Occupancy Schedules Research Articles

Redefining realistic and stochastic occupancy schedules and patterns for residential buildings in Jordan

Occupant behavior significantly influences building energy consumption, underscoring the importance of understanding the interplay between energy usage and occupant behavior. This integration is vital for evaluating how occupants and their interactions impact building energy consumption patterns. Despite widespread recognition of the importance of behavior-centric approaches in achieving sustainable architecture, there remains a gap in understanding human behavior, particularly in performance-based design. This study presents realistic and stochastic occupancy schedules for residential buildings in Jordan. This was achieved using a mixed-method approach involving a survey, data classification, clustering processes, and a co-simulation approach combining Rhino Grasshopper and Python. The results contributed to refining the general characteristics of each cluster within different age groups, encompassing various patterns of interaction, tolerance levels or levels of sensing, energy conservation consciousness, and occupancy patterns. A significant correlation was also observed between age groups, occupancy schedules, and energy consumption behaviors. The simulation also evaluated the effects of stochastic behavior patterns on the prediction of the energy performance of residential buildings. The results confirm the significant variation in energy use intensity levels between realistic occupancy schedules and fixed schedules for residential buildings. The results also prove that using realistic occupancy schedules for each age group significantly affects energy consumption behavior through occupant-based behaviors. This confirms the pivotal role of dynamic schedules and occupant behaviors in understanding and shaping energy consumption patterns and realistically determining the efficiency of building systems.

Assessing the Influence of Occupancy Factors on Energy Performance in US Small Office Buildings

Office buildings are responsible for about 35% of the total electricity in the US and over 70% of building energy consumption occurs during occupancy periods. Therefore, understanding occupancy behavior is crucial for reducing building energy consumption. However, given the stochastic nature of occupant behavior, identifying which occupancy parameters have the most impact on energy consumption poses a considerable challenge. This study aims to investigate and quantify the impact of various occupancy parameters on the energy performance of a US small-sized office building using an EnergyPlus-based nationwide energy simulation. First, dynamic occupancy schedules are created based on different occupancy parameters using an agent-based model. Next, the generated dynamic occupancy schedules are integrated into a small office building model from the Department of Energy’s prototypes. This creates a dataset of occupancy parameters and building energy performance across various climate zones. Finally, various feature selection and statistical analysis methods are applied to the generated dataset. This helps identify significant occupancy parameters and quantify their impact on building energy performance across different climate zones. According to the results of the study, buildings located in cool marine, mixed marine, and warm marine climate zones had lower total energy consumption compared to other zones. Additionally, feature selection methods identified “Occupant Density” as the primary significant variable impacting energy consumption, across all climate zones. These findings offer valuable insights into the influential occupancy parameters across various climate zones, highlighting the importance of tailoring occupancy schedules to enhance energy efficiency. They provide practical guidance that can be directly applied to optimize energy consumption and achieve significant energy savings in small office settings with different weather conditions.

Impact of space utilization and work time flexibility on energy performance of office buildings

This study investigates the impact of different space utilization on energy use intensity, heating load, cooling load, and thermal comfort of occupants using a combination of empirical data gathering and simulation-based studies. The increasing worldwide preoccupation with energy consumption and environmental sustainability has led to increased attention on optimizing interior spaces to mitigate total energy demand. The considered case study for conducting this research was the Norwegian living lab namely Zero Emission Building (ZEB) Flexible Lab in Trondheim, Norway. In this study, 10 different scenarios of occupancy schedules based on flexible arrangements from standard workweeks to extensive remote work configurations were designed and analyzed using IDA ICE 5.0. The findings demonstrate significant reductions in energy use across scenarios with increased teleworking and compressed work weeks. The remote scenario achieved the most significant decrease in Energy Use Intensity (EUI), with a reduction of 46 % compared to the base case. Similarly, the implementation of flexible hours and remote working in scenarios resulted in a reduction of electric heating demand by up to 23 %, underscoring the potential of occupancy-based strategies in enhancing building energy efficiency. However, uncomfortable hours increased by 59 % in the 2-day remote working scenario compared to the base case, demonstrating the need to consider climate conditions when implementing remote work. The research offers valuable insights into the complex connections between flexible arrangements and energy efficiency, considering many elements such as occupancy schedule and use dynamics. This article offers a comprehensive analysis that may give architects, building managers, and policymakers valuable insights. This study contributes to the developing sustainable architecture by emphasizing the impact of dynamic occupancy on the energy performance of office buildings.

