Date of Award


Document Type


Degree Name

Master of Science (MS)


College of Science and Mathematics


Applied Mathematics and Statistics

Thesis Sponsor/Dissertation Chair/Project Chair

Eric Forgoston

Committee Member

Pankaj Lal

Committee Member

Aparna Varde


Load forecasting is an important tool for both the energy and environmental sectors. It has progressed hand-in-hand with machine learning innovation, where recurrent neural networks, a type of artificial neural network, is primarily used. This thesis compares progressively complex, feed-forward artificial neural networks using a mix of weather and temporal data. We demonstrate that electrical load in New Jersey can be reliably predicted using memory-less algorithms with minimal predictors drawn from preexisting public data sources. The methods used in this thesis could be used to build competitive load forecasting models in other states, and if included in diverse model ensembles, may generate significant improvements.

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