Heating, Ventilation, And Air Conditioning Energy Consumption Research Articles

Exploring and field-demonstrating geofence-based occupancy-centric control in residential buildings

Several studies have highlighted the thermal comfort challenges associated with occupancy-centric control (OCC) when occupants first arrive, despite OCC demonstrating energy savings potential in buildings. To address this challenge, a geofence-based OCC (Geo-OCC), where the virtual boundary of the occupant’s physical location is infused into the smart thermostat, is explored to pre-condition buildings, aiming to alleviate thermal discomfort. This research adopted an apples-to-apples field testing approach to quantify how Geo-OCC impacts the thermal comfort and energy consumption of the heating, ventilation, and air-conditioning (HVAC) system in two 1,200 ft2 lab homes located in Texas, USA. These two homes were designed and built as identically as they could be. Two side-by-side experimental scenarios were designed to compare different control strategies through integrations with smart thermostats. In scenario 1, Geo-OCC was implemented in the east home, while a baseline case without OCC (Non-OCC) was executed in the west home. In scenario 2, Geo-OCC was employed in the east home, and a traditional OCC based on occupancy presence sensors (Sensed-OCC) was implemented in the west home, demonstrating Geo-OCC’s ability to alleviate thermal discomfort. The field-testing results of scenario 1 suggested that Geo-OCC achieved a 46.41 % reduction in heating energy consumption compared to Non-OCC over the testing duration of 25 days, resulting in a maximum unmet degree (UD) of 3.83 °C in a 5-minute sampling frequency, in contrast to a maximum UD of 0.83 °C in Non-OCC mode. The results from the field testing in scenario 2 indicated that Geo-OCC reduced the maximum UD to 1.94 °C in a 5-minute sampling frequency compared to 2.28 °C in Sensed-OCC, without an increase in heating energy consumption. The results of the scenario 1 experiment suggested the OCC decreased the HVAC energy consumption while having a slightly adverse effect on thermal comfort during occupied periods, especially upon the first arrival of occupants. The field testing of scenario 2 exhibited the capability of Geo-OCC to enhance thermal comfort with minor effects on the HVAC energy consumption. In addition, Geo-OCC displayed the potential to reduce the peak demand increase caused by traditional Sensed-OCC.

HVAC energy consumption prediction based on RJITL deep neural network model

During the operation of HVAC (Heating, Ventilation, and Air-Conditioning) systems, precise energy consumption prediction plays an important role in achieving energy savings and optimizing system performance. However, the HVAC system is a complex and dynamic system characterized by a large number of variables that exhibit significant changes over time. Therefore, it is inadequate to rely on a fixed offline model to adapt to the dynamic changes in the system that consume tremendous computation time. To solve this problem, a deep neural network (DNN) model based on Just-in-Time learning with hyperparameter R (RJITL) is proposed in this paper to predict HVAC energy consumption. Firstly, relevant samples are selected using Euclidean distance weighted by Spearman coefficients. Subsequently, local models are constructed using deep neural networks supplemented with optimization techniques to enable real-time rolling energy consumption prediction. Then, the ensemble JITL model mitigates the influence of local features, and improves prediction accuracy. Finally, the local models can be adaptively updated to reduce the training time of the overall model by defining the update rule (hyperparameter R) for the JITL model. Experimental results on energy consumption prediction for the HVAC system show that the proposed DNN-RJITL method achieves an average improvement of 5.17% in accuracy and 41.72% in speed compared to traditional methods.

Enhancing building envelopes: Parametric analysis of shading systems for opaque facades and their comparison with cool paints

Heat gain through the buildings opaque facades significantly contributes to the energy consumption of Heating, Ventilation, and Air Conditioning (HVAC) systems. In the post-construction phase, retrofitting options for reducing façade heat gain are limited, with cool paints being the prevalent strategy. However, the efficacy of shading systems as an alternative strategy remains underexplored as existing research predominantly focuses on proof-of-concept validation, often overlooking a comprehensive assessment of shading system configurations across diverse climates and building typologies. Moreover, a comparative analysis of the performance and potential synergies between cool paints and shading systems on opaque facades is crucial to understand their actual effectiveness in real-world applications. To address these gaps, our study undertakes an extensive parametric simulation, taking into account variables such as shading configurations, cool paints with varying facade solar absorbance values, facade orientation, diverse climates, and different building typologies. The results demonstrate that the use of shading on opaque facades alone could result in a HVAC energy saving of 8–28 %, while the application of cool paints (façade absorptance value of 0.2) alone could reduce the HVAC energy consumption by 10–35 %. By combining the use of shading and cool paints, the HVAC energy savings are further increased by 2–5 %. The findings of this study offer a novel perspective on the selection of opaque façade technologies, broadening the sustainable design and retrofit options.

