Visual Preferences and Human Interactions with Shading and Electric Lighting Systems

Buildings in the United States are responsible for 40% of the primary energy use and 30% of carbon dioxide emissions. As awareness is being raised for the energy consumption and environmental impacts of buildings, it is not surprising that improving building performance has gained significant attention over the past years. Increasing the energy efficiency and reducing the emissions associated with buildings is possible through the use of high-performance building design and implementation of advanced building controls. Moreover, as part of the modern life style, people in developed countries spend most of their time inside the buildings. This fact necessitates consideration of two important requirements. First that energy saving achieved by efficiency methods in practice should not compromise occupants’ comfort. Second, energy impacts of building users and their indoor environment preferences should be taken into account at both design and operation phases. Therefore, understanding and modeling human-building interactions and their links to energy consumption and occupant satisfaction with the indoor environment is the main goal of this research. To this end and with a focus on the visual environment, systematic data collection from a large number of participants is undertaken and novel probabilistic modeling approaches are explored to provide new insights towards human-centered sustainable buildings. The specific research objectives of this thesis are: 1. Study human interactions with motorized roller shades and dimmable electric lights as well as human perception and satisfaction with the luminous environment in private offices with variable daylight and electric light conditions. 2. Develop a novel Bayesian approach to model the interrelated human interactions with window shades and electric lights. 3. Develop a Bayesian classification and inference modeling framework for occupants’ visual preferences in daylit perimeter offices. To this end, four identical private offices in a high performance building located in West Lafayette, IN were equipped with sensing network and online survey questionnaires to study almost 300 occupants during the two sets of field studies conducted for this thesis. The first field study extends the knowledge of human-building interactions to advanced building systems such as motorized roller shades and dimmable electric lights and reveals behavioral patterns enabled through side-by-side comparisons of different environmental controls and user interfaces ranging from fully automated to fully manual and from low to high levels of accessibility (wall switch, remote controller and graphical web interface). Results of the field study reveal: (a) occupational dynamics and human variables as two key features, in addition to environmental variables, in describing human-shading and -electric lighting interactions; (b) higher daylight utilization in offices with easy-to-access controls; (c) strong preference for customized indoor climate, along with a relationship between occupant perception of control and acceptability of a wider range of visual conditions. With the insights gained from the first field study, the research extends to exploit the resulted dataset as a basis for the development of a hierarchical Bayesian approach which is used, for the first time, to model human interactions with motorized roller shades and dimmable electric lights. Bayesian multivariate binary-choice logit models have been constructed to predict shade raising/lowering and electric light increasing actions while Bayesian regression models with built-in physical constraints to estimate the magnitude of shading and electric lighting actions. The proposed models, in their structure, account for (a) intermediate operating states of the systems; (b) interrelated operation of shades and lights; (c) personal characteristics and human attributes. Moreover, the developed human-building interaction modeling framework benefits from the advantages of the Bayesian formalism as it (a) provides a systematic approach to identify significant features in describing the human-building interactions; (b) incorporates prior beliefs about the systems; (c) captures the epistemic uncertainty, which is important when dealing with small-sized datasets, a ubiquitous issue in human data collection in actual buildings. The second field study was designed and conducted to collect data for occupants’ satisfaction with the visual environment when exposed to different combinations of daylight and electric light conditions, along with data from room sensors, shading and light dimming states. The resulted dataset is then used as a basis to model occupants’ visual preferences such as prefer darker, prefer brighter, or satisfied with current conditions. Bayesian multinomial logistic regression is augmented with Dirichlet process prior to encode within the model structure that occupants’ visual preferences are influenced by a combination of environmental and control state variables as well as individual visual characteristics. The latter is treated as a hidden random variable which is used to cluster occupants with similar visual preference characteristics and to determine the optimal number of clusters among the observed population. Modeling results based on observations from 75 occupants in glare-free conditions suggest work plane illuminance, window unshaded area, and electric light ratio as significant features of the general visual preference model and reveal the existence of three distinct clusters with physical interpretation; preference for bright, moderate, and dark conditions. In the final step, a method for learning the visual preferences of new occupants is deployed which uses a mixture of the general probabilistic sub-models to infer new occupants’ cluster values and personalized preference profiles. The proposed approach proves to be efficient as it is shown to predict personalized profiles with 81% prediction accuracy with very few observations (less than 16) from each new occupant. In summary, the systematic data collection methods and prototype interfaces used in this dissertation establish a consistent and reliable approach for studying human interactions with building systems and satisfaction with the indoor environment. Unique datasets for human attributes towards the visual environment in perimeter building zones have been generated especially for the occupants’ direct preference votes with different visual conditions which is currently lacked in the literature. The probabilistic models for human interactions with shading and lighting systems and occupants’ visual preferences incorporate individual characteristics and account for uncertainties associated with limited data, thus, are to increase prediction accuracy when implemented in Building Performance Simulation tools. The research presented herein facilitates an effective pathway towards implementation of adaptive personalized environments and is a necessary precursor for future investigation and expansion to human-centered building controls