The role of passive, active, and operational parameters in the relationship between urban heat island effect (UHI) and building energy consumption

The energy performance of buildings located in urban areas is strongly influenced by the UHI phenomenon, which usually leads to higher cooling energy consumption and lower heating energy consumption. Despite the known effects of UHI on building energy consumption, there is a lack of detailed knowledge on the role of building design and operational parameters in determining the effects of UHI on building energy use. To address this knowledge gap, this study analyzes the impacts of UHI on annual, heating, and cooling energy consumption of 16 prototype buildings located in 16 climate zones in the United States. The objective is to determine the relative relevance of building types (e.g., residential, commercial, healthcare facilities etc.), occupancy parameters (e.g., occupancy schedule) and the Heating, Ventilation and Air Conditioning (HVAC) systems in governing the impact of UHI on the building energy use matrices. Additionally, the thermal properties of the building envelope were evaluated to determine their relative importance in mitigating the effects of UHI on building energy performance. We found that, among the studies building types, while the cooling energy use of restaurant and outpatient healthcare buildings was most affected by UHI (higher cooling energy demands), the outpatient healthcare buildings were most impacted by UHI in terms of their heating energy use (lower heating energy use). The selection of HVAC systems type was found to have negligible influence on the energy impacts of UHI. Lastly, with regard to the thermal properties of the building envelope, window insulation was noted to be the most influential thermal property, followed by roof and wall insulation. Also, the role of insulation as well as other thermal properties was higher in the context of UHI’s impact on heating energy consumption as compared to cooling energy consumption. Not only are the results of this study useful for urban planning purposes to determine the vulnerability of different buildings to UHI, but they also are beneficial to UHI-resilient building designs.

Infrared signal-based implementation of model-based predictive control (MPC) for cost saving in a campus building

Model-based predictive control (MPC) is an advanced control method that is used to achieve energy and cost savings in building energy management. However, there are various challenges to implementing MPC in actual buildings, such as additional engineering costs, physical damage to systems, and security concerns. This study proposes a non-intrusive implementation method for MPC based on a low-cost communication system. Firstly, a grey-box building model was developed along with the heating, ventilation, and air conditioning (HVAC) models based on the actual measurements of the test building. A simulation case study of MPC was performed, along with baseline feedback control, using these constructed models. The MPC outperformed the feedback control in terms of comfort, electricity consumption, and cost. It was implemented in an actual test zone to demonstrate the feasibility of a low-cost and non-intrusive implementation method that was realized using Arduino-based infrared signal communication and to evaluate the cost-saving potential of the MPC when compared with the baseline control. This experiment was conducted under a confined schedule for occupancy and set-point temperature for three days for the MPC and two days for the baseline feedback control in the cooling season. The cost savings of the MPC are as high as 5.8 %. Additional feedback cases were considered with a non-confined occupancy schedule and set-point temperature. The resulting energy consumption and cost savings are 25.2 % and 33.7 %, respectively. In this study, the applicability of the Arduino-based low-cost and non-intrusive implementation method for MPC was demonstrated, and the actual saving percentage of the MPC was compared with the conventional control method in a real building.

