Maintaining Occupant Comfort Research Articles

A generalized thermal comfort model using thermographic images and compact convolutional transformers: Towards scalable and adaptive occupant comfort optimization

Thermal comfort models that account for individual thermal sensations are crucial for optimizing environmental control while maintaining occupant comfort. However, the current approach of developing occupant-specific models is not scalable for new occupants whose data were not used for training, making it impractical for multi-occupant spaces and burdensome for management. To address this, we propose a thermal comfort model based on a subject-independent evaluation approach, capable of generalizing to new individuals not included during model training. This model accurately predicts thermal sensations without the need for occupant-specific models, allowing scalability and adaptability to new occupants. Furthermore, this study considers occupants with face masks, which is essential in environments where masks are required for their wellbeing and performance. The model was developed using Compact Convolutional Transformers which combines convolutional layers and transformer layers, allowing the model to capture both fine-grained local features and broader contextual relationships, making it effective for predicting thermal comfort from thermal images. The model was trained using the Leave-One-Subject-Out (LOSO) and Leave-One-Group-Out (LOGO) training approaches. The LOSO approach achieved high accuracies of 98.04 %, 98.93 %, and 98.88 % for dataset A (participants without face masks), dataset B (participants with face masks), and dataset C (a combination of both), respectively. The LOGO approach achieved accuracies of 99.92 %, 99.40 %, and 99.85 % for datasets A, B, and C, respectively, showing a slightly better prediction performance. The approach presented in this study offers a promising solution for designing accurate generalized models for real-time environmental control to meet the thermal comfort needs of occupants.

Optimal control strategy for building HVAC systems: Satisfying flexible demand response with different value-based selection

Growing renewable energy integration leads to significant fluctuations in electricity generation. To address this challenge, market-based mechanisms for dynamic carbon emission factors and electricity prices are emerging, particularly in regions like China pursuing carbon neutrality. Buildings, with HVAC systems as the largest consumers, require flexible Demand Response (DR) strategies to balance these fluctuations while maintaining occupant comfort. This paper proposes a hierarchical control framework for building HVAC systems. It optimizes operation for various objectives (minimizing energy consumption, reducing electricity costs, and lowering carbon emissions), while ensuring thermal comfort. The framework involves three modules. First is the time-based demand allocation, which uses genetic algorithms to optimize energy use based on future cooling demands, indoor temperature, and fluctuating electricity and carbon prices. It provides building-level temperature setpoints and electricity consumption plans. Second is the spatial indoor demand adjustment, which balances overall cooling load and temperature satisfaction across individual spaces, taking into account room-specific costs and difficulty confidents for temperature adjustments. This module assigns future temperature setpoints to each space. Third is HVAC system follow-up control, which Implements the optimized setpoints for real-time control of the HVAC system. The framework’s effectiveness is verified through application to a real commercial building project. Compared to a baseline scenario, the optimal electricity cost strategy achieved a 6.5% cost reduction, and the optimal carbon emission reduction strategy achieved an 8.2% reduction. Real-world control based on carbon emission optimization resulted in an actual year-on-year carbon emission reduction of 8.7%.

Comparative Analysis of Cooling Load Calculations: CLTD Method vs. Carrier HAP 5.01 Software for Hotel HVAC Design

This study examines the cooling load requirements of a hotel building by comparing two methodologies: the traditional Cooling Load Temperature Difference (CLTD) method and the Carrier Hourly Analysis Program (HAP) 5.01 software. The primary objective is to validate the accuracy and reliability of these methods in calculating cooling loads across different room types, from standard rooms to larger, more complex suites. The results show that the CLTD method consistently yields higher cooling load estimates, with discrepancies ranging from 3% to 14% compared to HAP 5.01 calculations. These differences are most significant in larger rooms, such as suites and owner’s suites, which have more extensive glass areas, higher occupancy, and more heat-generating equipment. The findings indicate that while the CLTD method is valuable for quick, preliminary estimates, the HAP 5.01 software provides a more precise and comprehensive analysis, taking into account hourly variations, equipment schedules, and other factors that impact cooling loads. This research highlights the need for careful selection of the appropriate calculation method to ensure the efficient design of HVAC systems, maximizing energy efficiency, and maintaining occupant comfort. The study concludes that for projects requiring high accuracy, particularly in complex or large spaces, dynamic simulation tools like HAP 5.01 are preferable. Detailed cooling load results and comparisons are provided in the supplementary documentation, offering further insights into the analysis and its implications for HVAC design.

