This study aimed to develop retrofitted natural ventilation options and control strategies for existing office buildings to improve thermal comfort, indoor air quality and energy consumption. For this purpose, a typical office building was selected in order to identify opportunities and constraints when implementing such strategies. Actual performance of the case study building was evaluated by conducting quantitative and qualitative field measurements including physical measurements and questionnaire surveys. Based on the actual building performance, a combination of Dynamic Thermal Simulation (using IES) and Computational Fluid Dynamics (using PHOENICS) models were built to develop appropriate natural ventilation options and control strategies to find a balance between energy consumption, indoor air quality, and thermal comfort. Several retrofitted options and control strategies were proposed and the best retrofitted natural ventilation options and control strategies were installed in the case study building. Post occupancy evaluation of the case study building after the interventions was also carried out by conducting physical measurements and questionnaire surveys. Post refurbishment measurements revealed that energy consumption and risk of overheating in the refurbished building were reduced by 9% and 80% respectively. The risk of unacceptable indoor air quality was also reduced by 60% in densely occupied zones of the building. The results of questionnaire surveys also revealed that the percentage of dissatisfied occupants reduced by 80% after intervention. Two new products including a Motorized ceiling tile and NVlogIQ , a natural ventilation wall controller, were also developed based on the results of this study.

Photocopy of typescript. / Digitized by Kansas Correctional Industries

Thesis (M.Ing. (Electrical Engineering))--North-West University, Potchefstroom Campus, 2007.

South Africa is currently facing a major electricity crisis due to the continuous growth in electricity demand. Eskom, the largest electricity supplier in South Africa, have enabled numerous methods to support energy reduction in both the residential and industrial sectors. Programs developed by Eskom to help the different major electricity consuming industries with the development of energy efficient and load shift strategies, have already been put into practice. These programs solely focus on the potential savings in megawatts each production sector might consist of. The key features of the Eskom electricity reduction initiative are driven by the energy efficiency concept and the peak demand load shift capability. Both the load shift and energy efficient initiatives are mostly active in the mining industry, because of the high electricity consumption levels of a standard mining operation. One of the most inefficient systems currently active within a mining operation is the ventilation control system. This dissertation describes the energy efficient and load shift research on the current underground ventilation system by means of certain design methodologies that might improve the inefficient operational features on both the standard underground auxiliary fans and the main surface fans. The operational features of a standard 2-pole 45 kW issued auxiliary fan were tested, by using a fan-testing column to compare the performance criteria to that of an improved auxiliary fan design. An energy saving potential on a single 45 kW unit of 11 kW was evident during the testing analysis. This amounted to an estimated annual energy saving potential of R 370,000.00 with a total saving of 561 kW on all the installed 45 kW units at Kopanang goldmine, by means of an investment in the replacement of the current installed units with that of the improved units. A secondary study was to gather information on the main surface fan operational features at Kopanang and Mponeng goldmines. The gathered information showed an estimated possibility for load shift and efficiency initiatives, which will result in fan operating life expansion and electricity savings capabilities. Annual electricity savings of up to R I ,500,000.00 were calculated on efficiency and load shift strategies and gave an indication on how costly inefficient operations are. The calculated I 0% increase in main fan efficiency resulted in an annual saving of nearly R 1,100,000.00 with a reduction of 1,05 MW at Mponeng goldmine and an annual saving of nearly R 721,000.00 with a reduction of 675 kW at Kopanang goldmine. The load shift potential at Mponeng and Kopanang goldmines were nearly 3,5 MW and 2,25 MW respectively. Capital investments from either Eskom or alternative investors will definitely play a crucial part in the realization of energy efficiency and load shift measures. It may include, improved fan installations, variable speed drives for the main fans and real time management systems. If the mine should decide to invest in these efficient strategies, the proposed Eskom DSM program might result in a net energy savings potential for any mining operation. / Thesis (M.Ing. (Electrical Engineering))--North-West University, Potchefstroom Campus, 2007.

