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- All Subjects: Electric lighting--Control.
- Creators: Reddy, T. Agami
Description
Lighting systems and air-conditioning systems are two of the largest energy consuming end-uses in buildings. Lighting control in smart buildings and homes can be automated by having computer controlled lights and window blinds along with illumination sensors that are distributed in the building, while temperature control can be automated by having computer controlled air-conditioning systems. However, programming actuators in a large-scale environment for buildings and homes can be time consuming and expensive. This dissertation presents an approach that algorithmically sets up the control system that can automate any building without requiring custom programming. This is achieved by imbibing the system self calibrating and self learning abilities.
For lighting control, the dissertation describes how the problem is non-deterministic polynomial-time hard(NP-Hard) but can be resolved by heuristics. The resulting system controls blinds to ensure uniform lighting and also adds artificial illumination to ensure light coverage remains adequate at all times of the day, while adjusting for weather and seasons. In the absence of daylight, the system resorts to artificial lighting.
For temperature control, the dissertation describes how the temperature control problem is modeled using convex quadratic programming. The impact of every air conditioner on each sensor at a particular time is learnt using a linear regression model. The resulting system controls air-conditioning equipments to ensure the maintenance of user comfort and low cost of energy consumptions. The system can be deployed in large scale environments. It can accept multiple target setpoints at a time, which improves the flexibility and efficiency of cooling systems requiring temperature control.
The methods proposed work as generic control algorithms and are not preprogrammed for a particular place or building. The feasibility, adaptivity and scalability features of the system have been validated through various actual and simulated experiments.
For lighting control, the dissertation describes how the problem is non-deterministic polynomial-time hard(NP-Hard) but can be resolved by heuristics. The resulting system controls blinds to ensure uniform lighting and also adds artificial illumination to ensure light coverage remains adequate at all times of the day, while adjusting for weather and seasons. In the absence of daylight, the system resorts to artificial lighting.
For temperature control, the dissertation describes how the temperature control problem is modeled using convex quadratic programming. The impact of every air conditioner on each sensor at a particular time is learnt using a linear regression model. The resulting system controls air-conditioning equipments to ensure the maintenance of user comfort and low cost of energy consumptions. The system can be deployed in large scale environments. It can accept multiple target setpoints at a time, which improves the flexibility and efficiency of cooling systems requiring temperature control.
The methods proposed work as generic control algorithms and are not preprogrammed for a particular place or building. The feasibility, adaptivity and scalability features of the system have been validated through various actual and simulated experiments.
ContributorsWang, Yuan (Author) / Dasgupta, Partha (Thesis advisor) / Davulcu, Hasan (Committee member) / Huang, Dijiang (Committee member) / Reddy, T. Agami (Committee member) / Arizona State University (Publisher)
Created2015
Description
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.
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.
ContributorsKarizi, Nasim (Author) / Reddy, T. Agami (Thesis advisor) / Bryan, Harvey (Committee member) / Dasgupta, Partha (Committee member) / Kroelinger, Michael D. (Committee member) / Arizona State University (Publisher)
Created2015