USING DEEP LEARNING TO OPTIMIZE HVAC SYSTEMS IN RESIDENTIAL BUILDINGS

ABSTRACT HVAC systems are crucial for maintaining indoor temperature and humidity in buildings but consume significant energy, accounting for over 50% of a building’s energy use. This study proposes a deep reinforcement learning (DRL) algorithm for optimizing energy consumption in residential building HVAC systems while maintaining occupant comfort. Climate data was collected using low-cost sensors, and a co-simulation framework was developed for offline training and validation of our DRL-based algorithm. The proposed DRL-based algorithm was compared to a rule-based HVAC system regarding energy consumption and occupant comfort. Results show that the proposed algorithm can reduce energy consumption by up to 15% compared to the rule-based HVAC system. DRL is a suitable approach for optimizing HVAC systems due to its ability to adapt to the dynamics of multi-parameterized systems. This study contributes to sustainable building design by proposing a DRL-based algorithm to reduce energy consumption while maintaining a comfortable indoor temperature. Using low-cost sensors and a co-simulation framework provides a practical and cost-effective method for training and validating the proposed algorithm.

Optimizing automated shading systems for enhanced energy performance in cold climate zones: Strategies, savings, and comfort

Enhancing urban sustainability requires innovative strategies to improve energy performance while maintaining occupant comfort in new and existing buildings. Buildings located in cold climate zones with high glazing-to-wall ratios often face unwanted solar gain and heat loss that, in turn, negatively impact building energy performance. Though automated shading systems effectively reduce excessive indoor solar gain in warm climates, a research gap exists regarding their ability to enhance building energy performance in heating-dominant climates. This paper investigates automated shading implementation for cold climate zones and develops a novel cold climate optimized sensorless control strategy for interior roller shades. A field study was conducted to assess the impact of roller shade operation on indoor thermal conditions. Results from the field study showed that the prototype roller shades operating on the developed control strategy reduced daytime indoor peak temperatures during the summer season to 26.4 °C ± 0.5 °C from 30.0 °C ± 1.9 °C and 28.5 °C ± 1.2 °C for the unshaded and shaded static operation baseline scenarios, respectively. During winter season tests, shade deployment reduced the overnight indoor cooling rate from 0.60 °C/hr ± 0.30 °C/hr and 0.42 °C/hr ± 0.18 °C/hr. A mathematical model was also developed to estimate shading system energy performance based on user-defined building specifications and weather-related variables. It was found that automated shading systems are suitable for use as a green retrofit strategy for cold climate zone buildings. The technology exhibited a payback period range of approximately 5–15 years, depending on factors such as glazing type, glazing orientation, solar exposure, and local climate conditions. Findings from both the simulation and prototype field study support the use of cold climate-optimized automated shading systems to improve overall building energy performance - reducing both building heating and cooling-related energy consumption.

Sensor-based indoor air temperature prediction using deep ensemble machine learning: An Australian urban environment case study

