Being located in subtropical climates, the cooling energy accounts for a huge percentage of the total power consumption, and has become the major cause for power shortages. Therefore, building energy conservation strategies has become the major remedy to tackle this problem. In this study, the building energy performance certificate evaluation system has been established, in referencing the European communities systems, while integrating the financial and consumers factors to establish the building labeling system in Taiwan.

abstract: An acute and crucial societal problem is the energy consumed in existing commercial buildings. There are 1.5 million commercial buildings in the U.S. with only about 3% being built each year. Hence, existing buildings need to be properly operated and maintained for several decades. Application of integrated centralized control systems in buildings could lead to more than 50% energy savings. This research work demonstrates an innovative adaptive integrated lighting control approach which could achieve significant energy savings and increase indoor comfort in high performance office buildings. In the first phase of the study, a predictive algorithm was developed and validated through experiments in an actual test room. The objective was to regulate daylight on a specified work plane by controlling the blind slat angles. Furthermore, a sensor-based integrated adaptive lighting controller was designed in Simulink which included an innovative sensor optimization approach based on genetic algorithm to minimize the number of sensors and efficiently place them in the office. The controller was designed based on simple integral controllers. The objective of developed control algorithm was to improve the illuminance situation in the office through controlling the daylight and electrical lighting. To evaluate the performance of the system, the controller was applied on experimental office model in Lee et al.’s research study in 1998. The result of the developed control approach indicate a significantly improvement in lighting situation and 1-23% and 50-78% monthly electrical energy savings in the office model, compared to two static strategies when the blinds were left open and closed during the whole year respectively. / Dissertation/Thesis / Doctoral Dissertation Architecture 2015

<p>In perimeter building zones with glass façades, controllable fenestration (daylighting/shading) and electric lighting systems are used as comfort delivery systems under dynamic weather conditions, and their operation affects daylight provision, outside view, lighting energy use, as well as overall occupant satisfaction with the visual environment. A well-designed daylighting and lighting control should be able to achieve high level of satisfaction while minimizing lighting energy consumption. Existing daylighting control studies focus on minimizing energy use with general visual comfort constraints, when adaptive and personalized controls are needed in high performance office buildings. Therefore, reliable and efficient models and methods for learning occupants’ personalized visual preference or satisfaction are required, and the development of optimal daylighting controls requires integrated considerations of visual preference/satisfaction and energy use. </p> <p>In this Dissertation, a novel method is presented first for developing personalized visual satisfaction profiles in daylit offices using Bayesian inference. Unlike previous studies based on action data, a set of experiments with human subjects was designed and conducted to collect comparative visual preference data (by changing visual conditions) in private offices. A probit model structure was adopted to connect the comparative preference with a latent satisfaction utility model, assumed in the form of a parametrized Gaussian bell function. The distinct visual satisfaction models were then inferred using Bayesian approach with preference data. The posterior estimations of model parameters, and inferred satisfaction utility functions were investigated and compared, with results reflecting the different overall visual preference characteristics discovered for each person.</p> <p>Second, we present an online visual preference elicitation learning framework for efficiently learning and eliciting occupants’ visual preference profiles and hidden satisfaction utilities. Another set of experiments with human subjects was conducted to implement the proposed learning algorithm in order to validate the feasibility of the method. A combination of Thompson sampling and pure exploration (uncertainty learning) methods was used to balance exploration and exploitation when targeting the near-maximum area of utility during the learning process. Distinctive visual preference profiles of 13 subjects were learned under different weather conditions, demonstrating the feasibility of the learning framework. Entropy of the distribution of the most preferred visual condition is computed for each learned preference profile to quantify the certainty. Learning speed varies with subjects, but using a single variable model (vertical illuminance on the eye), most subjects could be learned to an acceptable certainty level within one day of stable weather, which shows the efficiency of the method (learning outcomes). </p> <p>Finally, a personalized shading control framework is developed to maximize occupant satisfaction while minimizing lighting energy use in daylit offices with roller shades. An integrated lighting-daylighting simulation model is used to predict lighting energy use while it also provides inputs for computing personalized visual preference profiles, previously developed using Bayesian inference from comparative preference data. The satisfaction utility and the predicted lighting energy use are then used to form an optimization framework. We demonstrate the results of: (i) a single objective formulation, where the satisfaction utility is simply used as a constraint to when minimizing lighting energy use and (ii) a multi-objective optimization scheme, where the satisfaction utility and predicted lighting energy use are formulated as parallel objectives. Unlike previous studies, we present a novel way to apply the MOO without assigning arbitrary weights to objectives: allowing occupants to be the final decision makers in real-time balancing between their personalized visual satisfaction and energy use considerations, within dynamic hidden optimal bounds – through a simple interface. </p> <p>In summary, we present the first method to incorporate personalized visual preferences in optimal daylighting control, with energy use considerations, without using generic occupant behavior models or discomfort-based assumptions.</p>