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.

HVAC Load Forecasting Based on the CEEMDAN-Conv1D-BiLSTM-AM Model

Heating, ventilation, and air-conditioning (HVAC) systems consume approximately 60% of the total energy consumption in public buildings, and an effective way to reduce HVAC energy consumption is to provide accurate load forecasting. This paper proposes a load forecasting model CEEMDAN-Conv1D-BiLSTM-AM which combines empirical mode decomposition and neural networks. The load data are decomposed into fifteen sub-sequences using complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN). The neural network inputs consist of the decomposition results and five exogenous variables. The neural networks contain a one-dimensional convolutional layer, a BiLSTM layer, and an attention mechanism layer. The Conv1D is employed to extract deep features from each input variable, while BiLSTM and the attention mechanism layer are used to learn the characteristics of the load time series. The five exogenous variables are selected based on the correlation analysis between external factors and load series, and the number of input steps for the model is determined through autocorrelation analysis of the load series. The performance of CEEMDAN-Conv1D-BiLSTM-AM is compared with that of five other models and the results show that the proposed model has a higher prediction accuracy than other models.

Is IoT monitoring key to improve building energy efficiency? Case study of a smart campus in Spain

This work validates and demonstrates the potential of a methodology based on a continuous monitoring system with real time measurements, as a key support to make decisions on buildings energy systems based on quantitative data. For this work, a continuous loop-based monitoring methodology was designed and implemented to enhance the performance of Heating Ventilation and Air Conditioning (HVAC) systems by improving the energy efficiency of university buildings and the quality of life of the people who use them. The results from the case study, used to demonstrate the proposed methodology, can be extrapolated to the entire university in order to transform it into a smart campus. The case study also shows that the continuous measurement of temperatures allows to avoid excessive heating, reducing unnecessary energy consumption. Additionally, the case study shows the importance of monitoring CO2 concentration in rooms where occupancy is fluctuating, since it enables a control of the HVAC system based on the instantaneous CO2 level (and predictive in the immediate aftermath). This proposed measurement can save between 40% and 70% of HVAC energy consumption. The positive results obtained in this research show that implementing Internet of Things (IoT) smart ecosystems contribute to promote data-driven decisions in public tertiary buildings among decision-makers, politicians, and other stakeholders.

Benchmarking high performance HVAC Rule-Based controls with advanced intelligent Controllers: A case study in a Multi-Zone system in Modelica

The design, commissioning, and retrofit of heating, ventilation, and air-conditioning (HVAC) control systems are crucially important for energy efficiency. However, designers and control contractors adopt ad-hoc control sequences based on diffused and fragmented information; therefore, most of the existing control sequences are diverse and sub-optimal. ASHRAE Guideline 36 (GDL36), High-performance Sequences of Operation (SOO) for HVAC Systems, was thus developed to provide standardized and high-performance rule-based HVAC control sequences with the main focus on maximizing energy efficiency. However, only limited studies verify the performance of these high-performance rules-based control and most of these studies focus on the single-zone systems. In this Modelica-based simulation study, the high-performance rule-based control sequences from GDL36 were compared to the state-of-the-art intelligent controllers (i.e., optimization-based controller (OBC) and deep reinforcement-learning-based controller (DRLC)) in terms of the energy efficiency and thermal comfort. The performance evaluation was conducted in a medium-sized office building with a multi-zone Variable Air Volume (VAV) cooling system. The OBC and DRLC replaced the GDL36 airside supervisory level control loops. In other words, the optimal supply air temperature (SAT) and static differential pressure (DP) setpoints were determined by the optimization problems in OBC and trained control policy by DRLC. The OBC and DRLC were formulated to minimize the HVAC energy consumption and zone air temperature violation. The OBCs with different control intervals and DRLCs with different hyperparameters in the Proximal Policy Optimization (PPO) algorithm were exploited and fine-tuned. The simulation results show that the GDL36 has a comparable energy performance (within a 3% deviation) with DRLC in the cases with high or mild cooling loads. It also has a comparable energy performance (within a 3% deviation) with OBC in the case of a high cooling load. However, the case with GDL36 consumed 7% more energy in the testing shoulder period. For the thermal comfort metric, the GDL36 has slightly more zone air temperature violations in all testing scenarios compared to the two intelligent controllers. From this case study, we can conclude that the GDL36 has demonstrated its comparable performance in terms of energy efficiency and thermal comfort with the two intelligent controllers. The GDL36 airside SOO is good enough considering the complexity and tuning efforts of the intelligent controllers.