Accurate prediction of indoor temperature is critical for climate change adaptation and occupant health. The aim of this study is to investigate an improved deep ensemble machine learning framework (DEML), by adjusting the model architecture with several machine learning (ML) and deep learning (DL) approaches to forecast the sensor-based indoor temperature in the Australian urban environment. We collected ambient station-based temperatures, satellite-based outdoor climate characteristics, and low-cost sensor-based indoor environmental metrics from 96 devices from August 2019 to November 2022, and established DEML with a rolling windows approach to assess the prediction stability over time. The DEML model was compared with several benchmark models, including Random Forest (RF), Support Vector Machine (SVM), eXtreme Gradient Boosting (XGboost), Long-short term memory (LSTM), and Super Learner model (SL). A total of 13,715 days [median: 341 days; IQR (the interquartile range): 221–977 days] of low-cost sensor-based indoor temperature were included in 25 commercial and residential buildings across eight cities. The prediction performance of DEML was superior to the other five benchmark models in most of the sensors [coefficients of determination (R2) of 0.861–0.990 and root mean square error (RMSE) of 0.125–0.886 °C], followed by RF and SL algorithms. DEML consistently achieved high accuracy across different climate zones, seasons, and building types, which could be used as a crucial tool for optimizing energy use, maintaining occupant comfort and health, and adapting to the impacts of climate change.

Ten questions concerning reinforcement learning for building energy management

As buildings account for approximately 40% of global energy consumption and associated greenhouse gas emissions, their role in decarbonizing the power grid is crucial. The increased integration of variable energy sources, such as renewables, introduces uncertainties and unprecedented flexibilities, necessitating buildings to adapt their energy demand to enhance grid resiliency. Consequently, buildings must transition from passive energy consumers to active grid assets, providing demand flexibility and energy elasticity while maintaining occupant comfort and health. This fundamental shift demands advanced optimal control methods to manage escalating energy demand and avert power outages. Reinforcement learning (RL) emerges as a promising method to address these challenges. In this paper, we explore ten questions related to the application of RL in buildings, specifically targeting flexible energy management. We consider the growing availability of data, advancements in machine learning algorithms, open-source tools, and the practical deployment aspects associated with software and hardware requirements. Our objective is to deliver a comprehensive introduction to RL, present an overview of existing research and accomplishments, underscore the challenges and opportunities, and propose potential future research directions to expedite the adoption of RL for building energy management.

DeepValve: Development and experimental testing of a Reinforcement Learning control framework for occupant-centric heating in offices

Space heating controls in offices usually follow static schedules detached from actual occupancy, which results in energy waste by unnecessarily heating vacant offices. The uniqueness of stochastic occupancy profile and thermal response time of each office are two main challenges in hard-programming a transferrable control logic that can adapt space heating schedule to the occupancy profile. This study proposes a Reinforcement Learning-based control framework (called DeepValve) that learns by itself how to adapt the space heating schedule to the occupancy profile in each office to save energy while maintaining comfort. All the aspects of the proposed framework (design, training, hardware setup, etc.) are centered on ensuring that it can be implemented on many offices in practice. The methodology includes three main steps: training on a wide variety of simulated offices with real-world occupancy data, month-long tests on three simulated offices, and day-long experimental tests in an environmental chamber. Results indicate that the agent can quickly adapt to new offices and save energy (40% reduction in total temperature increment) while maintaining occupant comfort. The results highlight the importance of occupant-centric control in offices.

Analysis of Particulate and Microbiological Filtration Performance of Air Handling Unit Filters in a Low-Energy Office Building over 12 Months

Indoor air quality is an important consideration for the health and well-being of building occupants, and the SARS CoV-2 pandemic highlighted the importance of maintaining proper ventilation in buildings. Air handling units (AHUs) are used to provide fresh air and maintain occupant comfort. The objective of this work was to study the evolution of filtration efficiency in an AHU fitted with bag filters, installed to treat office air in a low-energy building, over a 12-month period. The particulate filtration efficiency (PFE) and the microbial filtration efficiency (MFE) were quantified by measuring particle size distribution and bacterial and fungal concentration in the air circulating in the AHU. The resulting microbial concentration measurements in the fresh air (between 10²–103 CFU/m3 for fungi and around 103 CFU/m3 for bacteria) were higher than those in the extracted air from the offices (between 101 and 102 CFU/m3 for fungi and around 102 CFU/m3 for bacteria). The PFE and MFE measured were almost constant throughout the 12 months, with an increase of the filter pressure drop from 70 to 90 Pa. The PFE and MFE were quite comparable for a particle diameter. Therefore, the measurement of PFE is a reliable indicator of the MFE.

