Supervised Machine Learning Techniques for Short-Term Load Forecasting

مرکز و کتابخانه مطالعات اسلامی به زبان های اروپایی

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" Supervised Machine Learning Techniques for Short-Term Load Forecasting "
Amarasundar, Harish Matin, Mohammad A.

Document Type	:	Latin Dissertation
Language of Document	:	English
Record Number	:	1052475
Doc. No	:	TL51592
Main Entry	:	Amarasundar, Harish
Title & Author	:	Supervised Machine Learning Techniques for Short-Term Load Forecasting\ Amarasundar, HarishMatin, Mohammad A.
College	:	University of Denver
Date	:	2019
Degree	:	M.S.
student score	:	2019
Note	:	93 p.
Abstract	:	Electric Load Forecasting is essential in today's world for the utility companies to allocate their resources economically and plan accordingly for future consumption 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 dierent scenarios. This Thesis is focused on modelling Supervised Machine Learning Algorithms to come up with the best possible solution for Short-Term Hour ahead Electric Load forecasting. The Data set for this Thesis comprises of Hour ahead Real time Load data from Electrical Reliability Council of Texas from the year 2018. The input Data set has the hourly load values, Weather data set and other details of a Day. The models were evaluated using Mean Absolute Percentage Error (MAPE) and R-Squared (R2) as the scoring criterion. Support Vector Machines yield the best possible results with the lowest Mean Absolute Percentage Error of 1.46%, a R2 score of 92% and the least computation time for the data set used in this Thesis. 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.
Descriptor	:	Artificial intelligence
	:	Computer engineering
	:	Electrical engineering
Added Entry	:	Matin, Mohammad A.
Added Entry	:	University of Denver