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
","abstract_html":"<p>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</p>","abstract_has_math":false,"creators":["Amarasundar, Harish"],"institution":null,"degree_name":"M.S.","degree_level":"Masters Thesis","degree_discipline":null,"degree_department":null,"school":null,"contributors":["Mohammad A. Matin, Ph.D.","George Edwards, Ph.D.","Mohammed Mahoor, Ph.D.","Yun-bo Yi, Ph.D."],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2019,"date_issued":"2019-01-01T08:00:00Z","date_published":"2019-01-01T08:00:00Z","updated_at":"2026-07-24T02:03:03Z","subjects":["Electric energy","Load forecasting","Machine learning","Neural networks","Time series predictions","Utility companies","Electrical and Computer Engineering","Engineering"],"languages":["en"],"rights":["Copyright is held by the author. User is responsible for all copyright compliance.
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"],"dc:subject":["Electric energy","Load forecasting","Machine learning","Neural networks","Time series predictions","Utility companies","Electrical and Computer Engineering","Engineering"],"dc:title":["Supervised Machine Learning Techniques for Short-Term Load Forecasting"],"thesis:degree_level":["Masters Thesis"],"thesis:degree_name":["M.S."]},"updated_at":"2026-07-24T02:03:03Z"}