Parameter identification approach to represent building thermal dynamics reducing tuning time of control system gains: A case study in a tropical climate

As one of the main consumers of primary energy globally, buildings have been among the main targets for implementing energy efficiency solutions, such as building control strategies that maintain occupant comfort and reduce operating costs. The design of such control schemes relies on a thermal model of the building to predict indoor temperature. The model should be sufficiently accurate to describe building dynamics but simple enough to remain optimal for control purposes. This paper proposes a methodology to identify thermal RC networks to model building thermal dynamics of a residential buildings located in humid and rainy climates, a topic not widely covered in current literature. The candidate models for the methodology are determined through a parameter dispersion study, which consists of training the models multiple times and checking if the parameters converge to a single value regardless of their initial value. Then the effect of the training dataset characteristics on model performance is studied. The methodology is established and then tested in a residential case study in Panama from these conclusions. Results show that a linear model with few parameters and trained with only 10 days of data can successfully represent a system with prominent nonlinear phenomena. The model with the best performance during active operation has a validation root mean square error of 0.36°C, which is satisfactory for controller design purposes. The model is then used to tune a proportional integral derivative controller, successfully employed to maintain the desired indoor temperature. Using the identified model to calibrate the controller avoids tedious trial and error procedures.

Deep and transfer learning for building occupancy detection: A review and comparative analysis

The building internet of things (BIoT) is quite a promising concept for curtailing energy consumption, reducing costs, and promoting building transformation. Besides, integrating artificial intelligence (AI) into the BIoT is essential for data analysis and intelligent decision-making. Thus, data-driven approaches to infer occupancy patterns usage are gaining growing interest in BIoT applications. Typically, analyzing big occupancy data gathered by BIoT networks helps significantly identify the causes of wasted energy and recommend corrective actions. Within this context, building occupancy data aids in the improvement of the efficacy of energy management systems, allowing the reduction of energy consumption while maintaining occupant comfort. Occupancy data might be collected using a variety of devices. Among those devices are optical/thermal cameras, smart meters, environmental sensors such as carbon dioxide (CO2), and passive infrared (PIR). Even though the latter methods are less precise, they have generated considerable attention owing to their inexpensive cost and low invasive nature. This article provides an in-depth survey of the strategies used to analyze sensor data and determine occupancy. The article’s primary emphasis is on reviewing deep learning (DL), and transfer learning (TL) approaches for occupancy detection. This work investigates occupancy detection methods to develop an efficient system for processing sensor data while providing accurate occupancy information. Moreover, the paper conducted a comparative study of the readily available algorithms for occupancy detection to determine the optimal method in regards to training time and testing accuracy. The main concerns affecting the current occupancy detection system in terms of privacy and precision were thoroughly discussed. For occupancy detection, several directions were provided to avoid or reduce privacy problems by employing forthcoming technologies such as edge devices, Federated learning, and Blockchain-based IoT.

Assessing the performance of global thermostat adjustment in commercial buildings for load shifting demand response

Abstract Efficiently leveraging new sources of flexibility is critical to mitigating load balancing challenges posed by variable renewable resources. The thermal inertia of commercial buildings allows us to shift their power consumption on minute to hourly timescales to provide demand response (DR) to the grid while maintaining occupant comfort. Global thermostat adjustment (GTA) provides a readily available and scalable approach for implementing load shifting DR using commercial heating, ventilation, and air conditioning (HVAC) systems, since it leverages the inherent sophistication of modern building automation systems. However, there is an incomplete understanding of GTA’s performance for this purpose and its impact on building systems and occupant comfort. In this paper, we explore the performance of GTA by analyzing results from nearly nine hundred experiments on eight university campus buildings in Michigan and North Carolina. Using GTA, we manipulate each building’s thermostat setpoints causing the building to shift its power consumption with respect to its baseline. We quantify the magnitude of HVAC power response, energy use of HVAC subsystems, and impact on occupant comfort. Finally, we connect our experimental results with power system operation using an optimization model that coordinates GTA actions across a large collection of grid-interactive efficient buildings to reduce high ramp rates on the grid and mitigate renewable energy curtailment. Overall, our work finds that the impacts on HVAC subsystems are often complex, and may result in additional energy being consumed by fans and terminal reheat. These effects must be considered when using GTA for load shifting. Additionally, we demonstrate that occupant comfort, as assessed by indoor temperature and humidity, can be maintained during GTA events. From a societal perspective, our modeling work finds that the additional renewable energy that can be integrated through the use of GTA strategies eclipses any additional energy consumed by buildings.

