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• A repository on occupant-centric control and operation case studies is proposed. • A survey was developed to collect and present the data systematically. • The current database includes 58 OCC case studies with different approaches. • Results show a lack of studies on lighting and building operators. • Occupant modeling is more commonly implemented for responsive controls. Occupant-centric controls (OCC) and operations have emerged as a key concept in shifting the focus from conventional building- (or system-) centric operations to a more occupant-centric approach. Despite the potential of OCC to meet occupants' demands and bridge buildings' energy performance gap, its implementation in real-world settings has been limited. In addition, there is a lack of standardization in methodologies and terms to facilitate meaningful comparisons among case studies. Therefore, this paper aims to present a repository of OCC case studies, offering a platform for standardization and presenting key information about practical implementations of these strategies in real-world scenarios. To accomplish this, descriptors, terms, and concepts about OCC case studies were discerned through a literature review. These elements were systematically integrated into a structured survey to capture comprehensive information on OCC field study implementations. This paper provides an overview of the survey structure and insights into the current dataset, which comprises a total of 58 OCC case studies. By publishing the case study repository, we intend to establish standard categories for OCC strategies and to offer researchers and practitioners a database through which they can understand trends and possibilities for implementing OCC strategies. [ABSTRACT FROM AUTHOR]

• Metadata inclusion and threshold optimization improve state models. • State models outperform action models in predictive performance. • Sequential state models outperform non-sequential models. • The short-term past sequence does not significantly enhance action models. • Explainable AI enables local and global comparison of state and action models. Window operation significantly impacts energy use and indoor environmental quality in buildings. Individuals behave differently, making it difficult for models trained on a specific dataset to encompass the entire spectrum of these actions. A generalizable model is essential to predict the behavior of diverse occupants in office spaces. To address this need, this paper presents a systematic approach that captures this diversity, thereby contributing to developing a model towards generalizability. The approach involves state and action modeling through a Random Forest algorithm on the ASHRAE Global Occupant Behavior Database. The data pre-processing, hyperparameter tuning, and evaluation are deeply described and applied to window action and state datasets. Our results demonstrated that including metadata in a state model and applying a G-Mean threshold moving technique can result in an F 1 -score of 0.74. This score slightly outperformed the state room-wise model, which was trained only on its own dataset and achieved an F 1 -score of 0.73. However, both models had similar accuracies of 77%. The action model did not fare as well as the state models, with an F 1 -score and accuracy score of just 0.42 and 49%, respectively. In contrast, the action model showed more explainable results for domain experts than state models. [ABSTRACT FROM AUTHOR]

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]

• Data about DHW in residential buildings were collected by survey in Southern Italy. • Contextual and personal factors influencing DHW usage were considered. • Descriptive statistics were performed to characterize DHW by climatic zone. • Inferential statistics were conducted to discover DHW usage patterns. • Four clusters of dwellings were identified considering DHW daily usage hours. In European efficient buildings, the share of energy use for DHW is a significant percentage of the heat requirement. The evaluation of DHW energy demand and consumption patterns has been neglected in the past and studies are located in Northern countries. This paper presents a survey conducted in Southern Italy. Descriptive statistics were performed to characterize DHW production by climatic zone. Inferential statistics were conducted to discover significant contextual and personal variables and identify usage groups. The most used energy source was biomass (49.3%), followed by methane gas (37.7%). Fireplaces were installed in the coldest zone, in detached houses, more in small municipalities (36%), and low-income families (32%). Solar systems were in 8% of dwellings having income higher than 28000€. Four clusters of dwellings were identified considering the daily usage hours. The eldest and less numerous households fell into the cluster with the lowest usage (3.5 h). Households with a percentage <25% of unemployed components had more probability to belong to clusters with more DHW usages. The paper contributes to fill the investigation in the Mediterranean area and provides a systematic approach to expand the limited knowledge about the influencing factors on the DHW production and usage. [ABSTRACT FROM AUTHOR]