Stochastic Model Predictive Control of Mixed-mode Buildings Based on Probabilistic Interactions of Occupants With Window Blinds

Between 4% to 20% of energy used for HVAC, lighting and refrigeration in a building is wasted due to issues associated with systems operations. It is estimated that proper building energy load control and operation can result in up to 40% utility cost savings. Current heuristic rules based on decision trees are difficult to define, manage and optimize as buildings become more complex. Advanced control strategies with weather forecast and cooling load anticipation, known as model predictive control (MPC), offer an attractive alternative for buildings with slow dynamics. However, MPC is mostly practiced through deterministic approaches. Deterministic MPC implicitly assumes that a dynamic model is able to perfectly predict the future behavior of the building over the desired control window, or prediction horizon. However, this assumption is clearly not rational because there will be both modeling errors and disturbances acting on the system over this period. One of these disturbances is associated with building occupant behaviors which interfere with deterministic assumptions. In this study, a probabilistic model of occupants’ behavior on window blind closing event is used to represent the disturbance coming from interactions of building residents with window blinds. This model is a multiple logistic regression analysis, based on a field study in an office building at the University of California, Berkeley (Inkarojrit, 2005). It considers the incident solar radiation on window surface and occupants’ self-reported brightness sensitivity as variable parameters to predict the closing event of blinds with 86.3% of accuracy. The probability of closing event is compared with a random number from the uniform distribution on the interval [0,1] at each time step and if it is greater than the random number, some indicator function will be equal to 1 (closing action) and vice versa. In order to implement the stochastic MPC, Monte Carlo simulation needs to be conducted due to the randomness of occupants’ behavior in closing the blinds. A test-building with mixed-mode cooling and high solar gains is considered as a test-bed. In our methodology, a detailed dynamic building model is developed and it is then used to identify the parameters of a 4th order linear time-variant state-space model. In the MPC formulation, the window opening schedule is optimized for the upcoming prediction horizon and the cost function is the minimization of energy usage subject to thermal comfort constraints during this horizon. Optimal control sequences based on the proposed stochastic MPC framework will be compared with deterministic MPC approaches to investigate possible advantages of considering uncertainties of occupant actions in model predictive controllers of buildings. References: Inkarojrit V., 2005. Balancing Comfort: Occupants’ Control of Window Blinds in Private Offices. PhD thesis, School of Architecture, University of California Berkeley

The role of transformational leadership style in enhancing lecturers’ job satisfaction

The challenges confronting Malaysia’s Research Universities in their futuristic movement towards world Class University are enormous. Leadership styles employed in higher education institutions play crucial role in achieving lecturers’ job satisfaction. This paper examines the influence of transformational leadership style employed by departments heads on improving lecturers’ job satisfaction. The population comprised the lecturers from three leading Research Universities. The responses were subjected to multiple regression analysis. The findings uncovered ‘inspirational motivation’ and ‘idealized influence’ as most often used practices of transformational leadership by the departments heads and identified that transformational leadership improves lecturers’ job satisfaction more than other leadership styles

Rhetorical pattern of the Indonesian EFL undergraduate students’ writings

The present research aimed to study the rhetorical patterns in students’ writings, whether they follow a deductive pattern or an inductive pattern, and whether the pattern is similar when writing in English and the Indonesian language. The sample for this study was 20 undergraduate students from the Faculty of Teacher Training and Education majoring in English Education in several universities in Indonesia. Participants were requested to write two essays and two email-format letters, one of each was written in English, the other in the Indonesian language. The results showed that all students preferred the deductive pattern for their two types of essays. However, for the letter writing, students preferred the inductive pattern more than the deductive one, with 12 students using the inductive pattern in their letters in English and 16 students using the inductive pattern in their letters in Indonesian. It is suggested that the Indonesian culture and the teaching instructions received in the classrooms may influence students’ choice of the patterns they use in different types of writings. The findings should give valuable information for the design of teaching writing courses in English Education majors in Indonesia

