A Data-driven Approach for Coordinating Air Conditioning Units in Buildings during Demand Response Events

Zhang, Xiangyu

A Data-driven Approach for Coordinating Air Conditioning Units in Buildings during Demand Response Events

dc.contributor.author	Zhang, Xiangyu	en
dc.contributor.committeechair	Rahman, Saifur	en
dc.contributor.committeemember	Broadwater, Robert P.	en
dc.contributor.committeemember	Yu, Guoqiang	en
dc.contributor.committeemember	Lu, Chang-Tien	en
dc.contributor.committeemember	Pipattanasomporn, Manisa	en
dc.contributor.department	Electrical Engineering	en
dc.date.accessioned	2019-02-07T09:00:27Z	en
dc.date.available	2019-02-07T09:00:27Z	en
dc.date.issued	2019-02-06	en
dc.description.abstract	Among many smart grid technologies, demand response (DR) is gaining increasing popularity. Many utility companies provide a variety of programs to encourage DR participation. Under these circumstances, various building energy management (BEM) systems have emerged to facilitate the building control during a DR event. Nonetheless, due to the cost and return on investment, these solutions mainly target homes and large commercial buildings, leaving aside small- and medium-sized commercial buildings (SMCB). SMCB, however, accounts for 90% of commercial buildings in the US, and offer great potential of load reduction during peak hours. With the advent of Internet-of-Things (IoT) devices and technologies, low cost smart building solutions have become possible for the SMCB; nonetheless, related intelligent algorithms are not widely available. This dissertation work investigates automated building control algorithms, tailored for the SMCB, to realize automatic device control during DR events. To be specific, a control framework for Air-Conditioning (AC) units' coordination is proposed. The goal of such framework is to reduce the aggregated AC power consumption while maintaining the thermal comfort inside a building during DR events. To achieve this goal, three major components of the framework were studied: building thermal property modeling, AC power consumption modeling and control algorithms design. Firstly, to consider occupants' thermal comfort, a reverse thermal model was designed to predict the indoor temperature of thermal zones under different AC control signals. The model was trained with supervised learning using coarse-grained temperature data recorded by smart thermostats; thus, it requires no lengthy configuration as a forward model does. The cost efficiency and plug-and-play feature of the model make it appropriate for SMCB. Secondly, a power disaggregation algorithm is proposed to model the power-outdoor temperature relationship of multiple AC units, using data from a single power meter and thermostats. Finally, algorithms based on mixed integer linear programming (MILP) and reinforcement learning (RL) were devised to coordinate multiple AC units in a building during a DR event. Integrated with the thermal model and AC power consumption model, these algorithms minimize occupants' thermal discomfort while restricting the aggregated AC power consumption below the DR limit. The efficiency of these control algorithms was tested, which demonstrate that they can generate AC control schedule in short notice (5 minutes) ahead of a DR event. Verification and validation of the proposed framework was conducted in both simulation and actual building environments. In addition, though the framework is designed for SMCBs, it can also be applied to large homes with multiple AC units to coordinate. This work is expected to give an insight into the BEM sector, helping the popularization of implementing DR in buildings. The research findings from this dissertation work shows the validity of the proposed algorithms, which can be used in BEM systems and cloud-based smart thermostats to exploit the untapped DR resource in SMCB.	en
dc.description.abstractgeneral	For power system operation, the demand and supply should be equal at all time. During peak hours, the demand becomes very high. One way to keep the balance is to provide more generation capacity, and thus more expensive and less efficient generators are brought online, which causes higher production cost and more pollution. Instead, an alternative is to encourage the load reduction via demand response (DR): customers reduce load upon receiving a signal sent by the utility company, usually in exchange for some monetary payback. For buildings to participate in DR, an affordable automation system and related control algorithms are needed. This dissertation proposed a cost-effective, self-learning and data-driven framework to facilitate small- and medium-sized commercial buildings or large homes in air-conditioner (AC) units control during DR events. The devised framework requires little human configuration; it learns the building behavior by analyzing the operation data. Two algorithms are proposed to coordinate multiple AC units in a building with two goals: firstly, reducing the total AC power consumption below certain limit, as agreed between the building owners and their utility company. Secondly, minimizing occupants’ thermal discomfort caused by limiting AC operation. The effectiveness of the framework is investigated in this dissertation based on data collected from a real building.	en
dc.description.degree	PHD	en
dc.format.medium	ETD	en
dc.identifier.other	vt_gsexam:18533	en
dc.identifier.uri	http://hdl.handle.net/10919/87517	en
dc.publisher	Virginia Tech	en
dc.rights	In Copyright	en
dc.rights.uri	http://rightsstatements.org/vocab/InC/1.0/	en
dc.subject	smart grid	en
dc.subject	demand response	en
dc.subject	HVAC coordination	en
dc.subject	building thermal model	en
dc.subject	reinforcement learning	en
dc.title	A Data-driven Approach for Coordinating Air Conditioning Units in Buildings during Demand Response Events	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	PHD	en