Building Air Research Articles

Prediction of building HVAC energy consumption based on least squares support vector machines

Air conditioning, as an essential appliance in daily life, has the function of ensuring comfortable room temperature, but it is also accompanied by a large amount of power consumption. Consequently, the study suggests an energy consumption prediction model based on improved genetic algorithm—least squares support vector machine—to accurately predict the energy consumption of building heating, ventilation, and air conditioning. This model uses the improved genetic algorithm for regularization parameter and kernel parameter optimization to prevent overfitting and underfitting issues. According to the testing results, the least squares support vector machine, an upgraded genetic algorithm, may accomplish convergence faster than other algorithms, taking only 0.2 milliseconds to finish. In addition, the average relative error of the improved genetic algorithm- least squares support vector machine did not exceed 0.6%. In the energy consumption prediction for the whole year of 2022, the average error of the improved genetic algorithm-least squares support vector machine was only 2.0 × 106 kWh, and the prediction accuracy could reach up to 97.2%. The above outcomes revealed that the energy consumption prediction model can accurately predict the air conditioning energy consumption, which provides a strong support for the control and optimization of the air conditioning system.

Calibration of a hybrid model for HVAC systems for fault data generation

The application of automated fault detection and diagnosis algorithms has proven to be an effective way to increase the energy efficiency of buildings. While data-driven strategies have demonstrated their potential in developing such algorithms, they require a set of historical data that represents both healthy and faulty equipment operation, which is not a common scenario in real facilities. This article presents a methodology for the generation of synthetic data on both healthy and faulty operation for building heating and air conditioning equipment, which is especially useful for supervision and failure detection in the case of replication and expansion of buildings in tertiary or residential building planning programmes. This work investigates the automatic calibration of a typical air-handling unit hybrid model, composed of the detailed parametric representation of the system components and its control sequence coupled with a simplified thermal load. The model is then extended with fault models that resemble commonly encountered component faults to generate operation data in faulty scenarios. The proposed framework is validated in a real use case with the calibration of an air-handling unit system and its control sequence using 15 days of data gathered from the building management system. The results obtained from the injection of seven typical faults are then compared with the fault-free simulations to highlight and validate their impact on key operating variables. The results prove the capabilities of this methodology to support database generation in the fields of fault detection and advanced maintenance of buildings’ air conditioning systems for building replicas.

Microplastic levels in the indoor air of buildings based on plastic waste recycling in Indonesia

Introduction: Microplastics are a new type of contamination in the environment caused by plastic fragmentation and degradation. The generation of plastic waste increases every year, therefore efforts are made to recycle it into building materials. It is unclear how building use resulting from recycling plastic waste affects the development of microplastics in the atmosphere. The purpose of this study is to determine the airborne concentrations of microplastics in buildings constructed from recycled plastic waste. Materials and methods: The study measured microplastic levels in the air for 30 days in a miniature building made from plastic waste. Samples taken during the dry season in Indonesia in 2023. Air sampling is carried out by passive method. Visual observation of the shape, and amount of microplastics using a microscope. Results: Microplastics are found in the air of building spaces made from recycled plastic waste with varying levels every day. The average level of microplasty in the air for 30 days was 30,8 particles/m2/day. Maximum microplastic rate of 63 particles/m2/day, and minimum rate of 18 particles/m2/ day. The average air temperature when sampling is 25,48 ºC, air humidity is 68,37 % and ultraviolet intensity is 860 mwatt/cm2. Conclusions: Microplastic levels in the air of building spaces made from recycled plastic waste that can not be identified can be identified whether they are still within safe limits or not. This is because there is no regulation in Indonesia even in the world that regulates the safe limit of microplastic levels in the environment.