An intelligent HVAC control strategy for supplying comfortable and energy-efficient school environment

In South Korea, school buildings require significant energy inputs for heating and air-conditioning, and the majority of the occupants are adolescent students, whose health and cognitive performance are vulnerable to poor indoor air quality (IAQ) and thermal discomfort. Using field measurements, some previous studies have reported that some Korean schools have poor IAQ and thermal conditions. Thus, it is necessary to develop effective heating, ventilation, and air-conditioning (HVAC) control strategies to improve the indoor environment and reduce energy consumption. Therefore, this study proposes an intelligent HVAC integrated control strategy that can improve indoor environmental quality (IEQ) and reduce energy consumption in school buildings. The proposed strategy utilizes an integrated neural network prediction model for IEQ and a heuristic method that can optimize control objectives (i.e., the predicted mean vote [PMV], carbon dioxide [CO2], particulate matter with diameters of 10 and 2.5 μm [PM10 and PM2.5, respectively], and HVAC energy consumption). To evaluate the control performance of the proposed strategy, the present study employs two base algorithms (i.e., a rule-based and a non-adaptive control approach) under non-disturbance and forcing disturbance scenarios. The control failure period for PMV is found to be 1.6420% and 9.4773% of the total occupancy period under the non-disturbance and forcing disturbance scenarios, respectively, while CO2 control failure does not occur under either scenario. The control failure periods for PM10 and PM2.5 were 5.1676%, and 7.1844%, respectively, under forcing disturbance. Under the non-disturbance scenario, the proposed strategy consumed 2,467.07 kWh and 870,26 kWh for heating and cooling, respectively, representing 91.1% and 84.08% of that for the rule-based algorithm. The proposed strategy can thus effectively improve the IEQ of a building and has the potential for use in the development of integrated environmental management solutions for buildings.

End-to-End Deep Reinforcement Learning Control for HVAC Systems in Office Buildings

The heating, ventilation, and air conditioning (HVAC) system is a major energy consumer in office buildings, and its operation is critical for indoor thermal comfort. While previous studies have indicated that reinforcement learning control can improve HVAC energy efficiency, they did not provide enough information about end-to-end control (i.e., from raw observations to ready-to-implement control signals) for centralized HVAC systems in multizone buildings due to the limitations of reinforcement learning methods or the test buildings being single zones with independent HVAC systems. This study developed a model-free end-to-end dynamic HVAC control method based on a recently proposed deep reinforcement learning framework to control the centralized HVAC system of a multizone office building. By using the deep neural network, the proposed control method could directly take measurable parameters, including weather and indoor environment conditions, as inputs and control indoor temperature setpoints at a supervisory level. In some test cases, the proposed control method could successfully learn a dynamic control policy to reduce HVAC energy consumption by 12.8% compared with the baseline case using conventional control methods, without compromising thermal comfort. However, an over-fitting problem was noted, indicating that future work should first focus on the generalization of deep reinforcement learning.

Data-driven simulation for energy and local comfort optimization: Case study of a laboratory

Recent control strategies for heating, ventilation and air conditioning (HVAC) in buildings consider ambient conditions, spatial temperature distribution and occupancy density to maximize occupant comfort at minimum energy. Simulators capturing the energy and thermal behaviour of buildings with HVAC systems provide a convenient platform for developing and testing such control strategies. Commonly used simulators are limited to zonal temperature simulation and are unable to model spatial distribution of temperature within the zones. Thus, these simulators cannot be used for control development that uses finer spatial temperature measurements. This paper proposes a simulation framework that emulates the HVAC behavior and energy consumption as well as temperature distribution in actual buildings. Data-driven techniques are used to facilitate model development and calibration. Since the simulator generates spatial thermal distribution, it is used to devise and test air conditioner (AC) set-point control policy that optimizes local comfort as opposed to the usual zonal comfort. The control thus designed is applied to optimize the AC setpoint schedules in a live academic setting and is shown to induce more energy savings than the control based on zonal comfort optimization.