South Africa is currently facing a major electricity crisis due to the continuous growth in electricity demand. Eskom, the largest electricity supplier in South Africa, have enabled numerous methods to support energy reduction in both the residential and industrial sectors. Programs developed by Eskom to help the different major electricity consuming industries with the development of energy efficient and load shift strategies, have already been put into practice. These programs solely focus on the potential savings in megawatts each production sector might consist of. The key features of the Eskom electricity reduction initiative are driven by the energy efficiency concept and the peak demand load shift capability. Both the load shift and energy efficient initiatives are mostly active in the mining industry, because of the high electricity consumption levels of a standard mining operation. One of the most inefficient systems currently active within a mining operation is the ventilation control system. This dissertation describes the energy efficient and load shift research on the current underground ventilation system by means of certain design methodologies that might improve the inefficient operational features on both the standard underground auxiliary fans and the main surface fans. The operational features of a standard 2-pole 45 kW issued auxiliary fan were tested, by using a fan-testing column to compare the performance criteria to that of an improved auxiliary fan design. An energy saving potential on a single 45 kW unit of 11 kW was evident during the testing analysis. This amounted to an estimated annual energy saving potential of R 370,000.00 with a total saving of 561 kW on all the installed 45 kW units at Kopanang goldmine, by means of an investment in the replacement of the current installed units with that of the improved units. A secondary study was to gather information on the main surface fan operational features at Kopanang and Mponeng goldmines. The gathered information showed an estimated possibility for load shift and efficiency initiatives, which will result in fan operating life expansion and electricity savings capabilities. Annual electricity savings of up to R I ,500,000.00 were calculated on efficiency and load shift strategies and gave an indication on how costly inefficient operations are. The calculated I 0% increase in main fan efficiency resulted in an annual saving of nearly R 1,100,000.00 with a reduction of 1,05 MW at Mponeng goldmine and an annual saving of nearly R 721,000.00 with a reduction of 675 kW at Kopanang goldmine. The load shift potential at Mponeng and Kopanang goldmines were nearly 3,5 MW and 2,25 MW respectively. Capital investments from either Eskom or alternative investors will definitely play a crucial part in the realization of energy efficiency and load shift measures. It may include, improved fan installations, variable speed drives for the main fans and real time management systems. If the mine should decide to invest in these efficient strategies, the proposed Eskom DSM program might result in a net energy savings potential for any mining operation. / Thesis (M.Ing. (Electrical Engineering))--North-West University, Potchefstroom Campus, 2007.

The perception of thermal comfort is an important factor influencing the acceptability of residential heating strategies. The perceived thermal comfort may affect a person's inclination to try a strategy or to use it on a long-term basis. In the study, perceived thermal comfort was assessed in relation to room temperature, humidity, clothing worn, preferred room temperatures, personal control over the temperatures, and energy consumption. The relationships among these variables were examined for five families participating in a live-in study comparing five residential heating strategies. The strategies tested included closing off bedroom vents/doors, setting the thermostat at 65°F, and the use of a solar greenhouse and a woodstove as supplemental heat sources. The families lived in a retrofitted solar test house for a period of four to six weeks. The house was equipped with a computer which monitored 37 channels of information at ten-second intervals and recorded the data hourly. The data collected included temperatures in every room, inside and outside humidity, wind velocity, and other variables that interplay in comfort levels and energy use. The ten adult respondents completed daily and weekly questionnaires containing Likert-type scales of thermal comfort and checklists of clothing worn. The results suggest the following conclusions: 1) the use of a residential setting to measure thermal comfort under varying environmental conditions can be successfully accomplished, 2) psychological variables such as personal control should be considered and tested by persons involved in standards development for the thermal environment, 3) the ability and experience of the persons to use a strategy can affect the achieved energy saving benefits of the strategy, 4) personal preference in the amount of personal effort a person is willing or able to give will impact on the decision on whether to use certain strategies, 5) heating strategies that can produce a direct source of heat or at least some warmer areas were rated higher by the project participants, and 6) weather can play an important role in the effectiveness of the solar greenhouse as a heating source. / Ph. D. / incomplete_metadata

