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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]

Shadow effect among buildings has great impacts on the building energy consumption and the performance of building photovoltaic, and calculating shadows on building surfaces in urban building energy modeling (UBEM) faces challenges of inefficiency, especially for dense urban areas. In this study, a novel shadow calculation approach based on sunlight channel is proposed that can streamline the surrounding environment and accelerate the shadow calculation process. The sunlight-channel algorithm can further accelerate the shadow calculation process by dynamically predetermining the shading surfaces according to the actual solar position. In a real urban context, the proposed approach can accelerate the computation process by over 10 times over the baseline and over 34 times over the non-accelerated method, with a mean absolute percentage error (MAPE) of 1.13% for the total solar radiation. The proposed approach copes well with both large-scale urban models and the complexity of building structures, particularly for urban models with complex changes in building heights. This approach can significantly enhance the computational efficiency in complex urban environments, facilitating an accurate and rapid analysis of the energy consumption and solar potential of buildings in dense cities. • Sunlight channel algorithm was proposed to screen the shading surfaces dynamically. • A rapid method for shadow calculation for complex urban environments was proposed. • 13 neighborhoods with different spatial scale and height variation were compared. • Accelerate by over 34 times over the non-accelerated method, with 98.8% accuracy. [ABSTRACT FROM AUTHOR]

• Structural acceleration of the shadow calculation was realized using a multithreaded framework. • The multithreaded shadow calculation framework can significantly shorten the calculation time. • The proposed shadow calculation method can achieve the same calculation accuracy. • The acceleration effect of multithreaded computing relates to the number of calculated surfaces. • For city-scale building shadow simulation, the calculation time is only 5.4% of the original time. Solar radiation significantly affects the thermal processes of buildings; thus, various building energy modeling programs have established corresponding shadow calculation modules. However, the large number of projection surfaces in a building requires extensive calculations, making it difficult to rapidly analyze the building shadow while ensuring hourly calculations. This study proposes an accelerated calculation framework for building shadows based on multithreaded parallel computing for an accurate building energy consumption analysis. Based on the basic principle of shadow projection calculation, the embedded, integrated development of a shadow calculation module is proposed, and a full-performance simulation platform kernel based on a multithreaded structure is constructed. Results show that the multithreaded shadow calculation framework can significantly shorten the calculation time. Based on case studies, a typical complex single building's shadow calculation speed can be increased by nearly 2.4 times, whereas for city-scale building shadow simulations, it is only 5.4% of the original duration. [ABSTRACT FROM AUTHOR]

Building thermal process simulation is an effective technique for quantitatively analyzing building energy consumption. The finite difference method is commonly used for structuring heat transfer equations due to its generality. However, the state-space method is preferred in BPS tools due to its computational efficiency. As building simulation analysis evolves, there is a need for high-performance computation to simulate large-scale constructions. This study proposes a new computing architecture for optimizing the state-space method in BPS tools, focusing on both personal computers and server clusters. The simulation procedure is parallelized at different stages while maintaining accuracy. Two case studies validate the feasibility of the proposed computing architectures. Compared to the original serial architecture, the parallel architecture achieves an acceleration of 2.12–2.33 times on a PC and 5.68–17.24 times on a server cluster. This significant acceleration potential demonstrates the power of parallelization in maximizing computing power. [ABSTRACT FROM AUTHOR]

• Unscheduled simulation of the occupant behaviour in buildings. • Simulation of individual occupant behaviour by the agent-based modelling. • Support for the personalising of the designed building space. • Possibility to investigate individuals comfort during building design phase. Simulation of occupant behaviour (OB) in buildings is a challenging task. Available software uses a broad spectrum of tools that try to reproduce the patterns of human activity. From building energy perspective, the main emphasis in research has been focused on discovering behaviour directly related to energy. In recent years, more attention has been given to simulating occupant actions that are indirectly influencing building energy use. In most of the cases, this is achieved with the use of agent-based modelling which allows describing occupant actions on a room level. According to the existing methodologies review, it is a proper step, but to include occupant behaviour in energy simulations, spatial and temporal resolution of the occupant behaviour model must be improved. Addressing this issue requires the development of a comprehensive model supported by numerous modules that would cover various significant occupant actions. This paper focuses on the development of the high-resolution, data-driven movement engine of occupants. It is one of the fundamental modules necessary to simulate occupant behaviour with high granularity. Once the model is developed within its essential functionalities, it will deliver a bottom-up model capable of testing various energy use strategies. It will allow for testing different heat, ventilation and air conditioning solutions and the responses provided by simulated occupants. The data used to develop this module was obtained thru in-situ measurements, with the use of depth registration. Information obtained from experiments is similar to previous research, but it also extends the investigation scope with an additional transition-based variable. [ABSTRACT FROM AUTHOR]

