Evaluation of distributed environmental control systems for improving IAQ and reducing energy consumption in office buildings

Conventional heating, ventilation, and air conditioning (HVAC) systems are incapable of providing control over individual environments or adjusting fresh air supply based on the dynamic occupancy of individual rooms in an office building. This paper introduces the concept of distributed environmental control systems (DECS) and shows that improvement in indoor air quality (IAQ) and energy efficiency can be achieved by providing required amounts of fresh air directly to the individual office spaces through distributed demand controlled ventilation (DDCV). In DDCV, fresh air is provided to each micro-environment (room or cubicle) based on input from distributed sensors (CO₂, VOC, occupancy, etc.) or intelligent scheduling techniques to provide acceptable IAQ for each occupant, rather than for groups or populations of occupants. In order to study DECS, a numerical model was developed that incorporates some of the best available models for studying building energy consumption, indoor air flow, contaminant transport and HVAC system performance. The developed model was applied to a DECS in a model office building equipped with a DDCV system. By implementing DECS/DDCV and intelligent scheduling techniques it is possible to achieve an improvement in IAQ along with a reduction in annual energy consumption compared to conventional ventilation systems.     

Building occupancy detection through sensor belief networks

Currently it is difficult to determine when and where people occupy a commercial building. Part of the difficulty arises from shortcomings in available sensor technology, but an even greater deficiency is the lack of analysis methods appropriate to the determination of occupancy. This paper describes a pilot study describing new sensing and data analysis techniques, applied to the determination of space occupancy. The central premise of the paper is that improved building operation with respect to energy management, security, and indoor environmental quality will be possible with better detection of building occupancy resolved in space and time. We developed and deployed a network of passive infrared occupancy sensors in two private offices, and applied analysis tools based on Bayesian probability theory to determine occupancy. Specifically, a class of graphical probability models, called belief networks, was applied to the occupancy data generated by the sensor network. The inference of primary importance is a probability distribution over the number of occupants and their locations in a building, given past and present sensor measurements. Inferences were computed for occupancy and its temporal persistence in individual offices as well as the persistence of sensor status. The raw sensor data were also used to calibrate the sensor belief network, including the occupancy transition matrix used in the Markov model, sensor sensitivity, and sensor failure models. This study shows that the belief network framework can be applied to the analysis of data streams from sensor networks, offering significant benefits to building operation compared to current practice.     

Agent-based system identification for control-oriented building models

The paper presents a general agent-based system identification framework as potential solution for data-driven models of building systems that can be developed and integrated with improved efficiency, flexibility and scalability, compared to centralized approaches. The proposed method introduces building sub-system agents, which are optimized independently, by solving locally a maximum likelihood estimation problem. Several models are considered for the sub-system agents and a systematic selection approach is established considering the root mean square error, the parameter sensitivity to output trajectory and the parameter correlation. The final model is integrated from selected models for each agent. Two different approaches are developed for the integration; the negotiated-shared parameter model, which is a distributed method, and the free-shared parameter model based on a decentralized method. The results from a case-study for a high performance building indicate that the model prediction accuracy of the new approach is fairly good for implementation in predictive control.     

The impact of window opening and other occupant behavior on simulated energy performance in residence halls

A poor depiction of occupant behavior in building performance simulation frequently results in substantial divergences between real and simulated results. The problem may be of particular concern with simulation supporting the renovation of older multi-unit residential buildings, buildings whose occupants use windows for temperature control even during heating season. Here, we investigated the impact of window operation models (as well as other occupant behaviors) on simulated energy performance in university residence halls. Based on environmental monitoring, along with information collected from occupant surveys and wearable devices, we estimated air exchange rates and developed a probabilistic window-operation prediction model. The data were collected in 76 dormitory rooms sampled from a pre-renovated historic building and two similar buildings. We then evaluated the window-operation model’s predictive performance in 15 dormitory rooms in the post-renovated building with new occupants. The results of our predictive model were also compared with previously reported window-operation models. We implemented each window-operation model in a calibrated EnergyPlus building performance model, comparing the results of each simulation to metered hourly steam consumption. The impact of the different window operation models on simulated heating energy use was significant (annual error ranging from 0.2% to 10%). Our model demonstrated the highest capability of predicting window state (accuracy=85.8%) and steam use (NMBE=?0.2%); however, some previously published windowoperation models also produced satisfactory performance, implying that such models may be generalizable to some extent. The results suggest that data collected from somewhat ubiquitous indoor environmental quality sensors can glean insights into occupant behavior for building performance simulation. Furthermore, the energy impacts resulting from the variations in occupant behavior studied here were large enough to show that the choice of behavior model can have meaningful implications for real-world applications, such as estimating saving from heating and lighting system upgrades.     

