Heating, Ventilation, And Air Conditioning Energy Research Articles

Dynamic predictions for the composition and efficiency of heating, ventilation and air conditioning systems in urban building energy modeling

The composition and efficiency of heating, ventilation and air conditioning (HVAC) systems are crucial inputs for urban building energy modeling (UBEM). However, in current studies, the heterogeneity of HVAC systems is often ignored, leading to huge modeling errors. To address these issues, this study developed a three-step prediction approach that can dynamically forecast the composition of HVAC systems (SystemID), the efficiency of heat sources and the efficiency of entire systems at the urban scale. The performance of the prediction models was evaluated through the ten-fold cross-validation. The results showed that the accuracy in predicting the composition could reach 0.87, and the coefficient of determination (R2) was greater than 0.93 in efficiency forecasts. The accuracy of the developed approach was further evaluated through the testing set in two situations, i.e., with the predicted and actual SystemID data. In the first situation, the overall accuracy in predicting the composition reached 78.2 %, and the R2 for forecasting the system efficiencies was over 0.6. Then, with the SystemID assumed to be accurate, the R2 increased to more than 0.87. To analyze the performance of the developed method in energy use predictions, two case regions in Changzhou and Nanjing were used, respectively. The simulation results were compared with the scenario in which the system efficiencies were determined based on the national standard. The results showed that the developed method could enhance the accuracy of total energy use predictions by approximately 10 % and HVAC energy use predictions by roughly 40 % at both urban and building scales. This helps policy-makers craft energy-saving strategies more reasonably.

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.

Field test of machine-learning based mean radiant temperature estimation methods for thermal comfort-integrated air-conditioning control improvement and energy savings

Advanced control strategies for heating, ventilation, and air-conditioning (HVAC) systems aim to enhance both buildings' energy efficiency and occupant thermal comfort. Despite their potential, real-world demonstration studies on such advanced technologies are still lacking. This study presents a field demonstration of a real-time predicted mean vote (PMV)-based HVAC control strategy in a typical residential building under hot and dry climate conditions. This study introduces an advanced thermal comfort-based controller (TCC) for the PMV-based HVAC control. TCC continually assesses indoor and outdoor thermal conditions and adjusts the HVAC setpoint temperature to optimize real-time energy use while ensuring satisfactory indoor thermal comfort. Machine learning models for the PMV-based HVAC control system are employed to estimate a mean radiant temperature (MRT) point, which is one of the variables used to calculate real-time PMV values. Three machine learning models (i.e., linear regression, regression trees, and artificial neural network) are adopted in this study with non-stationary real-time input values, including times, pre-determined setpoints, and indoor and outdoor temperatures. The developed TCC is installed in a full-scale experimental house in Kuwait, which is under hot and dry climate conditions, to assess the house's indoor thermal comfort and AC energy efficiency performance. Results indicate that the TCC provides better thermal comfort performance compared to the non-TCC case, including up to a 60% improvement in PMV. The proposed TCC controlled by the machine learning methods demonstrates HVAC energy savings potential of over 20% while meeting the desired thermal comfort levels in the experimental building. These findings are expected to be valuable, as they can contribute to reducing cooling energy in residential buildings in hot and dry climate conditions.

Comparing the life cycle costs of a traditional and a smart HVAC control system for Australian office buildings

Many smart technologies have been introduced in buildings with the aim to reduce the energy and GHG emissions associated with their operation, particularly through improved control systems for regulating heating, ventilation and air conditioning (HVAC) equipment. Despite their energy saving potential, only a few studies have comprehensively assessed the costs associated with their practical implementation from a life cycle perspective. Accordingly, this study quantifies and compares the life cycle costs of a smart HVAC control system with that of a traditional control system, in the context of an Australian office building. For both systems, the required hardware are specified based on the characteristics of these systems and the layout of the serviced spaces in the reference building. The costs incurred over the period of assessment are quantified using the net present cost (NPC) approach. To evaluate the effects of these control systems on the operational energy costs of the building HVAC system, the control logics of both these systems are modelled through building energy simulations. The results show that, over the period of assessment, the smart control system incurred a higher total cost compared to the traditional control system. However, the findings from the simulations show that the HVAC energy cost savings achieved through the specification of the smart control system offset the additional cost incurred to deploy this system over the traditional control system. The smart control system resulted in HVAC operational cost savings between 9 % and 10 % compared to the traditional control system. Sensitivity analyses indicated that the total life cycle costs varied between −27 % and +50 %, with the discount rate and energy price increase rate being the most influential parameters.

