes and temperatures, as well as the window gap air outlet temperature in case of ventilated facades. The different sub-models are first validated versus experimental data from a test chamber in Villafranca di Verona, Italy. Then, the full co-simulation approach is applied to a case study in which different control strategies for the fenestration system are investigated. [ABSTRACT FROM AUTHOR]

In ultra-low temperature district heating the supply temperature is less than required to heat the domestic hot water and a heat pump is therefore often proposed to raise the temperature. This paper investigates how this heat pump can be utilized for price based demand response to induce peak reductions and energy cost savings. A model predictive control strategy is proposed and evaluated through co-simulations where a model predictive controller is formulated in MATLAB and connected to an EnergyPlus hot water storage tank. It is demonstrated that the system is capable of reducing the district heating morning peak and the electric grid evening peak as well as providing energy cost savings for the end-user without compromising hygiene and comfort. [ABSTRACT FROM AUTHOR]

This paper presents a parametric study on the effect of four green-roof types on outdoor/indoor temperature and cooling demand under four different climates and three urban densities using co-simulation approach with ENVI-met and EnergyPlus. Results reveal an outdoor nighttime warming effect of not more than 0.2 °C which is most obvious with the semi-extensive while the outdoor and indoor cooling effect ranges between 0.05–0.6 °C and 0.4–1.4 °C, respectively depending on the green-roof type, urban density and time of the day. These daytime temperature reductions also vary per prevailing climates and follow this order: hot-dry, hot (or warm)-humid, and temperate which can be explained by the interplay between solar intensity/air temperature and relative humidity between the regions. In a hot-humid region, the evaporative cooling potential of greenery is dampened when compared to hot-dry region. This is also true for region with low solar intensity and humidity like the temperate region. In terms of cooling demand reduction, 5.2% was observed in hot-dry climate on the hottest day of the year with full-intensive green-roof while the least saving of 0.1% was found with semi-extensive green-roof in temperate climate. In general, for both outdoor temperature and cooling demand reduction, semi-intensive green-roof was found more effective than its full-extensive counterpart while the higher spatial green-roof is most important for indoor temperature reduction irrespective of the leaf density of the greenery. Therefore, the intent of green-roof installation should be a determining factor for the type and spatial extent to be implemented. [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]

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]

This paper introduces a Human and Building Interaction Toolkit (HABIT) for simulating the thermally adaptive behaviors and comfort of office occupants alongside building energy consumption. The toolkit uses the Building Controls Virtual Test Bed (BCVTB) to co-simulate a field-tested, agent-based behavior model with an EnergyPlus medium office model. The usefulness of the toolkit is demonstrated through a series of zone and building-level case study simulations that examine the wisdom of pairing local heating and cooling options with strategic thermostat set point offsets, judging from the energy, Indoor Environmental Quality (IEQ), and cost perspectives. Results generally suggest that trading efficient local heating/cooling options for whole space conditioning has both energy and comfort benefits, saving up to 28% of monthly HVAC energy while improving the acceptability of thermal conditions in a Philadelphia climate. Nevertheless, cost analysis shows that the fuel source of conserved energy must be considered – particularly in the case of personal heater use, which adds to electric plug loads and associated utility and CO 2 emissions cost penalties. Moreover, costs from even small changes in simulated occupant productivity tend to overwhelm energy costs, suggesting the need to improve the accuracy and precision of available productivity models across multiple seasons and climates. [ABSTRACT FROM AUTHOR]

Innovative and efficient ways to carry out numerical simulations are worth of investigation to reduce the computational complexity of building models and make it possible to solve complex problems. This paper presents a reduced order model, based on Proper Generalised Decomposition (PGD), to assess 2-dimensional heat and moisture transfer in walls. This model is associated with the multizone model Domus using an indirect coupling method. Both models are co-simulated to perform whole-building hygrothermal simulation, considering 2D transfer in walls. The whole-building model is first validated with data from the IEA Annex 41. Then, a case study is considered taking into account a 2-zones building with an intermediary shared wall modelled in 2 dimensions to illustrate the importance of the technique to analyse the hygrothermal behaviour of the wall. It has been highlighted that the whole model enables to perform more precisely analyses such as mould growth on the internal surface. In addition, important theoretical numerical savings (90%) are observed when compared to the large original model. However, the effective numerical savings are not so important (40%) due to the limitations of the co-simulation method. [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]

[Display omitted] Based on the data from U.S. Energy Information Administration (EIA), the total annual energy consumed by buildings in the United States has increased by 325% over the past 70 years. Many commercial buildings utilize a building management system (BMS) and occupancy sensors to better control heating, ventilation, and air conditioning (HVAC) systems. However, the complex and costly installation process of occupancy sensors prolongs the return on investment for the residential sector. This paper presents a cost-effective approach to occupancy detection utilizing a two-layer detection scheme based on data obtained from multiple non-intrusive sensors (temperature and motion). The sensor data were consumed by multiple heuristic models (lower layer) for recognizing a set of human activities (door handle touch, water usage, and motion near the door area). As non-intrusive sensors, such as temperature sensors, may lead to less accurate occupancy information, a data fusion scheme using machine learning (upper layer) is utilized to holistically validate any individual sensor. The proposed two-layer methodology enhances the validity and reliability of occupancy detection. The human activities data was used to train and test four machine learning models (Random Forest, Decision Tree, K-Nearest Neighbor, and Support Vector Machine). The proposed occupancy detection system was installed in a 62 m2 living lab. Four temperature sensors and one motion sensor were used to collect the environmental information for 54 days. The validity of the proposed detection system was verified by the accuracy and the F1-score of each model. In all machine learning models, the two-layer detection system showed significant improvements to the accuracy and the F1-score over the current state-of-the-art approach with the same data. As such, the proposed work demonstrated similar or improved level of the accuracy (95%) and F1-score (95%) over other works, while using reduced sensor density. [ABSTRACT FROM AUTHOR]