A joint optimization strategy for electric vehicles and air conditioning systems with building battery configuration

Building air conditioning systems, electric vehicles and battery energy storage systems all provide substantial flexibility for grid operations. However, the joint optimization strategy involving these three demand response resources in buildings has been infrequently studied. This research proposes a day-ahead optimization strategy to coordinate the joint operation of air conditioning systems and electric vehicles. In this framework, cooling load is predicted using an autoregressive exogenous model, while electric vehicle charging load is predicted through Monte Carlo simulations. An evaluation method is introduced to assess the comprehensive benefits of the demand response strategy, considering thermal comfort, economic efficiency, and grid friendliness. Using an office building in Tianjin, China, as a case study, the results indicate that the joint optimization strategy reduces operational costs by 3.71 % and peak electricity load by 38.62 % compared to the original strategy. Furthermore, it enhances the grid friendliness of the energy system by 42.64 %. The configuration of the battery energy storage system is also explored in conjunction with the optimization strategy to further improve grid friendliness. The impact of economic factors and thermal comfort on the configuration of the battery energy storage system is discussed. In the case study, raising the upper temperature limit by 1 °C can save at least 17.1 % in capacity, while battery energy storage system investment and operational costs can respectively be reduced by 55.04 % and 27.14 % within the thermal comfort range.

Development of an FDD model for an existing building using transfer learning

Building heating, ventilation, and air conditioning (HVAC) systems are affected by several errors that can cause thermal discomfort to occupants and waste energy in buildings. Therefore, early and accurate Fault Detection and Diagnosis (FDD) is vital for enhancing the system’s efficiency. Data-driven FDD is promising because it is convenient compared to the first-principles-based rule set that demands in-depth expertise. However, to realize data-driven FDD for real-life cases, the data-imbalance problem in FDD must be solved. In this study, the authors suggest a novel approach that generates synthetic data from a building system simulation tool, HVACsim+, using them as a source model to apply transfer learning to a target air handling unit system. Even though from the existing target system only the normal operational data were available, the transfer learning approach was satisfactory, confirming that the proposed method effectively mitigates data imbalance in developing data-driven FDD.

Unsupervised Fault Detection for Building Air Handling Unit Systems Using Deep Variational Mixture of Principal Component Analyzers

Unsupervised Fault Detection for Building Air Handling Unit Systems Using Deep Variational Mixture of Principal Component Analyzers

The Cost-Optimal Control of Building Air Conditioner Loads Based on Machine Learning: A Case Study of an Office Building in Nanjing

Building envelopes and indoor environments exhibit thermal inertia, forming a virtual energy storage system in conjunction with the building air conditioner (AC) system. This system represents a current demand response resource for building electricity use. Thus, this study centers on the CatBoost algorithm within machine learning (ML) technology, utilizing the LASSO regression model for feature selection and applying the Optuna framework for hyperparameter optimization (HPO) to develop a cost-optimal control method for minimizing building AC loads. This method addresses the challenges associated with traditional load forecasting and control methods, which are often impacted by environmental temperature, building parameters, and user behavior uncertainties. These methods struggle to accurately capture the complex dynamics and nonlinear relationships of AC operations, making it difficult to devise AC operation and virtual energy storage scheduling strategies effectively. The proposed method was applied and validated using a case study of an office building in Nanjing, China. The prediction results showed coefficient of variation in root mean square error (CV-RMSE) values of 6.4% and 2.2%. Compared with the original operating conditions, the indoor temperature remained within a comfortable range, the AC load was reduced by 5.25%, and the operating energy costs were reduced by 24.94%. These results demonstrate that the proposed method offers improved computational efficiency, enhanced model performance, and economic benefits.

