User:Abby.Martin

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Project

Mentor: Dr. Richard Povinelli

I will be researching the application and accuracy of linear regression model trees. I aim to test the effectiveness of this method in assisting with electric load forecasting. I also plan on comparing this method of forecasting to a multitude of other methods that have also been attempted.

Goals & Milestones

  • Test the influence of linear regression model trees on the accuracy of electrical use forecasting.
  • Determine if linear regression model trees are a better method of forecasting electric load forecasting than other methods.
  • Create a linear regression model tree using MATLAB for use in the electric portion of GasDay/ apply data and methods found/created to data from the GasDay lab.
  • Research linear regression model trees and electrical usage.
  • Continue research on linear regression model trees, electrical usage, and MATLAB. Start creating methods for forecasting electrical use using linear regression model trees.
  • Continue research on linear regression model trees and electrical usage. Also learn how to effectively use MATLAB.
  • Test my linear regression model tree with real data and compare its effectiveness with that of other forecasting methods.

Weekly Goals

Week One

  • Read and research papers that address the following topics:
    • Decision Trees
    • Machine Learning
    • Model Trees
    • Linear Regression Model Trees
    • Electric Load Forecasting and other methods that have been used

Week Two

  • Continue reading about linear regression model trees
  • Begin testing various datasets using the WEKA software
  • Begin reading some of the source code and documentation to better understand WEKA
  • Begin learning, using, and applying MATLAB

Week Three

  • Test real data using the WEKA software to create various model trees.
  • Determine what settings are best for forecasting data.

Weekly Log

Week One

  • Orientation activities and forms
  • Pre-REU Survey
  • Attended GasDay Camp
  • Met Dr.Povinelli and decided on research topic
  • Read papers on my topic to discover:
    • the definition and application of decision trees
    • difference between classification trees, regression trees, and model trees
    • machine learning and how trees split

Week Two

  • Met with Dr. Povinelli to further discuss concepts and goals
    • Explained:
      • the "greedy" approach
      • the various ways to determine the "best" variable and tree
      • suggested reading about the M5P Model
  • Read about the M5P Model and learned:
    • Splits using a Standard Deviation Reduction Method
    • Uses a smoothing method for leaves
    • Article contained helpful pseudocode for understanding the process of creating a linear regression model tree
  • Began working with the WEKA software to create linear regression model trees
  • Read some of the source code from WEKA to understand the linear regression model tree creation process
  • Began working with and learning MATLAB