• Introduced a new paradigm for energy-aware communities. • Presented a new a sociotechnical modeling approach based on utility theory. • Inferred attributes affecting the thermostat responses of each household during an eco-feedback intervention. • Quantified the impact of the eco-feedback on households' thermostat-adjustment behavior. Heating and cooling (HC) energy use is responsible for about 42% of the total annual energy consumption of the average household in the U.S and it is significantly affected by residents' energy-related behavior. In this paper, our goal is to realize a new paradigm for energy-aware communities that leverages smart eco-feedback devices and social games to engage residents in understanding and reducing their home HC energy use. Towards this goal, we present a new sociotechnical modeling approach based on utility theory to reveal causal effects in human decision-making and infer attributes affecting the thermostat adjustment behavior of each household during an eco-feedback intervention. Our approach 1) is based on a utility model that quantifies residents' preferences over indoor temperatures given decision attributes related to their thermal environment and eco-feedback design and 2) incorporates latent parameters that determine the unique behavioral characteristics of each household. For parameter learning, we develop a hierarchical Bayesian model with non-centered parameterization calibrated using field data collected from a multi-unit residential community located in Fort Wayne, IN. The dataset comprises two parts; a baseline period without any behavioral intervention, and an intervention period, where personalized eco-feedback and social games are deployed through resident engagement devices with smart thermostat control capabilities, including a wall-mounted tablet and smart speaker. Through the model calibration, we quantify the impact of the eco-feedback on households' thermostat-adjustment behaviors. We propose that the utility model developed in this work can serve as the foundation for analyzing resident behavior in connected residential communities with eco-feedback energy-saving programs. [ABSTRACT FROM AUTHOR]

One of the main required actions for moving forward in zero-carbon cities' mission is to increase building energy efficiency. Building energy simulations are key tools for HVAC control, building energy management, testing different building energy efficiency scenarios, and providing feedback to the occupants. Unfortunately, these tools have a significant weakness in occupant behavior modeling and prediction. Several recent studies used different occupancy measurements and methods to predict occupant behavior. However, they suffer from threatening the occupant's privacy and the high cost of their infrastructure and deployment. This paper aims to leverage passive WiFi sensing methods for occupant behavior estimation using commercial off-the-shelf WiFi devices, for which the infrastructure is already available in many buildings. Passive and device-free WiFi sensing does not threaten the occupant's privacy, and its deployment cost is low. Therefore, this method of occupancy measurement has the potential to be used on an urban scale and to play an essential role in zero carbon and smart cities in the future. Different machine learning algorithms have been applied to the collected, denoised, and preprocessed Channel State Information (CSI) data to estimate the occupant behavior. Five schedules of occupancy, occupant activity, lighting, electrical equipment usage, and thermostat set point temperature could be extracted and estimated from CSI data. In the last step, all estimated schedules were fed into EnergyPlus, an energy simulation software, along with other required data for accurate energy demand calculation to estimate heating and cooling demand, which can be used for HVAC control and occupancy feedback. [ABSTRACT FROM AUTHOR]

In recent years, a large number of building energy consumption prediction models, with various intended uses, have been proposed. The majority of these models have either taken a data-driven or a physical modeling approach, with each approach having its own strengths and limitations. Towards leveraging the strengths and reducing the limitations of each approach for improved prediction performance, this paper presents a hybrid machine-learning approach for occupant-behavior-sensitive energy consumption prediction. The proposed approach is composed of three constituent models: (1) a machine-learning model that learns the impact of outdoor weather conditions from simulation-generated data, (2) a machine-learning model that learns the impact of occupant behavior from real data, and (3) an ensemble model that predicts cooling energy consumption based on the outputs of the first two models. The simulation-generated data were created through simulating a set of reference buildings in EnergyPlus. The real data were collected from an office building in Pennsylvania. The proposed hybrid model was validated on an unseen real dataset. It achieved 0.73 kWh RMSE and 9.07% CV in hourly cooling energy consumption prediction, which indicates that the proposed approach is promising. [ABSTRACT FROM AUTHOR]

• Propose an occupant thermal preference-aware load scheduler for resilient communities. • Quantify the impact of occupant thermostat changing behavior on resilient community optimal scheduling through selected key resilience indicators. • Adopt the chance-constrained method for addressing the occupant behavior uncertainty in the community optimal control problem. • Provide a case study for controller performance evaluation on a virtual community tesebed. The load scheduling of resilient communities in the islanded mode is subject to many uncertainties such as weather forecast errors and occupant behavior stochasticity. To date, it remains unclear how occupant preferences affect the effectiveness of the load scheduling of resilient communities. This paper proposes an occupant preference-aware load scheduler for resilient communities operating in the islanded mode. The load scheduling framework is formulated as a model predictive control problem. Based on this framework, a deterministic load scheduler is adopted as the baseline. Then, a chance-constrained scheduler is proposed to address the occupant-induced uncertainty in room temperature setpoints. Key resilience indicators are selected to quantify the impacts of the uncertainties on community load scheduling. Finally, the proposed preference-aware scheduler is compared with the deterministic scheduler on a virtual testbed based on a real-world net-zero energy community in Florida, USA. Results show that the proposed scheduler performs better in terms of serving the occupants' thermal preference and reducing the required battery size, given the presence of the assumed stochastic occupant behavior. This work indicates that it is necessary to consider the stochasticity of occupant behavior when designing optimal load schedulers for resilient communities. [ABSTRACT FROM AUTHOR]