Date of Award

1-1-2014

Document Type

Masters Thesis

Degree Name

M.S.

Organizational Unit

Daniel Felix Ritchie School of Engineering and Computer Science

First Advisor

Wenzhong Gao, Ph.D.

Second Advisor

Mohammad Matin

Third Advisor

Jun Zhang

Fourth Advisor

Stephen Sewalk

Keywords

Policy, Current status, Latest products, Neural network, Short-term load forecasting, Smart grid, Smart meter

Abstract

Short-term load forecasting of power system has been a classic problem for a long time. Not merely it has been researched extensively and intensively, but also a variety of forecasting methods has been raised.

This thesis outlines some aspects and functions of smart meter. It also presents different policies and current statuses as well as future projects and objectives of SG development in several countries.

Then the thesis compares main aspects about latest products of smart meter from different companies.

Lastly, three types of prediction models are established in MATLAB to emulate the functions of smart grid in the short-term load forecasting, and then their results are compared and analyzed in terms of accuracy. For this thesis, more variables such as dew point temperature are used in the Neural Network model to achieve more accuracy for better short-term load forecasting results.

Publication Statement

Copyright is held by the author. User is responsible for all copyright compliance.

Rights Holder

Jixuan Zheng

Provenance

Received from ProQuest

File Format

application/pdf

Language

en

File Size

81 p.

Discipline

Electrical engineering, Engineering, Energy

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