Developing a residential occupancy schedule generator based on smart thermostat data

Occupancy patterns play a major role in residential buildings’ energy demand. This role is essential to be represented realistically in urban-scale energy simulations with a focus on matching the supply of renewable energy to demand. However, the lack of large-scale datasets that represent the seasonality and dynamic nature of occupancy, especially within the residential sector limited such analysis. Recently, the fast adoption of smart thermostats, featuring passive infrared sensors for motion detection, in residential buildings has allowed for extracting more representative occupancy information for different applications. To this end, this study introduces an open-source Python package to generate large-scale hourly occupancy profiles for residential buildings based on smart thermostat readings. The package takes advantage of the Donate Your Data (DYD) dataset by Ecobee to develop a rule-based framework that addresses the limitations of relying on motion-detection data to represent the whole-building occupancy. The framework was applied to over 8000 Canadian households as a case study. The generated profiles for these buildings are validated by comparing them with residential occupancy profiles generated from the Canadian Time Use Survey (TUS). The results showed that both profiles were statistically similar with a 3 % difference in the aggregated daily occupied hours. Finally, the diversity of the generated profiles before and after the COVID-19 pandemic is investigated to demonstrate the usefulness of the tool. The results proved the potential of the developed package to generate realistic and diverse occupancy schedules for the residential sector.

Non-intrusive thermal load disaggregation and forecasting for effective HVAC systems

Non-Intrusive Thermal Load Monitoring (NITLM) tracks the sub-loads generated by each heat source (e.g. occupants, equipment, solar radiation etc.) from the total thermal load and indirectly provides a room’s thermal properties without additional sensors. Since sub-loads can improve the efficiency of HVAC systems, NITLM is a very attractive technology for building energy management. NITLM has traditionally focused on analyzing past and present sub-loads. However, by forecasting future sub-loads, HVAC systems will be able to schedule operations that take into account the thermal properties of future rooms. This work focuses on a new NITLM framework that forecasts future sub-loads based on the current and past total thermal loads. In experiments, we selected occupant loads that are closely connected to HVAC systems and performed sub-load forecasting using two types of approaches. One is a two-step approach that separately performs them in turn. This approach use separately trained model for disaggregation and forecasting, this allow us to fine-tuning the hyper-parameter for dedicate model. Moreover, the two-step approach can take into account the different properties and difficulties of each inference, resulting in smaller errors in sub-load forecasting. The other is an integrated approach. This approach combines load disaggregation and forecasting into a single estimation process, eliminating error propagation and reducing overall error in sub-load forecasting. Moreover, this approach utilizes the Adaptive Moment Estimation (Adam) algorithm for effective parameter optimization, enabling complex training and improving the accuracy of sub-load forecasting. We conducted evaluations of thermal load disaggregation and forecasting across a range of realistic building scenarios. The findings indicate that the integrated approach predicts sub-loads with a MAE that is up to 34.9% lower than that of the two-step approach. Additionally, it identifies occupants presence with an 18.5% higher F-score. This demonstrates its enhanced suitability for accurately predicting sub-loads and estimating future occupancy schedules.

Energy-efficiency oriented occupancy space optimization in buildings: A data-driven approach based on multi-sensor fusion considering behavior-environment integration

Buildings contribute significantly to global energy consumption. Optimizing internal building space layout is an essential approach for reducing energy consumption. However, proactively improving energy efficiency by building space design is still challenging requiring comprehensive consideration of complex interactions between indoor environment and occupant behavior, which is less studied previously. Considering behavior-environment integration, this study proposes a data-driven approach based on multi-sensor fusion for energy-efficiency oriented occupancy space optimization in buildings. Firstly, time series data including indoor environment and occupant behavior were collected based on multi-sensor fusion. Then, a data-integrated Convolutional Neural Network (CNN) model was developed for occupancy state classification. Based on obtained occupant schedules, space occupancy patterns of users were extracted using hierarchical clustering, and space optimization was further conducted for energy efficiency improvement. Finally, energy consumption was predicted with random forest regression after space optimization, and the impact of occupancy space optimization on energy efficiency can be evaluated. The proposed method was successfully applied in an academic office building on a campus in Wuhan, China, which helped achieve energy consumption reduction by 23.5 %. This study presents a promising path towards sustainable energy goals in building design, which serves as advanced guidance in the management of building energy performance.

