Date of Award


Document Type

Masters Thesis

Degree Name


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.


Electric energy, Load forecasting, Machine learning, Neural networks, Time series predictions, Utility companies


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


Received from ProQuest

File Format




File Size

92 p.


Electrical engineering, Computer engineering, Engineering