Physics-informed linear regression is competitive with two Machine Learning methods in residential building MPC

Because physics-based building models are difficult to obtain as each building is individual, there is an increasing interest in generating models suitable for building MPC directly from measurement data. Machine learning methods have been widely applied to this problem and validated mostly in simulation; there are, however, few studies on a direct comparison of different models or validation in real buildings to be found in the literature. Methods that are indeed validated in application often lead to computationally complex non-convex optimization problems. Here we compare physics-informed Autoregressive–Moving-Average with Exogenous Inputs (ARMAX) models to Machine Learning models based on Random Forests and Input Convex Neural Networks and the resulting convex MPC schemes in experiments on a practical building application with the goal of minimizing energy consumption while maintaining occupant comfort, and in a numerical case study. The building has a water-based emission system and is located in temperate climate. We demonstrate that Predictive Control leads to savings between 26% and 49% of heating and cooling energy, compared to the building’s baseline hysteresis controller. Moreover, we show that all model types lead to satisfactory control performance in terms of constraint satisfaction and energy reduction. However, we also see that the physics-informed ARMAX models have a lower computational burden, and a superior sample efficiency compared to the Machine Learning based models. Moreover, even if abundant training data is available, the ARMAX models have a significantly lower prediction error than the Machine Learning models, which indicates that the encoded physics-based prior of the former cannot independently be found by the latter.

A Demand Response-Based Solution to Overloading in Underdeveloped Distribution Networks

This paper addresses the problem of overloading in power distribution networks, which stems from the transmission systems being incapable of delivering power from source to consumers during peak hours. This causes frequent power-outages (or blackouts), requiring the consumers to rely on alternative energy sources, e.g., Uninterrupted Power Supply (UPS) systems with batteries to meet their essential needs. This paper proposes a demand response (DR) framework to eliminate the problem of network overloading. The flexibility in the consumption of batteries and air conditioners (ACs) is exploited in the proposed framework. The operation of ACs is manipulated while maintaining occupant comfort, and the power flow from mains and batteries is scheduled based on an ensemble of demand forecast avoiding network overloading and consequent power-outages. The problem is modeled in an optimal control setting and solved using a stochastic model predictive control strategy, and a computationally effective method is also proposed to efficiently solve the underlying optimization problems. Towards the end, simulation results show the efficacy of the proposed framework to avoid overloading.

A digital twin framework for improving energy efficiency and occupant comfort in public and commercial buildings

Model Predictive Control (MPC) can be used in the context of building automation to improve energy efficiency and occupant comfort.Ideally, the MPC algorithm should consider current- and planned usage of the controlled environment along with initial state and weather forecast to plan for optimal comfort and energy efficiency.This implies the need for an MPC application which 1) considers multiple objectives, 2) can draw on multiple data sources, and 3) provides an approach to effectively integrate against heterogeneous building automation systems to make the approach reusable across different installations.To this end, this paper presents a design and implementation of a framework for digital twins for buildings in which the controlled environments are represented as digital entities. In this framework, digital twins constitute parametrized models which are integrated into a generic control algorithm that uses data on weather forecasts, current- and planned occupancy as well as the current state of the controlled environment to perform MPC. This data is accessed through a generic data layer to enable uniform data access. This enables the framework to switch seamlessly between simulation and real-life applications and reduces the barrier towards reusing the application in a different control environment.We demonstrate an application of the digital twin framework on a case study at the University of Southern Denmark where a digital twin has been used to control heating and ventilation.From the case study, we observe that we can switch from rule-based control to model predictive control with no immediate adverse effects towards comfort or energy consumption. We also identify the potential for an increase in energy efficiency, as well as introduce the possibility of planning energy consumption against local electricity production or market conditions, while maintaining occupant comfort.