Investigating the potential energy savings of MVHR in automotive HVAC systems

The environmental impact of ICEVs (internal combustion engine vehicles) have led to increasing numbers of EVs (electric vehicles) and plug-in hybrid vehicles being developed. However, current HVAC (heating, ventilation and air conditioning) systems can heavily impact energy consumption, reducing EV range. MVHR (mechanical ventilation with heat recovery) is an approach to minimise the HVAC energy consumption currently used in buildings to meet the Passivhaus building standard and can be used for both heating and cooling. In this work a MATLAB Simulink model and a road load model are used to quantify potential energy reduction and corresponding range savings from an automotive MVHR HVAC system. The model is calibrated against data from the industry sponsor for a baseline non-MVHR vehicle. The results show a 74.3%–94.9% HVAC energy consumption and corresponding range savings of a mean average of 8.8%–11.0% range savings over the baseline case without MVHR, and a corresponding 0.5%–2.5% range increase over the industry sponsor’s vehicle’s range under certification conditions. The work concludes that the application of MVHR technology to automotive cases is beneficial.

Real-time building heat gains prediction and optimization of HVAC setpoint: An integrated framework

Heating, ventilation and air-conditioning (HVAC) systems are responsible for a large part of building energy consumption. Lowering HVAC's energy consumption without compromising occupants' thermal satisfaction has been the focus of recent research. Existing studies showed that this requirement could be met by optimizing HVAC setpoints or using demand-based control strategies. However, such strategies require coupling with dynamic information such as occupancy to be effective. This study proposes an integrated framework to achieve real-time optimization of HVAC setpoints. This framework has three core components: a vision-based camera for detection, predictive HVAC energy and thermal comfort models, and HVAC temperature setpoint optimizer. Vision-based cameras were used to identify activities of occupants and equipment usage and predict a real-time value of internal heat gains. By feeding the information into the predictive HVAC energy and thermal comfort models, the HVAC load (heating or cooling) and the level of occupants' thermal dissatisfaction, predicted percentage dissatisfied were obtained using developed models, based on shallow neural networks. Afterwards, the HVAC temperature setpoint optimizer determined an optimal temperature setpoint following two optimization rules. When occupants were absent, HVAC system would be turned off to save energy. When they were present, an optimal temperature setpoint was determined by using Pareto-front. Based on the initial findings, the utilization of this framework can potentially lead to a reduction of HVAC heating energy by up to 36.8% and occupants' thermal dissatisfaction by up to 5.26%. In the summer, the savings of HVAC cooling energy would range from 3.5% to 33.9%, whilst occupants' thermal dissatisfaction could be decreased by 0.17–2.89%. Overall, this paper paved a path to achieve a win-win on HVAC energy savings and comfort by combining artificial intelligence techniques and building services.

Impacts of data preprocessing and selection on energy consumption prediction model of HVAC systems based on deep learning

Accurate energy consumption prediction is the basis of predictive control for heating, ventilation and air conditioning (HVAC) systems. Data-driven models are widely used for energy consumption prediction. The prediction accuracy can be affected by data preprocessing or selection, which are not well studied yet. This paper attempts to study the impacts of different data processing methods and feature selection on the HVAC energy consumption prediction based on data-driven models. Long and short-term memory models are developed based on historical data to predict the day-ahead hourly energy consumption of HVAC systems. Two data smoothing methods, Gaussian kernel density estimation and Savitzky-Golay filter, are selected and compared. The impacts of feature selection, training set volumes and update frequency of models are analyzed and compared. To obtain general results of the above problems, three office buildings are selected. Results show that the smoothing methods can not ensure the improvement in accuracy by removing the exceptional data in the raw data which arise reasonably in practice. The inputs with raw 1-day historical energy consumption, the dry-bulb temperature and dew-point temperature in the next day, holiday type and day type is recommended to predict day-ahead HVAC energy consumption. It is recommended to use a larger training set if computing cost is acceptable. The model should be updated after being used for over 7 weeks. This study would guide the development of prediction models in practice.