Parametric study on the performance of green residential buildings in China

The parametric study of the indoor environment of green buildings focuses on the quantitative and qualitative improvement of residential building construction in China and the achievement of indoor thermal comfort at a low level of energy use. This study examines the effect of the adaptive thermal comfort of indoor environment control in hot summer and cold winter (HSCW) zones. This work is based on a field study of the regional thermal assessment of two typical cases, the results of which are compared with simulated results of various scenarios of “energy efficiency” strategy and “healthy housing” environmental control. First, the simulated results show that the adaptive thermal comfort of indoor environment control is actually balanced in terms of occupancy, comfort, and energy efficiency. Second, adaptive thermal comfort control can save more energy for heating or cooling than other current healthy housing environmental controls in China's HSCW zone. Moreover, a large proportion of energy use is based on the subjective thermal comfort demand of occupants in any building type. Third, the building shape coefficient cannot dominate energy savings. The ratio of the superficial area of a building to the actual indoor floor area has a significant positive correlation with and affects the efficiency of building thermal performance.     

Understanding occupancy pattern and improving building energy efficiency through Wi-Fi based indoor positioning

Detailed visualisation and data analysis of occupancy patterns including spatial distribution and temporal variations are of great importance to delivering energy efficient and productive buildings. An experimental study comprising 24-h monitoring over 30 full days was conducted in a university library building.Occupancy profiles have been monitored and analysis has been carried out. Central to this monitoring study is the Wi-Fi based indoor positioning system based on the measured Wi-Fi devices' number and locations and data mining methods. Distinct from traditional occupancy and energy studies,more detailed informationrelated to the indoor positions and number of occupants has offered a better understanding of building user behaviour. The implication of the occupancy patterns for energy( e. g. lighting and other building services) efficiency is assessed,assisted with data from lighting sensors where needed. It is found occupancy patterns change dramatically with time. Also,the energy waste patterns have been identified through the method of data association rule mining. If the identified energy waste is removed,the total energy consumption can be reduced by 26. 1%. The indoor positioning information also has implications for optimizing space use,opening hours as well as staff deployment. The work could be extended to more rooms with diverse functions,other seasons and other types of non-domestic buildings for a more comprehensive understanding of building user behaviour and energy efficiency.     

Modeling space preferences for accurate occupancy prediction during the design phase

The accurate prediction of occupancy during the design phase of a building helps architects to improve space efficiency by eliminating the possible under-utilization and over-crowding of space during the design use phase. However, existing models exhibit limited accuracy in occupancy prediction. A major reason for this limitation is that spatial-choice behavior is ignored or oversimplified. We therefore developed a space-preference model to explain spatial-choice behavior, with a particular focus on individual work-related activities. For this purpose, we conducted a discrete-choice experiment: 2048 observations of spatial choices were collected, and a conditional logit model was used to model space preferences. The application of the space-preference model was illustrated by two case examples, with which the merits of the model were highlighted. It was then validated in a predictive success test and a case study. The model will help architects to assess potential over-crowding and under-utilization of space according to different design options.     

