Date of Award
5-2022
Document Type
Thesis
Degree Name
Master of Science (MS)
College/School
College of Science and Mathematics
Department/Program
Applied Mathematics and Statistics
Thesis Sponsor/Dissertation Chair/Project Chair
Eric Forgoston
Committee Member
Pankaj Lal
Committee Member
Aparna Varde
Abstract
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.
File Format
Recommended Citation
Raab, Erik W., "Forecasting Electricity Load in New Jersey with Artificial Neural Networks" (2022). Theses, Dissertations and Culminating Projects. 1054.
https://digitalcommons.montclair.edu/etd/1054