Exploring the natural ventilation potential of urban climate for high-rise buildings across different climatic zones

The natural cooling capacity of the environment can effectively reduce the building energy consumption by natural ventilation (NV). For architects, developing NV strategies requires a comprehensive understanding of the distribution characteristics of urban meteorology. This study investigates the vertical characteristics of urban local-scale meteorological parameters across climatic zones. It aims to assess the climatic potential for NV of outdoor air in high-rise buildings. Firstly, using Weather Research and Forecasting (WRF) model coupled with Local Climate Zone (LCZ) data and sounding data, the vertical distribution of meteorological parameters are explored in typical urban areas across different climatic zones. Then, based on an adaptive thermal comfort model, the climatic potential is assessed for NV in high-rise buildings via the indicator of annual natural ventilation hour (NVH). Finally, the characteristics of urban meteorology is further is analyzed in terms of its impact on the natural ventilation potential (NVP) in each climatic zone. Results indicate that air temperature decreases with height from 0 to 500 m, with more pronounced temperature gradients in compact building areas. Near the ground level, Kunming (Temperate Zone) has the most NVH, reaching 4,840 h annually. However, when building height exceeds 100 m, Guangzhou (Hot Summer and Warm Winter, HSWW) becomes more suitable for NV. In cold regions, low temperatures limit NVP during winter, while high humidity constrains it in Temperate and HSWW zones. Overall, the annual NVP in high-rise building areas exceeds that of low-rise building areas. These distribution characteristics of NVP are anticipated to promote the utilization of natural resources for sustainable urban development.

A comprehensive benchmark dataset for the validation of building component heat, air, and moisture (HAM) models

A comprehensive benchmark dataset for the validation of building component heat, air, and moisture (HAM) models

Harvesting condensate wastewater from commercial buildings air handling unit (AHU) drainage: An opportunity for water conservation

Water wastage is a matter of great concern, especially in this era of rapid development. Water consumption in high-rise and large-sized buildings is very high. Furthermore, high-rise buildings use a lot of water to generate the use of HVAC (Heating, Ventilation, and Air Conditioning) machines. However, the waste of water from this machine is also very disturbing. Therefore, it is important for us to realize that various ways and technologies can be used and utilized to prevent waste from happening. This research focuses on the wastage of water and waste water from air handling unit machines in high-rise commercial buildings around Penang, Malaysia. To make this study a success, several methods have been used, including interviews, observation at the study site, and conducting research through newspapers, journals, reports, and so on. Through this method, data to be analyzed can be collected. In this study, several factors lead to the waste of water from the Air Handling Unit machine, which will have adverse effects on the machine, the building, and also the environment such as condensate and drainage. To overcome the problem, it is important to implement proactive routine maintenance practices for the HVAC system to reduce the AHU wastewater. The finding incorporation of water-saving methods, such as using AHU wastewater for other non-potable purposes, is a practical and ecologically appropriate approach. This research emphasizes the potential for large water savings and environmental advantages in commercial buildings, calling for greater adoption of such methods in facility management and building operations.

Явные решения в проблеме звукоизоляции с использованием многослойных структур при нормальном прохождении волн

The problem of sound isolation at ultra-low frequencies (30–200 Hz) by using multilayered structures is con-sidered, taking into account the normal wave propagation and an arbitrary number of layers. It is assumed that the multilayered structure consists of parallel acoustic layers, connected to each other along vertical boundary lines; acoustic half-spaces (air) are located to the left and right of the structure. For the problem, we define the acoustic parameters of the structure. The wave equations of the harmonic process are described for the left and right half-spaces of the structure, using the Helmholtz equation and a system of linear algebraic equations with given boundary conditions. Besides, explicit analytical solutions are obtained to find the transmission coefficient (transparency coefficient), with an arbitrary number of layers. Examples of solution for low-frequency sound absorption for building structures with different numbers of layers of alternating building materials and air spaces are considered. The effectiveness of using mixed structures, in which air layers are sandwiched between high impedance layers, for low-frequency sound absorption has been confirmed by assessing the level of noise reduction.