An intelligent approach to assessing the effect of building occupancy on building cooling load prediction

Building cooling load prediction is one of the key factors in the success of energy-saving measures. Many computational models available in the industry have been developed from either forward or inverse modeling approaches. However, these models usually require extensive computer resources and lengthy computation. This paper discusses the use of the multi-layer perceptron (MLP) model, one of the artificial neural network (ANN) models widely adopted in engineering applications, to estimate the cooling load of a building. The training samples used include weather data obtained from the Hong Kong Observatory and building-related data acquired from an existing prestigious commercial building in Hong Kong that houses a mega complex and operates 24 h a day. The paper also discusses the practical difficulties encountered in acquiring building-related data. In contrast to other studies that use ANN models to predict building cooling load, this paper includes the building occupancy rate as one of the input parameters used to determine building cooling load. The results demonstrate that the building occupancy rate plays a critical role in building cooling load prediction and significantly improves predictive accuracy.     

Bayesian estimation of occupancy distribution in a multi-room office building based on CO<Subscript>2</Subscript> concentrations

Occupancy information in an office building is an important asset for determining energy-efficient operations and emergency evacuation of a building. In this study, we developed a method to estimate the occupancy distribution in a multi-room office building using Bayesian inference. The Markov chain Monte Carlo algorithm was used to estimate the real-time occupancy in individual rooms based on indoor carbon dioxide concentrations. The office building was made-up of five rooms with different physical configurations and dimensions, and the rooms were air-conditioned and ventilated by a central air handling unit. The carbon dioxide concentration data were generated by the simulation software CONTAMW according to a given schedule of occupancy and the supply airflow rates in each room. The objective of the present paper is to investigate the effects of various parameters of Bayesian inference on the accuracy of estimation results. The parameters include the probability of prior information, the uncertainty level of CO₂ data, and the time interval for monitoring CO₂.     

The 3-year follow-up study in a block of flats - experiences in the use of the Finnish indoor climate classification

Indoor climate of two new blocks of flats was investigated. The case building was built for people with respiratory diseases by following the instructions of the Finnish Classification of Indoor Climate, Construction and Finishing Materials, while the control building was built using conventional building technology. The main indoor air parameters (temperature, relative humidity and levels of CO, CO2, ammonia, total volatile organic compounds, total suspended particles, fungal spores, bacteria and cat, dog and house dust mite allergens) were measured in six apartments of both the buildings on five occasions during the 3-year occupancy. In addition, a questionnaire to evaluate symptoms of the occupants and their satisfaction with their home environment was conducted in connection with indoor air quality (IAQ) measurements. The levels of indoor air pollutants in the case building were, in general, lower than those in the control building. In addition, the asthmatic occupants informed that their symptoms had decreased during the occupancy in the case building. This case study showed that high IAQ is possible to reach by careful design, proper materials and equipment and on high-quality construction with reasonable additional costs. In addition, the study indicated that good IAQ can also be maintained during the occupancy, if sufficient information on factors affecting IAQ and guidance on proper use and care of equipment are available for occupants.     

Towards a model of user behaviour regarding the manual control of windows in office buildings

This paper presents the results of a field study of manual control of windows which has been carried out in 21 individual offices within the Fraunhofer Institute's building in Freiburg, Germany, from July 2002 to July 2003. Window status, occupancy, indoor and outdoor climatic conditions were measured every minute. Previous research findings are validated and extended by the results of this field study. The analysis of user behaviour reveals a strong correlation between the percentage of open windows and the time of year, outdoor temperature and building occupancy patterns. Most window opening is connected with the arrival of a person. Based on the results, a preliminary user model is proposed to simulate and predict window status in office buildings with varying outdoor temperature and occupancy.     