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.

HVAC energy cost minimization in smart grids: A cloud-based demand side management approach with game theory optimization and deep learning

In this paper, we present a novel cloud-based demand side management (DSM) optimization approach for the cost reduction of energy usage in heating, ventilation and air conditioning (HVAC) systems in residential homes at the district level. The proposed approach achieves optimization through scheduling of HVAC energy usage within permissible bounds set by house users. House smart home energy management (SHEM) devices are connected to the utility/aggregator via a dedicated communication network that is used to enable DSM. Each house SHEM can predict its own HVAC energy usage for the next 24 h using minimalistic deep learning (DL) prediction models. These predictions are communicated to the aggregator, which will then do day ahead optimizations using the proposed game theory (GT) algorithm. The GT model captures the interaction between aggregator and customers and identifies a solution to the GT problem that translates into HVAC energy peak shifting and peak reduction achieved by rescheduling HVAC energy usage. The found solution is communicated by the aggregator to houses SHEM devices in the form of offers via DSM signals. If customers’ SHEM devices accept the offer, then energy cost reduction will be achieved. To validate the proposed algorithm, we conduct extensive simulations with a custom simulation tool based on GridLab-D tool, which is integrated with DL prediction models and optimization libraries. Results show that HVAC energy cost can be reduced by up to 36% while indirectly also reducing the peak-to-average (PAR) and the aggregated net load by up to 9.97%.

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.

Optimal HVAC Energy and Regulation Reserve Scheduling in Power Markets

This paper considers the optimal scheduling of the hourly energy consumed and regulation reserve capacity offered by a Heating, Ventilation and Air-Conditioning (HVAC) system participating in a day-ahead electricity market. We formulate an Integrated hourly energy and regulation reserve Scheduling and Deployment (ISD) problem using high fidelity models of the HVAC and the building structure's thermal storage property. The ISD problem optimizes the hourly costs and benefits resulting from the power consumed, the regulation reserve capacity offered, the occupant thermal comfort utility, and the expected Intra-Hour Costs (IHCs) due to the HVAC's imperfect tracking of the regulation signal broadcasted by the Independent System Operator every few seconds. Addition of the IHCs to the objective function is the major innovation of this paper. It enables optimal HVAC scheduling to internalize expected regulation signal tracking error cost traded-off against incremental occupant thermal discomfort that may result from perfect tracking. The cost causation circle closes by noting that a high hourly regulation reserve offer may result in higher expected IHCs by increasing the associated tracking error and the incremental occupant discomfort. The paper's innovation is achieved by (i) estimating an analytic function of the hourly HVAC scheduling decisions and state variables that approximates the expected IHCs, and (ii) including this exogenously estimated analytic IHCs function in the objective of the hourly ISD optimization problem. Non-convexities and non-linearities introduced to the HVAC optimization problem resulting from high modeling fidelity and the inclusion of the expected IHCs, are addressed efficiently through piecewise convex relaxations that provide tight optimality bounds. Extensive numerical results are finally provided to demonstrate the applicability and performance of our ISD problem formulation on a realistic office building.

Energy optimization for HVAC systems in multi-VAV open offices: A deep reinforcement learning approach