A comprehensive review of Building Energy Management Systems (BEMS) for Improved Efficiency

Building Energy Management Systems (BEMS) play a crucial role in enhancing energy efficiency and sustainability in buildings. This abstract provides a comprehensive review of BEMS, focusing on its components, benefits, challenges, and future trends. BEMS is a centralized system that monitors and controls building services, such as heating, ventilation, air conditioning, lighting, and other systems, to improve energy efficiency and occupant comfort. The key components of BEMS include sensors, controllers, communication networks, and user interfaces. These components work together to collect data on building performance, analyze energy consumption patterns, and optimize system operation. One of the primary benefits of BEMS is its ability to reduce energy consumption and costs. By monitoring and controlling building systems based on occupancy schedules and environmental conditions, BEMS can significantly reduce energy waste. Additionally, BEMS can improve occupant comfort and productivity by maintaining optimal indoor environmental conditions. Despite its benefits, BEMS faces several challenges, including high upfront costs, complexity of installation, and integration issues with existing building systems. However, advancements in technology, such as the Internet of Things (IoT) and cloud computing, are addressing these challenges and making BEMS more accessible and cost-effective. Looking ahead, the future of BEMS is promising, with continued advancements in technology driving its adoption and integration into smart building systems. The integration of artificial intelligence and machine learning algorithms into BEMS is expected to further improve its energy-saving capabilities and enhance building performance. In conclusion, BEMS is a key technology for improving energy efficiency and sustainability in buildings. By leveraging its components and capabilities, BEMS can help reduce energy consumption, lower costs, and create healthier and more comfortable indoor environments.

Occupancy schedule development and its effect on OpenStudio prototype college building model

College buildings have unique characteristics compared with school buildings. Therefore, defining the realistic occupancy schedule in a prototype college building has significant research opportunities. In this study, the actual operating schedules of each space type were collected and generated based on the class reservation schedule and compared with the previous reference schedule (primary/secondary school). The schedules were analyzed for their effect on the OpenStudio prototype college building model. The findings highlight that the use of a typical school building schedule in a college building impairs the granularity of information. The analysis shows significant differences between the previous occupancy schedule and the updated occupancy schedule of the college building, leading to a considerable decrease in occupancy density. Furthermore, the effect of these occupancy pattern changes on the prototype building model is examined. The variations were observed in minimum ventilation requirements, the average mechanical ventilation rate, and energy consumption attributed to changes in occupancy density.

Numerical simulation study of the effect of integrating hemp concrete and passive strategies on the energy consumption of a residential building in Al-Hoceima

In this work, we present an energy efficiency study for a residential building located in the city of Al-Hoceima, Morocco. The aim is to bring the building into compliance with the technical requirements of the Moroccan Thermal Building Regulations (RTCM). To achieve this, several modifications and interventions were carried out, such as the use of hemp concrete for insulation and the integration of passive strategies to minimize energy loads. Similarly, this energy study is carried out as a simulation using TRNSYS software, so that the building is modeled as a multizone entity with an occupancy schedule. The simulation results show that the technical requirements of the RTCM are achieved using a construction containing hemp concrete with an optimum thickness of around 10cm, this thickness can be reduced to 4cm using double glazing, or to 1cm using controlled natural ventilation in summer and winter.

An innovative heterogeneous transfer learning framework to enhance the scalability of deep reinforcement learning controllers in buildings with integrated energy systems