Smart building management vs. intuitive human control—Lessons learnt from an office building in Hungary

Smart building management and control are adopted nowadays to achieve zero-net energy use in buildings. However, without considering the human dimension, technologies alone do not necessarily guarantee high performance in buildings. An office building was designed and built according to state-of-the-art design and energy management principles in 2008. Despite the expectations of high performance, the owner was facing high utility bills and low user comfort in the building located in Budapest, Hungary. The objective of the project was to evaluate the energy performance and comfort indices of the building, to identify the causes of malfunction and to elaborate a comprehensive energy concept. Firstly, current building conditions and operation parameters were evaluated. Our investigation found that the state-of-the-art building management system was in good conditions but it was operated by building operators and occupants who are not aware of the building management practice. The energy consumption patterns of the building were simulated with energy modelling software. The baseline model was calibrated to annual measured energy consumption, using actual occupant behaviour and presence, based on results of self-reported surveys, occupancy sensors and fan-coil usage data. Realistic occupant behaviour models can capture diversity of occupant behaviour and better represent the real energy use of the building. This way our findings and the effect of our proposed improvements could be more reliable. As part of our final comprehensive energy concept, we proposed intervention measures that would increase indoor thermal comfort and decrease energy consumption of the building. A parametric study was carried out to evaluate and quantify energy, comfort and return on investment of each measure. It was found that in the best case the building could save 23% of annual energy use. Future work includes the follow-up of: occupant reactions to intervention measures, the realized energy savings, the measurement of occupant satisfaction and behavioural changes.     

围护结构霉菌种类识别及生长风险研究

围护结构霉菌生长不仅会影响围护结构的热工性能,还会危害室内空气质量和人员健康.以上海市某居民小区为研究对象,通过现场采集和DNA高通量测序,对围护结构中的霉菌种类进行了识别.基于菌种识别结果,选择文献中相应菌属的培养数据,建立了用于霉菌生长风险预测的等值线模型.基于最佳温度下霉菌生长速率和相对湿度的函数关系式对该模型进...     

Effect of thermostat and window opening occupant behavior models on energy use in homes

Existing dynamic energy simulation tools exceed the static dimension of the simplified methods through a better and more accurate prediction of energy use; however, their ability to predict real energy consumption is undermined by a weak representation of human interactions with the control of the indoor environment. The traditional approach to building dynamic simulation considers energy consumption as fully deterministic, taking into account standardized input parameters and using fixed and unrealistic schedules (lighting level, occupancy, ventilation rate, thermostat set-point). In contrast, in everyday practice occupants interact with the building plant system and building envelope in order to achieve desired indoor environmental conditions. In this study, occupant behavior in residential building was modelled accordingly to a probabilistic approach. A new methodology was developed to combine probabilistic user profiles for both window opening and thermostat set-point adjustments into one building energy model implemented in the dynamic simulation tool IDA Ice. The aim of the study was to compare mean values of the probabilistic distribution of the obtained results with a singular heating energy consumption value obtained by means of standard deterministic simulations. Major findings of this research demonstrated the weakness of standardized occupant behavior profile in energy simulation tools and the strengths of energy models based on measurements in fields and probabilistic modelling providing scenarios of occupant behavior in buildings.     

Sensor-based demand-controlled ventilation: a review

With sensor-based demand-controlled ventilation (SBDCV), the rate of ventilation is modulated over time based on the signals from indoor air pollutant or occupancy sensors. SBDCV offers two potential advantages: better control of indoor pollutant concentrations, and lower energy use and peak energy demand. Based on theoretical considerations and on a review of literature, SBDCV has the highest potential to be cost-effective in applications with the following characteristics: (a) a single or small number of pollutants dominate so that ventilation sufficient to control the concentration of the dominant pollutants provides effective control of all other pollutants; (b) large buildings or rooms with unpredictable temporally variable occupancy or pollutant emission; and (c) climates with high heating or cooling loads or locations with expensive energy. At present, most SBDCV systems are based on monitoring and control of carbon dioxide (CO₂) concentrations. There is a limited number of well-documented case studies that quantify the energy savings and the cost-effectiveness of SBDCV. The case studies reviewed suggest that in appropriate applications, SBDCV produces significant energy savings with a payback period typically of a few years.     