Impact of residue accessible surface area on the prediction of protein secondary structures

<p>Abstract</p> <p>Background</p> <p>The problem of accurate prediction of protein secondary structure continues to be one of the challenging problems in Bioinformatics. It has been previously suggested that amino acid relative solvent accessibility (RSA) might be an effective factor for increasing the accuracy of protein secondary structure prediction. Previous studies have either used a single constant threshold to classify residues into discrete classes (buries vs. exposed), or used the real-value predicted RSAs in their prediction method.</p> <p>Results</p> <p>We studied the effect of applying different RSA threshold types (namely, fixed thresholds vs. residue-dependent thresholds) on a variety of secondary structure prediction methods. With the consideration of DSSP-assigned RSA values we realized that improvement in the accuracy of prediction strictly depends on the selected threshold(s). Furthermore, we showed that choosing a single threshold for all amino acids is not the best possible parameter. We therefore used residue-dependent thresholds and most of residues showed improvement in prediction. Next, we tried to consider predicted RSA values, since in the real-world problem, protein sequence is the only available information. We first predicted the RSA classes by RVP-net program and then used these data in our method. Using this approach, improvement in prediction was also obtained.</p> <p>Conclusion</p> <p>The success of applying the RSA information on different secondary structure prediction methods suggest that prediction accuracy can be improved independent of prediction approaches. Thus, solvent accessibility can be considered as a rich source of information to help the improvement of these methods.</p

Determination of the design parameters for making urban wastewater plants in cold regions of Iran

زمینه و هدف: تعیین مشخصات کمی و کیفی فاضلاب خام ورودی به تصفیه خانه ها یکی از ارکان اصلی طراحی صحیح تصفیه خانه های فاضلاب به حساب می آید. این مطالعه با هدف تعیین پارامترهای طراحی تصفیه خانه های فاضلاب شهری برای مناطق سردسیر کشور انجام شد. روش بررسی: در این مطالعه توصیفی- تحلیلی سه تصفیه خانه فاضلاب شهرکرد، بروجن و فارسان واقع در استان چهارمحال و بختیاری به عنوان پایلوت در منطقه سردسیر انتخاب و مشخصات کمی و کیفی فاضلاب ورودی به آنها در یک دوره یکساله (سال 1386) بررسی گردید. علاوه بر اندازه گیری پیوسته دبی فاضلاب ورودی به تصفیه خانه، دما، pH ورودی، غلظت اکسیژن خواهی شیمیایی (COD)، غلظت اکسیژن خواهی بیوشیمیایی پنج روزه (BOD)، مواد معلق (TSS)، مواد معلق فرار (VSS)، نیتــــروژن کجلدال (TKN)، فسفر فسفاتی، در نمونه های مرکب 24 ساعته متناسب با دبی، اندازه گیری شد.داده ها به کمک آزمون های آماری t و ANOVA تجزیه و تحلیل شدند. یافته ها: میانگین سرانه پارامترهای مورد تحقیق در این مطالعه بر حسب گرم در روز به ازای هر نفر برای BOD5 معادل 41، COD معادل 60، TSS معادل 65، VSS معادل 47، TKN معادل 3/8 و برای فسفات معادل 93/0 و متوسط تولید فاضلاب، 177 لیتر به ازای هر نفر در روز و ضریب حداکثر و حداقل دبی فاضلاب به ترتیب 76/1 و 29/0 بدست آمد. مقایسه نتایج پارامترهای سرانه مورد نظر در سه تصفیه خانه اختلاف معنی داری بین پارامترهای BOD5، COD، نیتروژن کجدال و فسفر وجود نداشت ولی میزان TSS، VSS در تصفیه خانه های مورد مطالعه بیشتر بود (05/0

Length dependent reversible off–on activation of photo-switchable relay anion transporters

A homologous series of azobenzene-derived photo-switchable ion relay transporters is reported. We reveal that both the length and geometry of the relay strongly affect transport rate, allowing the relative activity of the E and Z isomers to be reversed and hence the wavelengths of light used for on and off switching to be exchanged