Coloured low-emissivity films for building envelopes for year-round energy savings

Buildings are responsible for over 40% of total US energy use, of which about 40% is directly related to the operation of heating, ventilation and air-conditioning (HVAC) systems. Saving energy to heat and cool buildings would contribute substantially to sustainability. Here we propose a category of coloured low-emissivity films for building walls that constitute the main component of the building thermal envelope. We demonstrate high reflectance (~90%) in infrared wavelength range and selective reflectance in the visible light wavelength range for desired colours. These films can help minimize radiative heat exchange between the indoor and outdoor environments, thus saving energy for all-year cooling and heating while satisfying the required aesthetical effect. Simulations show that these films can help reduce heat gain and loss by up to 257.6 MJ per installation wall area annually. In the case of a typical midrise apartment building, the HVAC system can save up to 124.46 GJ (equal to 9.87% of the building’s HVAC energy consumption). By rough estimation, a global CO2 emission reduction of 1.14 billion metric tons annually could be achieved. Our work provides insights for innovative energy-saving building envelope materials that can help achieve global carbon neutrality and sustainability.

Predictive modelling and optimization of HVAC systems using neural network and particle swarm optimization algorithm

The concept of maintaining indoor environmental quality comprising building indoor temperature, relative humidity, CO2, and volatile organic compound (VOC) level poses new challenges to the optimal operation of heating, ventilation and air-conditioning (HVAC) systems. While existing case studies demonstrate the energy-saving potentials for efficient HVAC operation, there is a lack of studies quantifying energy savings whilst considering indoor environmental conditions. This study proposes a state-of-the-art modelling and optimization approach to minimize the energy consumption of the HVAC systems without compromising indoor environmental quality. While the primary objective of ensuring optimal operation of HVAC systems is to minimize energy consumption, controlling indoor environmental parameters to remain within the acceptable range imposes excess energy use. These two conflicting objectives constitute a multi-variable constrained optimization problem that has been solved using a particle swarm optimization (PSO) algorithm. Real-time predictive models are developed for the individual indoor environmental parameters and HVAC energy consumption using a Nonlinear Autoregressive Exogenous (NARX) neural network (NN). During model development, models' performance is optimized in terms of complexity, predictive accuracy, and ease of application to a real system. The proposed predictive models are then optimized to provide an optimal control setting for the HVAC systems considering seasonal variations. The results indicate that it is possible to reduce 7.8% of total energy, without negotiating indoor environmental conditions, e.g., air temperature 19.60–28.20°C and relative humidity 30–65% as per ASHRAE Standard 55, and CO2 ≤ 800 ppm and VOC ≤1000 ppm as per AS 1668.2.

A social constructionist approach to managing HVAC energy consumption using social norms – A randomised field experiment

This study tests the hypothesis that thermal comfort is socially constructed and as such, social norms will be effective in influencing Heating Ventilation and Air-Conditioning (HVAC) consumptions towards more sustainable levels. Within the framework of a randomized field experiment, the response of hotel guests to message prompts to set their room thermostat to a prescribed temperature was observed. Response behavior was monitored using temperature data logging devices which were placed in the hotel rooms. The data loggers recorded temperature readings from a sample size of 1047 guest stays. The findings suggest that social norms are effective in influencing hotel guests’ room temperature settings, indicating that thermal comfort may not only be a physiological and psychological need, but most importantly a social construct. The implication is that the future of the current unsustainable trend in resource consumption and Green House Gas pollution, driven by the increasing adoption of, and demands for, HVAC systems in buildings, can be modified towards more sustainable levels.

Liquid Thermo-Responsive Smart Window Derived from Hydrogel

Summary Buildings account for 40% of global energy consumption, while windows are the least energy-efficient part of buildings. Conventional smart windows only regulate solar transmission. For the first time, we developed high thermal energy storage thermo-responsive smart window (HTEST smart window) by trapping the hydrogel-derived liquid within glasses. The excellent thermo-responsive optical property (90% of luminous transmittance and 68.1% solar modulation) together with outstanding specific heat capacity of liquid gives the HTEST smart window excellent energy conservation performance. Simulations suggested that HTEST window can cut off 44.6% heating, ventilation, and air-conditioning (HVAC) energy consumption compared with the normal glass in Singapore. In outdoor demonstrations, the HTEST smart window showed promising energy-saving performance in summer daytime. Compared with conventional energy-saving glasses, which need expensive equipment, the thermo-responsive liquid-trapped structure offers a disruptive strategy of easy fabrication, good uniformity, and scalability, together with soundproof functionality that opens an avenue for energy-saving buildings and greenhouses.