AbstractDeep Reinforcement Learning (DRL)-based control shows enhanced performance in the management of integrated energy systems when compared with Rule-Based Controllers (RBCs), but it still lacks scalability and generalisation due to the necessity of using tailored models for the training process. Transfer Learning (TL) is a potential solution to address this limitation. However, existing TL applications in building control have been mostly tested among buildings with similar features, not addressing the need to scale up advanced control in real-world scenarios with diverse energy systems. This paper assesses the performance of an online heterogeneous TL strategy, comparing it with RBC and offline and online DRL controllers in a simulation setup using EnergyPlus and Python. The study tests the transfer in both transductive and inductive settings of a DRL policy designed to manage a chiller coupled with a Thermal Energy Storage (TES). The control policy is pre-trained on a source building and transferred to various target buildings characterised by an integrated energy system including photovoltaic and battery energy storage systems, different building envelope features, occupancy schedule and boundary conditions (e.g., weather and price signal). The TL approach incorporates model slicing, imitation learning and fine-tuning to handle diverse state spaces and reward functions between source and target buildings. Results show that the proposed methodology leads to a reduction of 10% in electricity cost and between 10% and 40% in the mean value of the daily average temperature violation rate compared to RBC and online DRL controllers. Moreover, online TL maximises self-sufficiency and self-consumption by 9% and 11% with respect to RBC. Conversely, online TL achieves worse performance compared to offline DRL in either transductive or inductive settings. However, offline Deep Reinforcement Learning (DRL) agents should be trained at least for 15 episodes to reach the same level of performance as the online TL. Therefore, the proposed online TL methodology is effective, completely model-free and it can be directly implemented in real buildings with satisfying performance.

Adjacent heat transfer characteristics and energy efficiency optimization in university classrooms under partial-space heating by a numerical model based on field test

Universities’ course timetabling arranged by lecture conditions could cause different occupation schedules in classrooms, resulting in intermittent heating conditions. The adjacent heat transfer contributes to heating energy consumption. A new course timetabling was needed to decrease intermittent heating energy consumption by the reallocation of the adjacent room heat transfer. A university building in Chengdu, China, was chosen to conduct field study and measurement. The dynamic heat transfer between interior walls and its effect on the indoor thermal environment and heating energy consumption were investigated by EnergyPlus. The results showed that: (1) the differences between the heat transfer in adjacent walls under different conditions could exceed 50 %. (2) Increasing the heating synchronization rate, extending the heating duration, and reducing the interval improved the indoor thermal environment and reduced heating energy consumption by 37 %. (3) The recommended timetabling was that the adjacent classrooms both have 4 h of lectures in the morning and afternoon to meet the course demand. Compared with the benchmark, the heating load and carbon emission could be reduced by 40 % and 2.44 kg/h, respectively. The significance of this study is to improve occupant thermal comfort and reduce energy consumption in university buildings through developing educational timetabling.

Development of a Building Simulation Model for Indoor Temperature Prediction and HVAC System Anomaly Detection

In order to reduce global energy consumption, energy-efficient, green and smart buildings have to be built. In addition to the application of other energy efficiency measures, an effective management of HVAC systems is required. High quality management and control of these systems ensures optimal occupant comfort levels, proper operation, rational energy consumption, and a positive impact on the environment. This is especially important for large buildings with complex systems such as hotels. As a contribution to the creation of appropriate tools for the management and control of HVAC systems in smart buildings, this paper presents the results of the current development of a detailed dynamic simulation model based on data collected from a smart room system in a hotel in Zagreb, Croatia. The smart room system, which is integrated into the hotel's building management system, provides historical data on set and current room temperatures, room occupancy schedule, window opening, fan coil operation status, fan rotation speed, valve opening, and operating mode with a time step of 5 minutes. The simulation model based on the TRNSYS software uses a part of the available data and calculates the current internal room temperatures. A comparison of the predicted and measured temperatures at each time step showed that the deviations are within the acceptable limits. The final objectives of the model development are the identification of anomalies in the operation of the HVAC system and the optimization of its operation with the aim of reducing energy consumption.