Biomimetic building facades demonstrate potential to reduce energy consumption for different building typologies in different climate zones.

Greenhouse gas (GHG) emissions leading to anthropogenic global warming continue to be a major issue for societies worldwide. A major opportunity to reduce emissions is to improve building construction, and in particular the effectiveness of building envelope, which leads to a decrease in operational energy consumption. Improving the performance of a building's thermal envelope can substantially reduce energy consumption from heating, ventilation, and air conditioning while maintaining occupant comfort. In previous work, a computational model of a biomimetic building façade design was found to be effective in temperate climates in an office context. Through a case study example based on animal fur and blood perfusion, this paper tests the hypothesis that biomimetic building facades have a broader application in different building typologies across a range of climate zones. Using bioinspiration for innovation opens new ideas and pathways for technological development that traditional engineering design does not provide. This study exemplifies the process in a building façade, integrating a new form of insulation, heating and cooling. Methods of mathematical modelling and digital simulation methods were used to test the energy reduction potential of the biomimetic façade was tested in a set of operational applications (office, school, and aged care) and across different climate zones (tropical, desert, temperate, and cool continental). Results indicated that the biomimetic façade has potential to reduce energy consumption for all building applications, with the greatest benefit shown in residential aged care (67.1% reduction). Similarly, the biomimetic building façade showed potential to reduce operational services energy consumption in all climate zones, with the greatest energy reductions achieved in the tropical (55.4% reduction) and humid continental climates (55.1% reduction). Through these results the hypothesis was confirmed suggesting that facades engineered to mimic biological functions and processes can improve substantially decrease building operational energy consumption and can be applied in different building classifications and different climate zones. These results would significantly decrease operational greenhouse gas emissions over the lifetime of a building and provide substantial savings in energy bills. Such facades can contribute to the further reduction in greenhouse gas emissions in a broad range of contexts in the built environment and other areas of technology and design. The flexibility and adaptability of biomimetic facades exemplify how biological strategies and characteristics can augment and improve performance in different environments, since the organisms that inspire innovation are already well-adapted to the conditions on earth. This study also exemplified a method by which other biomimetic building envelope features may be assessed. Further work is suggested to assess economic viability and constructability of the proposed facades.Graphic abstract

Cooling energy savings and occupant feedback in a two year retrofit evaluation of 99 automated ceiling fans staged with air conditioning

Controlled air movement is an effective strategy for maintaining occupant comfort while reducing energy consumption, since comfort at moderately warmer temperatures requires less space cooling. Modern ceiling fans provide a 2–4 °C cooling effect at power consumption comparable to LED lightbulbs (2–30 W) with gentle air speeds (0.5–1 m/s). However, very limited design guidance and performance data are available for using ceiling fans and air conditioning together, especially in commercial buildings. We present results from a 29-month field study of 99 automated ceiling fans and 12 thermostats installed in ten air-conditioned buildings in a hot/dry climate in California. Staging ceiling fans to automatically cool before, and then operate together with air conditioning enabled raising air conditioning cooling temperature setpoints in most zones, with overall positive occupant interview and survey responses. Overall measured cooling season (April– October) compressor energy savings were 36%, normalized by floor area served (41% during summer peak billing hours). Weather-normalized changes in zone energy use varied from 24% increase to 73% decrease across 13 compressors, reflecting variation in occupant schedules and other uncontrolled factors in occupied buildings. Median weather-normalized energy savings per compressor were 21%. Staging ceiling fans and air conditioning provided comfort across a wider temperature range, using less energy, than air conditioning alone.