Une utilisation efficace et coordonnée des systèmes installés dans le bâtiment doit permettre d’améliorer le confort des occupants tout en consommant moins d’énergie. Ces objectifs à optimiser sont pourtant antagonistes. Le problème résultant peut être alors vu comme un problème d’optimisation multicritères. Par ailleurs, pour répondre aux enjeux industriels, il devra être résolu non seulement dans une optique d’implémentation simple et peu coûteuse, avec notamment un nombre réduit de capteurs, mais aussi dans un souci de portabilité pour que le contrôleur résultant puisse être implanté dans des bâtiments d’orientation différente et situés dans des lieux géographiques variés.L’approche choisie est de type commande prédictive (MPC, Model Predictive Control) dont l’efficacité pour le contrôle du bâtiment a déjà été illustrée dans de nombreux travaux, elle requiert cependant des efforts de calcul trop important. Cette thèse propose une méthodologie pour la synthèse des contrôleurs, qui doivent apporter une performance satisfaisante en imitant les comportements du MPC, tout en répondant à des contraintes industriels. Elle est divisée deux grandes étapes :1. La première étape consiste à développer un contrôleur MPC. De nombreux défis doivent être relevés tels que la modélisation, le réglage des paramètres et la résolution du problème d’optimisation.2. La deuxième étape applique différents algorithmes d’apprentissage automatique (l’arbre de décision, AdaBoost et SVM) sur une base de données obtenue à partir de simulations utilisant le contrôleur prédictif développé. Les grands points levés sont la construction de la base de données, le choix de l’algorithme de l’apprentissage et le développement du contrôleur logique.La méthodologie est appliquée dans un premier temps à un cas simple pour piloter un volet,puis validée dans un cas plus complexe : le contrôle coordonné du volet, de l’ouvrant et dusystème de ventilation. / An efficient and coordinated control of systems in buildings should improve occupant comfort while consuming less energy. However, these objectives are antagonistic. It can then be formulated as a multi-criteria optimization problem. Moreover, it should be solved not only in a simple and cheap implementation perspective, but also for the sake of adaptability of the controller which can be installed in buildings with different orientations and different geographic locations.The MPC (Model Predictive Control) approach is shown well suited for building control in the state of the art but it requires a big computing effort. This thesis presents a methodology to develop logical controllers for equipments in buildings. It helps to get a satisfactory performance by mimicking the MPC behaviors while dealing with industrial constraints. Two keys steps are required :1. In the first step, an optimal controller is developed with hybrid MPC technique. There are challenges in modeling, parameters tuning and solving the optimization problem.2. In the second step, different Machine Learning algorithms (Decision tree, AdaBoost, SVM) are tested on database which is obtained with the simulation with the MPC controller. The main points are the construction of the database, the choice of learning algorithm and the development of logic controller.First, our methodology is tested on a simple case study to control a blind. Then, it is validatedwith a more complex case : development of a coordinated controller for a blind, natural ventilationand mechanical ventilation.

University of Macau / Faculty of Science and Technology / Department of Electrical and Electronics Engineering