Quantification of HVAC energy savings through occupancy presence sensors in an apartment setting: Field testing and inverse modeling approach

While many simulation-based studies have demonstrated the energy-saving potential of occupancy-driven smart thermostats in residential buildings, field testing of these devices remains relatively limited. The objective of this study is to propose a new field-testing approach to evaluate the heating, ventilation and air-conditioning (HVAC) energy-saving potential of occupancy-driven thermostats and occupancy-centric controls (OCC) for HVAC in a residential apartment in Texas, U.S. A proprietary prototype occupancy sensing system was chosen for this study, coupled with a Raspberry Pi-based hardware framework to facilitate OCC implementation by linking occupancy sensors to the existing HVAC system. Energy consumption of the HVAC system during both non-OCC baseline and OCC modes was measured using various sensors installed at the test site. Furthermore, XGBoost models were developed, used onsite measurements, to consider the effects of various factors like occupancy and outdoor weather conditions on HVAC energy consumption to facilitate a fair comparison. The scope of the analysis was further expanded to cover the period from 2018 to 2022 and Typical Meteorological Year 3 (TMY3) by incorporating a statistical occupancy schedule generator. The results suggest that approximately 15.1% cooling energy consumption could be saved during the testing period, equivalent to around 109 kWh in electricity savings. Moreover, OCCs have the potential to achieve electricity savings ranging from 300 to 330 kWh in the months between April and September, depending on the weather in each year. This corresponds to a cooling energy saving ratio ranging from 19% to 24%. Despite energy savings, OCC had some minor adverse effects on their thermal comfort and perceptions of Indoor Environmental Quality (IEQ). Moreover, residents' ratings for indoor thermal comfort dropped from neutral to slightly dissatisfied after the implementation of OCCs.

Factors Affecting Indoor Temperature in the Case of District Heating

In this study, the influence of variables defining indoor temperature is studied, focusing on operational data and visual and technical inspections rather than the temperature control setpoints and occupancy schedule. This is incorrect because infiltration and insolation are highly variable. This results in lowering the temperature difference between the supply and return lines, overheating some spaces, lowering the indoor temperature in others, and poor hydronic balancing. The novelty lies in studying the actual operating condition of real district heating (DH) systems. The research hypothesis is that internal heat gains along with the infiltration of and variations in outdoor temperature cause daily changes in indoor temperature. These factors seem to be the primary reasons for the variations in supply and return temperature, if the rate of energy loss is not large in new office buildings constructed according to tightened contemporary energy conservation regulations. The saving effect is achieved by allowing the energy to be dumped into building envelopes; thus, the flow rate or supply temperature are varied in a narrower range. Dumping heat by using the storage capacity of building envelopes is suggested. The corrected design approach minimizes energy consumption and increases annual performance (e.g., by 14.1% here). Advantages are achieved by tuning a controller at a DH substation.

Method of test for CO2-based demand control ventilation systems: Benchmarking the state-of-the-art and the undervalued potential of proportional-integral control

Carbon dioxide (CO2) based demand control ventilation (DCV) adjusts a building’s outdoor air ventilation rate in response to indoor CO2 concentration to save energy while maintaining indoor air quality. Packaged heating, ventilation, and air-conditioning systems often contain DCV controllers with embedded proprietary algorithms that lack transparent performance data. A test method was developed to assess the ability of a DCV controller to maintain the indoor CO2 concentration at a setpoint in response to a series of CO2 generation functions that represent three different building occupancy densities and two occupancy schedules. Six commercially available controllers were tested to demonstrate the method and provide directly comparable results. The performance (in terms of CO2 control and damper movement) of each controller tested was compared to the performance of an ideal controller which knows the CO2 generation function. Finally, the performance of a proportional-integral (PI) controller with preset gains was developed and tested to determine the potential maximum performance achievable with this control strategy. The best performing commercially available controller achieved CO2 control (within 75 ppm of the setpoint) approximately 80 % of the time with damper movement slightly less than an ideal controller. However, most of the commercially available controllers had marginal or poor performance for CO2 control and damper movement. Two controllers had damper movement more than three times the ideal controller. Notably, a PI algorithm configured and tested by the research team achieved superior performance with CO2 control 92 % of the time and damper movement 1.5 times the ideal controller.