Occupant behavior is a major contributing factor to building energy consumption. Large discrepancies might occur when occupants with diverse types in terms of behavior patterns reside in an identical building. Moreover, it is vital to derive several kinds of typical occupant behavior that represent the assemblage of occupants in a simplified way as the reference for building designers and energy policy makers. This paper presents a preliminary research on occupant behavior categorization through the building energy simulation, where some typical occupant air-conditioning behavior patterns use in Chengdu, China have been derived from a large-scale questionnaire survey based on the energy consumption levels. These typical occupant behavior patterns could be used as the context for an overview of building energy in a community, as well as the evaluation of building energy-saving technology where occupants are involved. Further efforts are required in the algorithm of dividing occupant behavior into typical patterns, the validation of the classification results, and the analysis of other behaviors. [ABSTRACT FROM AUTHOR]

• A built-in numerical method of radiative cooling in building simulation is raised. • Detailed experimental validation of the integration method is performed. • Energy saving of radiative cooling in a strip mall in six climates is analyzed. • A generalized and easily-usable simulation tool with built-in LRSIM is provided. Besides highly reflecting solar energy, the spectrum-selective radiative cooling materials (RCMs) dissipate heat to outer space especially through the atmospheric window (e.g., 8–13 μm) and achieve sub-ambient temperature under direct sunlight. Not only potentially contributing to Earth's heat rebalance, the daytime radiative cooling technology but also presents promising applications to building facades as a passive 'zero-energy' cooling method. Although there has been a large amount of radiative cooling materials developed recently due to the wavelength scale photonic material design, the wide application of radiative cooling materials to buildings faces challenges, including the barriers of scalable manufacturing and the lack of industry acceptable simulation tools for designers and engineers. Deliberating the major impacting factors such as moisture variation in atmosphere, this study designed a systematic strategy of incorporating spectrum-selective radiative cooling with whole-building performance simulation by developing a new long-wavelength radiation spectrum integral module (LRSIM) suitable for being built into the state-space model, like the DeST tool. Reduced-scale controllable experiments have validated the accuracy of LRSIM, giving < 3% deviation for the spectrum-based long-wavelength radiation calculation. Subsequently, experimental results from full-scale model houses were utilized to further validate the DeST tool embedded with LRSIM for building application of spectrum-selective radiative cooling materials. Indoor air temperature deviation < 1 °C was observed between the simulated and measured data, indicating the compelling prediction power of the integrated tool for radiative cooling modeling. Simulation analysis to a single-story commercial building at six locations around the world has shown energy-saving potential of 14–42% for an example radiative cooling material. Different from other customized plug-in integration, the experimentally validated DeST with built-in LRSIM will provide architecture designers and application engineers a general-purpose and easily-usable tool for promoting adoption of radiative cooling technology in buildings. [ABSTRACT FROM AUTHOR]

[Display omitted] • Occupancy forecasting in buildings is essential for operation and energy management. • By integrating temporal-sequential analysis and machine learning method, the proposed model outperforms other baseline models. • The consideration of weekly routine patterns by the TS-week-ANN model possessed better robustness and generalizability. Building occupancy is the basis for building energy simulations, operations, and management. With the increasing need for energy conservation and the occupant-centric service of building energy systems, occupancy forecasting has become an essential input for simulations. These applications include model predictive control and demand response, with the potential to optimize the use of renewable energy sources. Based on recent research, occupancy forecasting tends to be based on occupancy data of the previous time step or continuous lagged dependent variables. Previous analysis demonstrated that building occupancy is innately temporal and sequential with seasonal features, which may be instructive for forecast research. This study proposes a temporal-sequential (TS) analysis and machine learning integrated approach for occupancy forecasting. Using hourly occupant data from 16 different buildings, we demonstrate that the proposed temporal-sequential analysis using a 1-week seasonal period with an artificial neural network structure (TS-week-ANN) outperforms other baseline methods. [ABSTRACT FROM AUTHOR]