Une utilisation efficace et coordonnée des systèmes installés dans le bâtiment doit permettre d’améliorer le confort des occupants tout en consommant moins d’énergie. Ces objectifs à optimiser sont pourtant antagonistes. Le problème résultant peut être alors vu comme un problème d’optimisation multicritères. Par ailleurs, pour répondre aux enjeux industriels, il devra être résolu non seulement dans une optique d’implémentation simple et peu coûteuse, avec notamment un nombre réduit de capteurs, mais aussi dans un souci de portabilité pour que le contrôleur résultant puisse être implanté dans des bâtiments d’orientation différente et situés dans des lieux géographiques variés.L’approche choisie est de type commande prédictive (MPC, Model Predictive Control) dont l’efficacité pour le contrôle du bâtiment a déjà été illustrée dans de nombreux travaux, elle requiert cependant des efforts de calcul trop important. Cette thèse propose une méthodologie pour la synthèse des contrôleurs, qui doivent apporter une performance satisfaisante en imitant les comportements du MPC, tout en répondant à des contraintes industriels. Elle est divisée deux grandes étapes :1. La première étape consiste à développer un contrôleur MPC. De nombreux défis doivent être relevés tels que la modélisation, le réglage des paramètres et la résolution du problème d’optimisation.2. La deuxième étape applique différents algorithmes d’apprentissage automatique (l’arbre de décision, AdaBoost et SVM) sur une base de données obtenue à partir de simulations utilisant le contrôleur prédictif développé. Les grands points levés sont la construction de la base de données, le choix de l’algorithme de l’apprentissage et le développement du contrôleur logique.La méthodologie est appliquée dans un premier temps à un cas simple pour piloter un volet,puis validée dans un cas plus complexe : le contrôle coordonné du volet, de l’ouvrant et dusystème de ventilation. / An efficient and coordinated control of systems in buildings should improve occupant comfort while consuming less energy. However, these objectives are antagonistic. It can then be formulated as a multi-criteria optimization problem. Moreover, it should be solved not only in a simple and cheap implementation perspective, but also for the sake of adaptability of the controller which can be installed in buildings with different orientations and different geographic locations.The MPC (Model Predictive Control) approach is shown well suited for building control in the state of the art but it requires a big computing effort. This thesis presents a methodology to develop logical controllers for equipments in buildings. It helps to get a satisfactory performance by mimicking the MPC behaviors while dealing with industrial constraints. Two keys steps are required :1. In the first step, an optimal controller is developed with hybrid MPC technique. There are challenges in modeling, parameters tuning and solving the optimization problem.2. In the second step, different Machine Learning algorithms (Decision tree, AdaBoost, SVM) are tested on database which is obtained with the simulation with the MPC controller. The main points are the construction of the database, the choice of learning algorithm and the development of logic controller.First, our methodology is tested on a simple case study to control a blind. Then, it is validatedwith a more complex case : development of a coordinated controller for a blind, natural ventilationand mechanical ventilation.

Past research has shown that natural ventilation can be used to satisfy upwards of 98% of the yearly cooling demand when utilized in the appropriate climate zone. Yet widespread implementation of natural ventilation has been limited in practice. This delay in market adoption is mainly due to lack of effective and reliable control. Historically, control of natural ventilation was left to the occupant (i.e. they are responsible for opening and closing their windows) because occupants are more readily satisfied when given control of the indoor environment. This strategy has been shown to be effective during summer months, but can lead to both over and under ventilation, as well as the associated unnecessary energy waste during the winter months. This research presents the development and evaluation of a model-based control algorithm for natural ventilation. The proposed controller is designed to modulate the operable windows based on ambient temperature, wind speed, wind direction, solar radiation, indoor temperature and other building characteristics to ensure adequate ventilation and thermal comfort throughout the year without the use of mechanical ventilation and cooling systems. A midrise student dormitory building, located in Portland OR, has been used to demonstrate the performance of the proposed controller. Simulation results show that the model-based controller is able to reduce under-ventilated hours to 6.2% of the summer season (June - September) and 2.5% of the winter (October - May) while preventing over-heating during 99% of the year. In addition, the model-based-controller reduces the yearly energy cost by 33% when compared to a conventional heat pump system. As a proactive control, model-based control has been used in a wide range of building control applications. This research serves as proof-of-concept that it can be used to control operable windows to provide adequate ventilation year-round without significantly affecting thermal comfort. The resulting control algorithm significantly improves the reliability of natural ventilation design and could lead to a wider adoption of natural ventilation in appropriate climate zones.