Development and evaluation of data-driven controls for residential smart thermostats

The advent of smart thermostats with real-time sensing raises the question of how to preemptively control heating, ventilation, and air conditioning (HVAC) systems to minimize energy usage while maintaining occupant comfort. To this end, we empirically compare a standard reactive deadband control to two new smart thermostat HVAC control methods: (1) a model-free reinforcement learning (RL) approach and (2) a novel model predictive control (MPC) method, whose solution is optimal with respect to its data-driven linear model. We evaluated the controls with 500 unique energy models of houses located in the United States. The models were modified to facilitate the short-term performance simulation required for residential HVAC systems. Overall, we found the MPC controller offers three distinct advantages over the RL and deadband methods: (1) MPC had the lowest average cost (defined as a custom weighted combination of runtime and comfort) of the evaluated controllers; (2) the MPC control’s linear model was able to reliably extrapolate from the sparse sample of training observations, thus enabling it to adapt quickly to recent data; and (3) in contrast to RL methods, MPC did not subject the houses or occupants to the discomfort of system exploration.

Community-scale interaction of energy efficiency and demand flexibility in residential buildings

Demand-side management (DSM) strategies, including energy efficiency (EE) and demand flexibility (DF), contribute to cost-effective operation of the electricity grid. From a system-level perspective, such programs reduce costs, enhance reliability, and reduce network issues. Similarly, DSM programs help participating customers reduce utility bills while maintaining occupant comfort. Understanding the relationship between EE and DF is key to realizing the full potential of DSM programs. In this study, we modeled an all-electric residential community based on a 498-home community that is planned for construction in Fort Collins, Colorado in the United States. We used this community model to study the relationship between different EE measures, including building envelope upgrades and smart appliances, and DF enabled by a home energy management system (HEMS) responding to a time-varying tariff. Various EE levels in the homes – code-minimum, zero energy ready, and even higher levels of envelope efficiency – were simulated. DF is enabled by the HEMS, which coordinates behind-the-meter resources, including flexible building loads, PV, and home battery systems, to minimize utility bills while maintaining occupant comfort. When comparing to the code-minimum homes, EE upgrades alone reduce HVAC energy use during peak hours by up to 50% and the HVAC utility bill by up to $312/year. With the addition of HEMS, the average daily peak demand can be reduced by up to 0.58 MW or 1.2 kW/home in the higher envelope efficiency homes. The combination of EE upgrades, HEMS, and home battery systems is expected to save homeowners up to $590/year while increasing community load flexibility. However, HEMS and home battery systems are less effective in increasing the DF in the more efficient homes due to the lower load.

Demand-side flexibility and demand-side bidding for flexible loads in air-conditioned buildings

Demand-side flexibility (DSF) has been touted as a possible solution to the challenges in power system operation arising from increasing intermittent renewables penetration and the emergence of electric vehicles. In Singapore, where around 24 to 60% of electricity demand in buildings could be attributed to heating, ventilation, and air conditioning (HVAC) purposes, air-conditioned buildings represent a significant flexibility resource which could be used to provide DSF and help tackle these challenges. This study aims to investigate the DSF potential of Singapore’s building stock and to explore how this potential could be realized through demand-side bidding. To this end, a building energy modeling tool with explicit modeling of the relationship between occupant comfort and HVAC load with model predictive control, CoBMo, is used. CoBMo allows optimal load scheduling to be expressed as a linear programming problem: minimizing overall electricity cost while maintaining occupant comfort. A price-based market clearing model is developed to evaluate demand-side bidding implementation, for which a case study on Singapore’s Downtown Core district is developed. Three scenarios with possible future utility-scale photovoltaic (PV) penetration in Singapore’s electricity system are explored, alongside a sensitivity analysis and a comparison between centralized dispatch and demand-side bidding with price-quantity pairs and linear curves. Results of the analysis show that DSF potential varies between building types, depending on cooling load and occupancy schedule. When extreme price fluctuations happen in future Singapore electricity market with 10 GWp PV penetration, demand-side bidding could aid consumers to utilize their DSF potential by encouraging more effective energy use and in turn, reducing their total electricity cost.