With global warming intensifying and resource conflicts escalating, the world is undergoing a transformative shift toward sustainable practices and energy-efficient solutions. With more than 32% of the global energy used by commercial and residential buildings, there is an urgent need to revisit traditional approaches to Building Energy Management (BEM). Within a BEMSplatform, regulating the operation of Heating, Ventilation, and Air Conditioning (HVAC) systems is more important, noting that HVAC systems account for about 40% of the total energy cost in the commercial sector.This paper offers a Deep Reinforcement Learning (DRL) algorithm as a data-driven approach to controlling HVAC operation to enhance the energy efficiency of commercial buildings with open offices while ensuring thermal comfort for occupants in different zones. Compared to alternative methods such as rule-based models and model-predictive control, data-driven models have shown promising results in optimizing building energy consumption without the need for building-specific thresholds, prior knowledge about the underlying physics of heat distribution, and digital mapping of the airflow. Despite the astonishing performance of modern DRL methods in controlling energy management, a particular energy-saving solution for open-plan offices with multiple Variable Air Volume (VAV) systems, where different zones cannot be treated independently, is still missing. Also, some of the existing methods suffer from key issues such as long training time and lack of generalizability for using over-complicated models, incorporating external factors that are hard to model and characterize, and including factors that are not typically accessible. To solve these issues, we propose a low-complexity DRL-based model with multi-input multi-output architecture for the HVAC energy optimization of open-plan offices, which uses only a handful of controllable and accessible factors. The efficacy of our solution is evaluated through extensive analysis of the overall energy consumption and thermal comfort levels compared to a baseline system based on the existing HVAC schedule from a real case. This comparison shows that our method achieves 37% savings in energy consumption with minimum temperature violation (<1%) of the desired temperature range during work hours. It takes only a total of 40 min for 5 epochs (about 7.75 min per epoch) to train a network with superior performance and covering diverse conditions for its low-complexity architecture; therefore, it easily adapts to changes in the building setups, weather conditions, occupancy rate, etc. Moreover, by enforcing smoothness on the control strategy, we suppress the frequent and unpleasant on/off transitions on HVAC units to avoid occupant discomfort and potential damage to the system. The generalizability of our model is verified by applying it to different building models and under various weather conditions.

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.

Adaptive HVAC System Based on Fuzzy Controller Approach

Heating, ventilation, and air conditioning (HVAC) system performance research has received much attention in recent years. Many researchers suggest a set of appropriate fuzzy inputs that can be used to design fuzzy rules-based smart thermostats or controllers that can respond to demand-controlled ventilation, which in turn optimizes HVAC energy usage and provides satisfactory indoor temperatures. Previous research has focused on limited input parameters, such as indoor occupancy status, ambient temperature, and humidity constraints, which cannot efficiently and precisely manage thermal comfort. Hence, this study proposes a novel fuzzy controller with additional input parameters to keep indoor thermal comfort consistent with the corresponding number of occupants. The process employs an automatic fuzzy rule generation method to simplify the task of generating rules in the fuzzy inference system (FIS) using Mamdani FIS. A design-builder is used for modeling the HVAC systems. Local weather data were used to conduct simulations via EnergyPlus. The thermal comfort analysis using the Fanger model for three different scenarios shows that the proposed FIS controller can successfully respond to the indoor comfort variation in all possible scenarios and ensure a satisfactory comfort level. The proposed method demonstrates up to 50% energy savings if occupants do not worry about comfort.

Retracted: Deep learning-based energy inefficiency detection in the smart buildings

The operation of the heating, ventilation, and air conditioning (HVAC) system is essential for the indoor thermal environment and is significant for energy consumers in commercial properties. Although earlier studies suggested that reinforcement learning controls could increase HVAC energy savings, they lacked sufficient details regarding end-to-end management. Recently, the focus on gathering and analyzing data from smart meters and buildings connected to energy-saving studies has increased. Deep reinforcement learning (DRL) suggests novel methods for operating HVAC systems and lowering energy usage. This paper evaluates energy consumption by Convolution Recurrent Neural Networks (CRNN), and Deep Reinforcement Learning is used. This is intended to forecast energy use under various climatic circumstances, and the processes are assessed under different communication protocols. The suggested control technique might directly accept quantitative elements, such as climate and indoor air quality conditions, as input and control indoor thermal set - points at a supervisory level by utilizing the deep neural network. In a highly effective office area in the Houston area, time series data, CRNN, and DRL are effectively used to uncover new energy-saving options (TX, USA). The article presents 1-year information from the Net Zero, Energy Star, and Leadership in Energy and Environment Design (LEED)-certified building, demonstrating a potential energy savings of 8% with the presented design. The findings demonstrate how useful the suggested strategy is in assisting building owners in locating new potential for energy conservation.

Minimum sensor grid density and configuration to enable CO2-based demand-controlled ventilation in an office building

CO2-based demand-controlled ventilation (DCV) requires placement of CO2 sensors in air handling units (AHUs) as well as in individual zones. In complex multi-zone systems or across building portfolios, the installation and maintenance costs of these sensors are non-negligible. This study explores how CO2 sensor grids can be configured in a sparser manner to allow for CO2-based DCV of spaces adjacent to zones with CO2 sensors that may not have sensing infrastructure themselves. A simulation-based study of a 26-zone office building was conducted under a variety of occupancy schedules, number and placement of zone-level CO2 sensors, and with and without motion detectors to determine how RP-1747 DCV impacted heating, ventilation, and air conditioning (HVAC) energy use and zone CO2 concentrations when implemented traditionally or with adjacent control, for a total of 29,730 simulations. It was found that CO2 sensors installed in approximately 31% of zones could effectively enable CO2-based DCV across the entire floor plate with a negligible impact on the number of hours where CO2 concentrations were elevated, resulting in 7.3% to 17.4% higher HVAC energy savings compared to individual control of spaces in the case study building.

