Short-Term Load Forecasting Using Neural Network for Future Smart Grid Application

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

Name thesis:degree_name

M.S.

Level thesis:degree_level

Masters Thesis

Year dc:date.available

2014

Author dc:creator

• Zheng, Jixuan

Contributors dc:contributor

• Wenzhong Gao, Ph.D.
• Mohammad Matin
• Jun Zhang
• Stephen Sewalk

• Policy
• Current status
• Latest products
• Neural network
• Short-term load forecasting
• Smart grid
• Smart meter
• Electrical and Computer Engineering
• Engineering
• Power and Energy

Statement dc:rights

• <p>Copyright is held by the author. User is responsible for all copyright compliance.</p>

Language dc:language

en

Repository record dc:identifier

https://digitalcommons.du.edu/etd/735

OAI identifier oai:identifier

oai:digitalcommons.du.edu:etd-1734