Improved robust model predictive control for residential building air conditioning and photovoltaic power generation with battery energy storage system under weather forecast uncertainty

The rising demands for comfort alongside energy conservation underscore the importance of intelligent air conditioning control systems. Model Predictive Control (MPC) stands out as an advanced control strategy capable of addressing these demands. However, accurate prediction of all relevant variables remains a challenge in practical scenarios, complicating MPC’s ability to devise effective control actions amid prediction inaccuracies. To counteract this issue, this paper introduces an enhanced Double-Layer Model Predictive Control (DLMPC) algorithm. This innovative approach adjusts for discrepancies between forecasted and actual values without the need for additional variables and models, thereby reducing the adverse effects of prediction errors. Additionally, we develop precise models for room temperature simulation and for calculating air conditioning (AC) load and energy consumption, grounded in empirical data from residential settings and AC performance tests. Validation of these models demonstrates their efficacy in enabling MPC to formulate efficacious control strategies. When juxtaposed with a baseline model, the DLMPC algorithm significantly improves temperature regulation accuracy by up to 15.12% and achieves a 10.50% reduction in energy consumption over the heating season.

Applying OPTICS with and without PCA for fault detection of fan coil units using building automation system data

In building operations, Heating, Ventilation, and Air Conditioning (HVAC) system faults lead to substantial energy waste. Due to the rapid growth in the availability of sensing data and advancements in computational technology, AI-based methods have emerged as a powerful tool for Fault Detection and Diagnosis (FDD) of HVAC systems, significantly enhancing the efficiency of building operations. Several studies support supervised learning methods for FDD; however, these are limited in practice due to their reliance on labeled datasets, which are generally unavailable. In this context, unsupervised methods, notably those using cluster analysis, show great promise, particularly for analysing Building Automation System (BAS) data. This paper presents a methodology using OPTICS – an ordered clustering algorithm – for fault detection and explores the impact of PCA on its accuracy for fault detection, using both published and full-scale historical building data. While beneficial for reducing computational costs and enhancing noise reduction — thereby generally improving the clarity of cluster differentiation — PCA’s dimensionality reduction may result in the loss of critical information, leading to some clusters being less discernible or entirely undetected. These overlooked clusters could be indicative of underlying faults, and their obscurity represents a significant limitation of PCA when identifying potential faults in complex data.

Energy efficiency of life support systems of buildings in «green construction»

Purpose: develop an integrated air conditioning system with the environment, which will allow to reduce energy consumption due to lowering the temperature of air taken from outside from natural or artificial green areas. Methods: the basis of the solution to the task of developing an integrated system was the use of three well-known methods of greening of home territories: traditional, non-traditional and container. Because green spaces, in the warm period of the year, allow you to naturally reduce the parameters of the outside air, which are used for the needs of the building's ventilation and air conditioning systems. Findings: due to the complex use of all types of landscaping, it is possible to reduce the energy consumption for work of ventilation and air conditioning systems by reducing the temperature of air taken from the outside from natural or artificial green areas. Due to this, when developing integrated air conditioning systems, we can achieve: a decrease in the temperature of the supply air from the landscaping area, which will reduce not only the energy costs for cooling and humidification, but also the reduction of pollution of the supply air; when using trees and shrubs of certain breeds, which emit phytoncides and other beneficial secretions, in the outdoor air intake area, it will improve the quality of incoming air. Practical implication: the developed integrated air conditioning system with the environment will allow to reduce the influence of the outside air temperature on the indoor air temperature due to the integrated use of all types of greening. Originality: a review of literary sources showed that today the possibility of taking outside air from existing or artificially created green areas is not used in the design of ventilation and air conditioning systems.

An Analysis of Energy Consumption In The Goverment Buildings’ In Indonesian Border

In this research, energy consumption analysis was conducted at the Main building regent’s office of nunukan. The analysis was conducted on the main variables of energy efficiency, namely: measurement of temperature and relative humidity, calculation of Overall Thermal Transfer Value (OTTV) and Roof Thermal Transfer Value (RTTV), calculation of Energy Consumption Intensity (IKE) in air-conditioned and non-air-conditioned rooms as well as an analysis of opportunities to increase the efficiency of energy consumption in the buildings.. Based on the calculation and result of IKE in both buildings, they are still considered in the category of EFFICIENT. However, based on the measurement of temperature and relative humidity, it shows that in both buildings air conditioning is still necessary to achieve the level of thermal comfort, therefore an increase of efficiency in the load is needed to avoid wastage.

