Development of a Software Platform with Distributed Learning Algorithms for Building Energy Efficiency and Demand Response Applications
| dc.contributor.author | Saha, Avijit | en |
| dc.contributor.committeechair | Rahman, Saifur | en |
| dc.contributor.committeemember | De La Ree, Jaime | en |
| dc.contributor.committeemember | Yu, Guoqiang | en |
| dc.contributor.committeemember | Haghighat, Alireza | en |
| dc.contributor.committeemember | Pipattanasomporn, Manisa | en |
| dc.contributor.department | Electrical and Computer Engineering | en |
| dc.date.accessioned | 2017-01-25T09:00:16Z | en |
| dc.date.available | 2017-01-25T09:00:16Z | en |
| dc.date.issued | 2017-01-24 | en |
| dc.description.abstract | In the United States, over 40% of the country's total energy consumption is in buildings, most of which are either small-sized (<5,000 sqft) or medium-sized (5,000-50,000 sqft). These buildings offer excellent opportunities for energy saving and demand response (DR), but these opportunities are rarely utilized due to lack of effective building energy management systems and automated algorithms that can assist a building to participate in a DR program. Considering the low load factor in US and many other countries, DR can serve as an effective tool to reduce peak demand through demand-side load curtailment. A convenient option for the customer to benefit from a DR program is to use automated DR algorithms within a software that can learn user comfort preferences for the building loads and make automated load curtailment decisions without affecting customer comfort. The objective of this dissertation is to provide such a solution. First, this dissertation contributes to the development of key features of a building energy management open source software platform that enable ease-of-use through plug and play and interoperability of devices in a building, cost-effectiveness through deployment in a low-cost computer, and DR through communication infrastructure between building and utility and among multiple buildings, while ensuring security of the platform. Second, a set of reinforcement learning (RL) based algorithms is proposed for the three main types of loads in a building: heating, ventilation and air conditioning (HVAC) loads, lighting loads and plug loads. In absence of a DR program, these distributed agent-based learning algorithms are designed to learn the user comfort ranges through explorative interaction with the environment and accumulating user feedback, and then operate through policies that favor maximum user benefit in terms of saving energy while ensuring comfort. Third, two sets of DR algorithms are proposed for an incentive-based DR program in a building. A user-defined priority based DR algorithm with smart thermostat control and utilization of distributed energy resources (DER) is proposed for residential buildings. For commercial buildings, a learning-based algorithm is proposed that utilizes the learning from the RL algorithms to use a pre-cooling/pre-heating based load reduction method for HVAC loads and a mixed integer linear programming (MILP) based optimization method for other loads to dynamically maintain total building demand below a demand limit set by the utility during a DR event, while minimizing total user discomfort. A user defined priority based DR algorithm is also proposed for multiple buildings in a community so that they can participate in realizing combined DR objectives. The software solution proposed in this dissertation is expected to encourage increased participation of smaller and medium-sized buildings in demand response and energy saving activities. This will help in alleviating power system stress conditions by employing the untapped DR potential in such buildings. | en |
| dc.description.abstractgeneral | In the US and many other countries around the world, the daily peak load experienced is frequently much higher than the daily average load. This low load factor causes inefficient use of generation and transmission resources. Besides inefficient use, the peak load also increases system stress conditions resulting from inadequate generation, transmission line outages or transformer failures. This can create supply-limit conditions which may induce cascaded failures and large area blackouts. To avoid system stress conditions due to increasing demand and to use power system resources more efficiently, demand response (DR) serves as an effective tool to reduce peak demand through demand-side load curtailment. This dissertation focuses on DR applications in buildings. In the United States, buildings consume over 40% of the country’s total energy use. These includes both commercial and residential buildings. Most of the commercial buildings are either small-sized (<5,000 sqft) or medium-sized (5,000-50,000 sqft). These buildings offer excellent opportunities for demand response, which can be implemented through use of building energy management/building automation software. But, building automation software is not yet very popular in small and medium-sized buildings due to lack of low-cost and easy-to-use software solutions. A DR program offered by a utility can be price-based or incentive-based. Price-based DR programs employ dynamic pricing structure to encourage customers to reduce consumption to save bills, whereas incentive-based programs focus on customer commitment to the utility for providing requested load curtailment during peak load situations, in return for monthly or yearly monetary incentives. As most of the peak load reduction potential comes from incentive-based DR programs, this dissertation focuses on an incentive-based DR program. A customer can conveniently participate in such a program by using automated DR algorithms within an energy management software that can control building loads without customer intervention. Providing load curtailment may interfere with customer comfort, and therefore these algorithms must learn customer comfort preferences and consider them while making load shedding decisions. In this dissertation, a software solution is developed for demand response implementation in buildings, which includes contribution to a secure software platform that enables monitoring and control of loads, and automated learning-based algorithms that can learn customer comfort ranges for building loads and use this learning to make load curtailment decisions in an incentive-based DR program, while ensuring customer comfort. | en |
| dc.description.degree | Ph. D. | en |
| dc.format.medium | ETD | en |
| dc.identifier.other | vt_gsexam:9586 | en |
| dc.identifier.uri | http://hdl.handle.net/10919/74423 | en |
| dc.publisher | Virginia Tech | en |
| dc.rights | In Copyright | en |
| dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | en |
| dc.subject | Building Energy Management System | en |
| dc.subject | Energy Efficiency | en |
| dc.subject | Demand Response | en |
| dc.subject | Internet of Things | en |
| dc.subject | Reinforcement Learning | en |
| dc.title | Development of a Software Platform with Distributed Learning Algorithms for Building Energy Efficiency and Demand Response Applications | en |
| dc.type | Dissertation | en |
| thesis.degree.discipline | Electrical Engineering | en |
| thesis.degree.grantor | Virginia Polytechnic Institute and State University | en |
| thesis.degree.level | doctoral | en |
| thesis.degree.name | Ph. D. | en |
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