Control and communication between PLC S7-1200 and ATV310 drive via modbus protocol

Variable Frequency Drives (VFDs) are electronic power controllers that allow for accurate control of the speed of alternating current (AC) induction motors that used in many kinds of machines including fans, pumps and compressors. These motors are used in most heating, ventilation and air-conditioning (HVAC) systems and account for a significant percentage of the total HVAC energy consumption. More efficient operation of these motors using VFDs can result in significant energy savings. Besides, the communication between VFDs and PLC for Supervisory Control And Data Acquisition (SCADA) is also important. Modbus protocol has many kinds as RS485, RTU, Profinet,...In this paper we will present the communication between a PLC Siemens S7-1200 and an ATV310 Drive (Schneider) via Modbus RTU protocol which is supported.

Fast prediction for multi-parameters (concentration, temperature and humidity) of indoor environment towards the online control of HVAC system

Heating, ventilation and air conditioning (HVAC) systems are the most energy-consuming building implements for the improvement of indoor environmental quality (IEQ). We have developed the optimal control strategies for HVAC system to respectively achieve the optimal selections of ventilation rate and supplied air temperature with consideration of energy conservation, through the fast prediction methods by using low-dimensional linear ventilation model (LLVM) based artificial neural network (ANN) and low-dimensional linear temperature model (LLTM) based contribution ratio of indoor climate (CRI(T)). To be continued for integrated control of multi-parameters, we further developed the fast prediction model for indoor humidity by using low-dimensional linear humidity model (LLHM) and contribution ratio of indoor humidity (CRI(H)), and thermal sensation index (TS) for assessment. CFD was used to construct the prediction database for CO2, temperature and humidity. Low-dimensional linear models (LLM), including LLVM, LLTM and LLHM, were adopted to expand database for the sake of data storage reduction. Then, coupling with ANN, CRI(T) and CRI(H), the distributions of indoor CO2 concentration, temperature, and humidity were rapidly predicted on the basis of LLVM-based ANN, LLTM-based CRI(T) and LLHM-based CRI(H), respectively. Finally, according to the self-defined indices (i.e., EV, ET, EH), the optimal balancing between IEQ (indicated by CO2 concentration, PMV and TS) and energy consumption (indicated by ventilation rate, supplied air temperature and humidity) were synthetically evaluated. The total HVAC energy consumption could be reduced by 35% on the strength of current control strategies. This work can further contribute to development of the intelligent online control for HVAC systems.

Nationwide HVAC energy-saving potential quantification for office buildings with occupant-centric controls in various climates

The occupant-centric control (OCC) is receiving an increasing attention since it could reduce building heating ventilation and air-conditioning (HVAC) system energy consumptions while not affecting the occupant thermal comfort. This paper aims to quantify the nationwide energy-saving potential of implementing the occupant-centric HVAC controls in typical office buildings. First, the medium office and large office from the Department of Energy (DOE) Commercial Prototype Building Models (CPBM) were enhanced to have detailed layouts and dynamic occupancy schedules. Then, a comprehensive simulation plan was created by incorporating the multiple zone-level and system-level occupant-centric building HVAC controls recommended by the updated ASHRAE Standard 90.1 – 2019 and ASHRAE Guideline 36 – 2018. Three control scenarios with different occupancy sensing methods were identified in this simulation plan. A nation-wide parametric analysis, which includes two building types, three occupancy sensing scenarios, two building code versions, and 16 U.S. climate zones, was carried out. The simulation results of the key control variables and HVAC energy consumption suggest that generally, both the occupancy presence sensor and occupant counting sensor could achieve energy savings for the office buildings in the majority of the scenarios. However, compared with the occupancy presence sensor, which could support both the temperature setpoint reset and operational breathing zone airflow rate reset for the unoccupied zones, the occupant counting sensor only brings a marginal benefit. Besides, a higher HVAC energy-saving ratio could be achieved in the heating-dominated zone, since the energy reduction brought with the minimum outdoor airflow rate reset is stronger in the heating mode.