CFD Analysis of Natural Convection on the Outer Surface of the Containment of APWR Model

To improve the safety of nuclear power plants, a modern NPP such as Advance Pressurize Water Reactor (APWR) is equipped with a safety feature called PCCS (Passive Containment Cooling System), where naturally circulating air cools the outer surface of the containment which is used to remove decay heat released inside the containment vessel. The decay heat from the reactor core will be transferred out through the conduction mechanism and the natural convection mechanism on the outer surface of the containment vessel. Then, the hot air that forms in the gap between the containment and its baffles will rose through the chimney located at the top of the concrete building and cold air will enter through the inlet to create a natural circulation cycle. To obtain the effectiveness of the use of baffles, as a comparison, it is necessary to analyze the natural convection heat transfer on the outer surface of a containment vessel that is not equipped with a baffle. In this research, CFD analysis of natural convection on the outer surface of the containment of APWR reactor model has been done. Based on the model developed, the analysis was done to get temperature and convection heat transfer coefficient in the air flow on the containment surface. Heat flux in the containment surface varied from 500 W/m2 to 2.000 W/m2 with an increase of 500 W/m2 intervals. Based on the analysis results, the correlation equations are also proposed in this paper, namely the correlation of natural convection heat transfer for laminar regime on a vertical cylindrical with the heat flux constant in the form Nuq = a(Raq)b.

Volatile organic compounds and odor emissions from typical building materials and air sample storage time analysis

Building materials are the main sources of indoor volatile organic compounds (VOCs) and odors, which significantly impact indoor air quality and perceived air quality. In this study, gas chromatography-mass spectrometry was used to measure VOC emissions from 34 building materials, while an untrained panel assessed odor intensity (OI) and acceptability (ACC). The total VOC (TVOC) concentrations ranged from 156.5 to 3572.0 μg/m3, with alkanes identified as the predominant types of VOCs. Formaldehyde and n-hexane were found to be the most abundant compounds. Aldehydes are the dominant odorants, with hexanal being the most prevalent one according to the odor activity value (OAV). OI ranged from 2.35 to 4.69, while ACC ranged from −0.49 to 0.36. Adhesives exhibited both the highest TVOC concentration as well as a strong odor compared to other building materials; furniture boards emitted the lowest TVOC concentrations; wall coverings emitted minimal levels of odors. The effects of storage time on VOC concentrations and odor assessments were analyzed after sample collection for different durations: 6 h, 24 h or 48 h. A storage time period of 24 h was determined to be sufficient for assessing OI and ACC whereas shorter durations may be required when analyzing VOC concentrations or OAV values. The data provided in this study could help regulate VOC and odor emissions from building materials, as well as facilitate developing a model correlating subjective assessment results of OI and ACC with objective measurements of VOC concentrations and OAV.

Control Strategy for Building Air Conditioning Cluster Loads Participating in Demand Response Based on Cyber-Physical System