Quantifying the impact of Covid-19 on the energy consumption in the low-income housing in Greater London

Covid-19 has caused great challenges to the energy sector, particularly in residential buildings with low-income households. This study investigates the impact of the confinement measures due to the Covid-19 outbreak on the energy demand of seven residential archetype buildings in Greater London. Three levels of confinement for occupant schedules are proposed and compared with the base case before Covid-19. The archetypes, their boundary conditions, and input parameters are set up according to statistics from English Housing Survey (EHS) sample data for low-income housing. The base case scenario (normal life without confinement measures) is validated against the measured data energy consumption from the National Energy Efficiency Data-Framework (NEED) statistics. The results show that electricity consumption is significantly lower than that for heating and hot water for all the archetypes. By comparing the base case scenario with the full Covid-19 lockdown scenario, the results indicate that heating and hot water consumption (kWh) for all the residential archetypes increases, on average, by 10%, and total electricity demand (kWh) increases by 13%. The study highlights the importance of introducing detailed occupancy profiles in multi-zone building energy simulation models during a pandemic that leads to a greater shift towards home working, which may increase the risk of fuel poverty in low-income housing.

Synconn_build: A python based synthetic dataset generator for testing and validating control-oriented neural networks for building dynamics prediction

Applying model-based predictive control in buildings requires a control-oriented model capable of learning how various control actions influence building dynamics, such as indoor air temperature and energy use. However, there is currently a shortage of empirical or synthetic datasets with the appropriate features, variability, quality and volume to properly benchmark these control-oriented models. Addressing this need, a flexible, open-source, Python-based tool, synconn_build, capable of generating synthetic building operation data using EnergyPlus as the main building energy simulation engine is introduced. The uniqueness of synconn_build lies in its capability to automate multiple aspects of the simulation process, guided by user inputs drawn from a text-based configuration file. It generates various kinds of unique random signals for control inputs, performs co-simulation to create unique occupancy schedules, and acquires weather data. Additionally, it simplifies the typically tedious and complex task of configuring EnergyPlus files with all user inputs. Unlike other synthetic datasets for building operations, synconn_build offers a user-friendly generator that selectively creates data based on user inputs, preventing overwhelming data overproduction. Instead of emulating the operational schedules of real buildings, synconn_build generates test signals with more frequent variation to cover a broader range of operating conditions.•Synconn_build is an open-source tool designed to address the lack of datasets for benchmarking control-oriented building dynamics prediction models.•The tool automates simulations, data acquisition, and EnergyPlus configuration, guided by user inputs.•Synconn_build prevents data overproduction by selectively creating data, offering a user-friendly approach to dataset generation.

Typical academic building energy model development and energy saving evaluation using occupant-based control

Studies on occupant-based controls focused on office buildings have been increasing over the past decade because of the significant energy saving potential they can offer in buildings. However, there are very few studies that focus on academic buildings at colleges and universities, which represent a significant number of buildings in the U.S. This paper aims to develop typical academic building models and use these models to evaluate the energy savings potential of implementing occupant-based control (OBC) using EnergyPlus. Four types of typical university-level academic building models were determined based on cluster analysis, including Office-dominated, Laboratory-dominated, Study Room-dominated, and Mixed-use. The occupancy schedules were updated to include stochastic occupancy schedules to represent academic building use. Next, the baseline and proposed models were built. The proposed model uses OBC, resetting the temperature schedule and minimum outdoor air flow schedule based on occupancy, following the current recommendations of ASHRAE 90.1, and 62.1. To demonstrate the use of this developed model, it was run using ASHRAE Climate Zone 5A climate conditions. Results show that there is significant energy saving potential for academic buildings with the implementation of OBC, including an HVAC energy savings of 35%–51% using occupancy presence data for HVAC controls, and a further energy saving increase (3–9%) using occupancy counts for HVAC controls. The results of this work can be used to evaluate other energy saving measures, and aid building designers and operators in making informed decisions in applying appropriate control strategies to optimize building energy systems, as well as predict energy use and demand.