Date of Award
1-1-2019
Document Type
Masters Thesis
Degree Name
M.S.
Organizational Unit
Daniel Felix Ritchie School of Engineering and Computer Science, Electrical and Computer Engineering
First Advisor
Mohammad A. Matin, Ph.D.
Second Advisor
George Edwards, Ph.D.
Third Advisor
Mohammed Mahoor, Ph.D.
Fourth Advisor
Yun-bo Yi, Ph.D.
Keywords
Electric energy, Load forecasting, Machine learning, Neural networks, Time series predictions, Utility companies
Abstract
Electric Load Forecasting is essential for the utility companies for energy management based on the demand. Machine Learning Algorithms has been in the forefront for prediction algorithms. This Thesis is mainly aimed to provide utility companies with a better insight about the wide range of Techniques available to forecast the load demands based on different scenarios. Supervised Machine Learning Algorithms were used to come up with the best possible solution for Short-Term Electric Load forecasting. The input Data set has the hourly load values, Weather data set and other details of a Day. The models were evaluated using MAPE and R2 as the scoring criterion. Support Vector Machines yield the best possible results with the lowest MAPE of 1.46 %, a R2 score of 92 %. Recurrent Neural Networks univariate model serves its purpose as the go to model when it comes to Time-Series Predictions with a MAPE of 2.44 %. The observations from these Machine learning models gives the conclusion that the models depend on the actual Data set availability and the application and scenario in play
Publication Statement
Copyright is held by the author. User is responsible for all copyright compliance.
Rights Holder
Harish Amarasundar
Provenance
Received from ProQuest
File Format
application/pdf
Language
en
File Size
92 p.
Recommended Citation
Amarasundar, Harish, "Supervised Machine Learning Techniques for Short-Term Load Forecasting" (2019). Electronic Theses and Dissertations. 1642.
https://digitalcommons.du.edu/etd/1642
Copyright date
2019
Discipline
Electrical engineering, Computer engineering, Engineering