Impact of combined moisture buffering capacity of a hemp concrete building envelope and interior objects on the hygrothermal performance in a room

In this article, a hygrothermal building model, taking into account the building envelope, indoor heat and moisture sources, indoor environment and moisture buffering capacity of interior objects, is presented and validated with the test cases found in the literature. The model is used to study the impact of hemp concrete and the moisture buffer capacity of the interior elements on the prediction of the hygrothermal comfort in the building. The numerical results show that the use of hemp concrete in buildings can ensure good hygrothermal comfort. Besides, taking into account the effect of moisture buffering of indoor objects increases the building performance. Our results also suggest that neglecting moisture transfer through the envelope increases significantly the predicted percentage of dissatisfied indices and reduces the acceptability of indoor air quality during the occupied period. This study also confirmed that the combined relative humidity-sensitive ventilation system and moisture buffering capacity of building envelope and of interior objects is a very efficient way to reduce the heating energy consumption.     

Calculation of optimal thermal load of intermittently heated buildings

A building is permanently in thermodynamic non-equilibrium due to changing weather, free gains and indoor temperature set-point. Load calculation in dynamic conditions is an essential goal of building energy simulation. This paper demonstrates that the load calculation is a control problem. Supposing that the thermal model of the building is linear and that the model of the building, the weather conditions and occupational program are known in the design stage, the paper proposes an unconstrained optimal control algorithm which uses feed-forward to compensate the weather conditions and model predictive programming (MPP) for set-point tracking. MPP is obtained by modifying the dynamic matrix control (DMC), a variant of model predictive control (MPC).The peak load depends on the set-back time of the indoor temperature: smaller the set-back time, larger the peak load, but smaller energy consumption. Then, the choice of the weighting coefficients in the model predictive programming may be done on economical considerations.     

Analysis of local optima in predictive control for energy efficient buildings

We study the problem of heating, ventilation and air conditioning (HVAC) control in a typical commercial building. We propose a model predictive control (MPC) approach which minimizes energy cost while satisfying occupant comfort and control actuator constraints, using a simplified system model and incorporating predictions of future weather and occupancy inputs. In simplified physics-based models of HVAC systems, the product between air temperatures and flow rates arising from energy balance equations leads to a non-convex MPC problem. Fast computational techniques for solving non-convex optimization can only provide certificates of local optimality. Local optima can potentially cause MPC to have worse performance than existing control implementations, so deserve careful consideration. The objective of this article is to investigate the phenomenon of local optima in the MPC optimization problem for a simple HVAC system model. In the first part of the article, simplified physics-based models and MPC design for two common HVAC configurations are introduced. In the second part, simulation results exhibiting local optima for both configurations are presented. We perform a detailed analysis on the different types of local optima and their physical interpretation. We then use this analysis to derive physics-based rules to exclude classes of locally optimal control sequences under specific conditions.     

A study on the thermal performance of the earthen tower in summer

This paper focuses on the thermal properties of earthen towers, unique traditional dwelling buildings at Yongding, in Fujian, China. The main climate factors affecting indoor thermal environment in the area are discussed. The architectural details of the earthen tower functioning as thermal defence in summer are analysed. A computer simulation programme, developed especially for the condition of building and climate in Fujian, is used to study the thermal interaction between building structure, occupancy and climate. The results from various rooms in two typical forms of the building show that the thermal performance of the earthen tower is excellent.     

Average permeability measurements for an individual dwelling in Romania

The air permeability represents that feature of the building playing a major role in both the building energy performance and the indoor environment quality, therefore its prediction is very important. The statistical prediction models which are used today on a very large scale present large errors. The experimental measurements correct this deficit, but they are impossible to be carried out for large apartment building due to technical concerns. In this study we propose an intermediate approach “the prediction of average permeability as a weighted mean of the different measured permeabilities characteristic to the different types of joinery”. The article presents the mathematical models and the adapted experimental protocol for four different parameters that describes the permeability. The experimental work was carried out for an apartment placed at the ground level of a two storey house in Romania. The proposed approach presents smaller errors: 5% for the overall leakage airflow and 15% for the average permeability. The study presents interesting data being among the first permeability measurements in Romania. The originality of the study is also given by the proposed model which is oriented towards large dimensions blocks of apartments.