In recent years, with rising urbanization and ongoing adjustments in industrial structures, there has been a growing dependence on public buildings. The load of public buildings gradually becomes the main component of the peak load in summer, among which the load of air conditioning is particularly prominent. To clarify the key problems and solutions to these challenges, this study proposes a multi-objective optimization control strategy for building air conditioning cluster participation in demand response based on Cyber-Physical System (CPS) architecture. In a three-layer typical CPS architecture, the unit level of the CPS achieves dynamic information perception of air conditioning clusters through smart energy terminals. An air conditioning load model based on the multiple parameter types of air conditioning compressors is presented. Then, the system level of the CPS fuses multiple pieces of information through smart energy gateways, analyzing the potential for air conditioning clusters when they participate in demand response. The system of system level (SoS level) of the CPS deploys a multi-objective optimization control strategy which includes the uncertainty of the initial states of air conditioning clusters within the intelligent building energy management system. The optimal model takes into account the differences in the environmental conditions of each individual air conditioning unit within the cluster and sets different operating modes for each unit to achieve load reduction while maintaining temperatures within a comfortable range for the human body. The Multi-Objective Particle Swarm Optimization (MOPSO) algorithm based on Pareto frontiers is applied to solve this optimization control strategy and to optimize the operational parameters of the air conditioning clusters. A comparative analysis is conducted with single-objective optimization results obtained using the traditional Particle Swarm Optimization (PSO) algorithm. The case study results indicate that the proposed multi-objective optimization control strategy can effectively improve the thermal comfort of the human body towards the controlled temperatures of air conditioning clusters while meeting the accuracy of demand response. In the solution phase, the highest temperature within the air conditioning clusters is 24 °C and the lowest temperature is 23 °C. Adopting the proposed multi-objective optimization control strategy, the highest temperature is 26 °C and the lowest temperature is 23.5 °C within the clusters and the accuracy of demand response is up to 92%. Compared to the traditional PSO algorithm, the MOPSO algorithm has advantages in convergence and optimization precision for solving the proposed multi-objective optimization control strategy.

Impact of modules number of thermoelectric cooler coupled with PV panels and phase change material on building air conditioning

This paper integrates a photovoltaic panel, phase change material, and thermo-electric cooling system into the room wall for air conditioning to replace conventional air conditioning with a new environmentally friendly system. This technique uses a photovoltaic panel on the wall's exterior to power the thermo-electric system in the wall's interior surface. Two phase change materials layers are installed inside the wall to extract the heat dissipation from the PV panel and the thermo-electric cooling system. A complete transient mathematical model for a room is constructed and solved numerically. The cooling system is studied in five wall positions: east, west, south, north, and the room's roof. The effect of each cooling system position on the room temperature, cooling capacity, and its coefficient of performance is investigated. The highest PV output power in the roof system is 857 W, while this number is 819 and 510 W for the east and west walls, respectively. The results show that the best cooling system performance is achieved at a specific number of thermoelectric elements, which is different for each wall position. It is 300 for the east and roof systems and 500 for the west and south systems. At this optimum number, the minimum room temperature reaches 19.4, 26.7, 26.6, and 20.4 °C for the east, west, south, and roof system, respectively. While, the maximum cooling capacity is about 1734, 2157and 1662 W for the east, west and roof systems, respectively. The lowest coefficient of performance value is 2, 4.35, and 1.9 for the east, west, and roof systems, respectively, making the new solar-powered system competitive with the conventional one.

Addressing building related energy burden, air pollution, and carbon emissions of a low-income community in Southern California

This study examines the impact of low-income assistance and electrification programs on a disadvantaged community in Southern California. An urban building energy model is paired with an AC power flow and electric distribution system degradation model to evaluate how the cost of energy, carbon emissions, and pollutant emissions change after applying building weatherization, energy efficiency, and electrification measures to the community. Results show that traditional weatherization and energy efficiency measures (upgrading lighting and appliances, improving insulation to current building code standards) are the most cost-effective, reducing the cost of energy and carbon emissions by 10–20 % for the current community. Heat pump water heaters offer a 40 % average reduction in carbon emissions and almost 50 % decrease in criteria pollutant emissions, but at a cost increase of 17–22 %. Appliance electrification also reduces carbon emissions 5–10 % but increases cost by 7 % to 25 %. For reducing carbon, government programs that support building electrification are most cost-effective when they combine switching from natural gas to electricity with high efficiency system. Electrifying hot water and appliances effectively reduces emissions but must be paired with improved low-income assistance programs to prevent increased energy burden for low-income families. The urban building energy model and electrical distribution simulations used in this study can be replicated in other low-income communities.