Predicting Energy Consumption using Machine Learning and Deep Learning

Inamdar, Sana Salim (2024) Predicting Energy Consumption using Machine Learning and Deep Learning. Masters thesis, Dublin, National College of Ireland.

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Abstract

Forecasting the increasing demand of electricity has become important these days due to the increase in demand of Energy consumption. The increase in population is also the main reason for the increase in the demand of energy consumption. If this Exponential increasing demand for electricity is not fulfilled it can result in Blackouts and to prevent the blackout forecasting Energy Consumption should be done well in advance. In our study ,PJM electric grid dataset has been used. This dataset contain information about different companies based in the United states which has been spread over 13 different states. Based on this data we have selected Machine Learning and Deep Learning techniques that will be performed. In Machine learning the algorithms that are performed are Linear Regression, Random Forest and XG Boost whereas in deep learning the algorithms that are performed are LSTM and RNN. In terms of Machine Learning algorithms the model that performed well was Random Forest with accuracy of 99.91% and error score of 996 Mw, whereas in Deep learning algorithms the model that performed well with good accuracy of 99.46% and less error score of 5818 Mw was LSTM.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Mulwa, Catherine UNSPECIFIED
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science H Social Sciences > HC Economic History and Conditions > Natural resources > Power resources > Energy consumption Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning
Divisions:	School of Computing > Master of Science in Data Analytics
Depositing User:	Ciara O'Brien
Date Deposited:	18 Aug 2025 15:43
Last Modified:	18 Aug 2025 15:43
URI:	https://norma.ncirl.ie/id/eprint/8575

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