Advancements in Smart Thermostat Technology for Enhanced HVAC Energy Management

This study examines the transformative potential of smart thermostat technology in revolutionizing Heating, Ventilation, and Air Conditioning (HVAC) energy management. Through a combination of literature review and empirical analysis, the research explores the significance of energy-efficient HVAC systems in reducing carbon footprint and energy costs. Traditional thermostat limitations, including inflexible scheduling and lack of user engagement, underscore the need for innovative solutions. The evolution of smart thermostat technology is traced from basic programmable thermostats to advanced systems integrated with learning algorithms, occupancy sensing, and the Internet of Things (IoT). Empirical data from surveys, case studies, and energy consumption analysis showcase the tangible impact of smart thermostat adoption. Energy savings of up to 30% in commercial settings and 25% in residential contexts underscore the technology's potential for efficiency. User satisfaction surveys reveal improved comfort levels and satisfaction with remote control capabilities. Challenges such as installation complexities and data security concerns highlight areas for improvement. Data analytics and machine learning emerge as pivotal in enhancing smart thermostat efficiency, contributing to additional energy savings. Comparisons between smart thermostat models underscore the importance of occupancy sensing and remote control features in optimizing energy efficiency and user satisfaction. The study's findings suggest a promising avenue for transforming HVAC energy management towards a more sustainable and user-centric approach

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.

Architectural Design Strategies for Enhancement of Thermal and Energy Performance of PCMs-Embedded Envelope System for an Office Building in a Typical Arid Saharan Climate

The literature showed many studies that evaluated single or multiple Phase change materials (PCMs) layers in passive, active, or in hybrid configurations for building applications. However, little attention has been given to evaluating the energy performance of buildings when PCMs are used together with other passive design strategies. In this work, the energy performance of an office building in a typical arid Saharan climate is simulated using EnergyPlus when a PCMs-embedded envelope is implemented. The office building was analyzed without/with PCMs using various thicknesses. Results indicated that the annual electrical energy for heating, ventilation and air conditioning (HVAC) could be reduced between 3.54% and 6.18%, depending on the PCM thickness. The performance of the office building, including PCMs, was then simulated using two practical architectural design strategies, namely windows-to-wall ratio (WWR) and rezoning of the interior spaces. Outcomes revealed that the annual energy consumption for HVAC can be reduced from 10% to 15.5% and from 6.1% and 8.54% when WWR is reduced by half to three-quarters, and the perimeter zones are enlarged by one-third to two-thirds of the original space area, respectively. By combining both architectural design strategies and PCM, the annual electrical HVAC energy can be reduced between 12.08% and 15.69%, depending on the design configuration and PCM thickness. This design option provides additional benefits also since it reduces the vulnerability of increasing the lighting and fuel gas heating energy because more perimeter zones are exposed to daylighting and solar radiation, respectively.

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.

The use of cluster analysis to assess thermal comfort in university classrooms

Nowadays, providing health, well-being, productivity and energy efficiency to users inside buildings is essential. Applying these aspects aligned with sustainability becomes necessary to reduce the use of heating, ventilation, and air-conditioning (HVAC) systems. These systems are currently used to provide better thermal conditions to the occupants, who spend around 80% of their time indoors. The actual thermal conditions can be affected by several factors, such as the climatic type of the region, orientation, size, building type, and energy levels, among others. To assess thermal conditions inside buildings, several thermal comfort models have been developed over the years. However, the Predicted Mean Vote (PMV) created by Fanger is still the most common model to assess thermal comfort indoors. In this context, this research aimed to analyze thermal comfort conditions in university classrooms in Southern Brazil. By collecting the environmental and personal variables of thermal comfort and the mean thermal sensation of students through measurements and questionnaires, a total of 519 responses were obtained during the Brazilian autumn. A statistical cluster analysis was performed to classify individuals according to their sensations. Differences between genders were verified and changing indoor temperatures lower in winter would therefore save HVAC energy without impacting occupant comfort.