INTELLIGENT DEMAND SIDE MANAGEMENT OF RESIDENTIAL BUILDING ENERGY SYSTEMS

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Sinha, M. (2013). INTELLIGENT DEMAND SIDE MANAGEMENT OF RESIDENTIAL BUILDING ENERGY SYSTEMS. Unc Charlotte Electronic Theses And Dissertations.
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Abstract

Advent of modern sensing technologies, data processing capabilities and rising cost of energy are driving the implementation of intelligent systems in buildings and houses which constitute 41% of total energy consumption. The primary motivation has been to provide a framework for demand-side management and to improve overall reliability. The entire formulation is to be implemented on NILM (Non-Intrusive Load Monitoring System), a smart meter. This is going to play a vital role in the future of demand side management. Utilities have started deploying smart meters throughout the world which will essentially help to establish communication between utility and consumers. This research is focused on investigation of a suitable thermal model of residential house, building up control system and developing diagnostic and energy usage forecast tool. The present work has considered measurement based approach to pursue. Identification of building thermal parameters is the very first step towards developing performance measurement and controls. The proposed identification technique is PEM (Prediction Error Method) based, discrete state-space model. The two different models have been devised. First model is focused toward energy usage forecast and diagnostics. Here one of the novel idea has been investigated which takes integral of thermal capacity to identify thermal model of house. The purpose of second identification is to build up a model for control strategy. The controller should be able to take into account the weather forecast information, deal with the operating point constraints and at the same time minimize the energy consumption. To design an optimal controller, MPC (Model Predictive Control) scheme has been implemented instead of present thermostatic/hysteretic control. This is a receding horizon approach. Capability of the proposed schemes has also been investigated.

Details
Author

Sinha, Maruti

Title

INTELLIGENT DEMAND SIDE MANAGEMENT OF RESIDENTIAL BUILDING ENERGY SYSTEMS

Physical Description

1 online resource (164 pages) : PDF

Date

2013

Degree Granting Institution

University of North Carolina at Charlotte

Abstract

Advent of modern sensing technologies, data processing capabilities and rising cost of energy are driving the implementation of intelligent systems in buildings and houses which constitute 41% of total energy consumption. The primary motivation has been to provide a framework for demand-side management and to improve overall reliability. The entire formulation is to be implemented on NILM (Non-Intrusive Load Monitoring System), a smart meter. This is going to play a vital role in the future of demand side management. Utilities have started deploying smart meters throughout the world which will essentially help to establish communication between utility and consumers. This research is focused on investigation of a suitable thermal model of residential house, building up control system and developing diagnostic and energy usage forecast tool. The present work has considered measurement based approach to pursue. Identification of building thermal parameters is the very first step towards developing performance measurement and controls. The proposed identification technique is PEM (Prediction Error Method) based, discrete state-space model. The two different models have been devised. First model is focused toward energy usage forecast and diagnostics. Here one of the novel idea has been investigated which takes integral of thermal capacity to identify thermal model of house. The purpose of second identification is to build up a model for control strategy. The controller should be able to take into account the weather forecast information, deal with the operating point constraints and at the same time minimize the energy consumption. To design an optimal controller, MPC (Model Predictive Control) scheme has been implemented instead of present thermostatic/hysteretic control. This is a receding horizon approach. Capability of the proposed schemes has also been investigated.

Genre

doctoral dissertations

Subjects--Topics

Mechanical engineering
Power resources

Degree

Ph.D.

Keywords

Data Driven Model
Diagnostics
Energy
Model Predictive Control
Prediction Error Method
System Identification

Subject Area

Mechanical Engineering

Advisor(s)

Cox, Robert

Committee Members

Kelly, Scott
Kakad, Yogendra
Soliman, Ahmed

Degree Note

Thesis (Ph.D.)--University of North Carolina at Charlotte, 2013.

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Rights Holder Information

Copyright is held by the author unless otherwise indicated.

Identifier

Sinha_uncc_0694D_10465

Permalink

http://hdl.handle.net/20.500.13093/etd:1425