DOI: https://doi.org/10.15368/theses.2020.21
Available at: https://digitalcommons.calpoly.edu/theses/2126
Date of Award
3-2020
Degree Name
MS in Electrical Engineering
Department/Program
Electrical Engineering
College
College of Engineering
Advisor
Xiao-Hua Yu
Advisor Department
Electrical Engineering
Advisor College
College of Engineering
Abstract
Many methods have been used to forecast real-time electricity prices in various regions around the world. The problem is difficult because of market volatility affected by a wide range of exogenous variables from weather to natural gas prices, and accurate price forecasting could help both suppliers and consumers plan effective business strategies. Statistical analysis with autoregressive moving average methods and computational intelligence approaches using artificial neural networks dominate the landscape. With the rise in popularity of convolutional neural networks to handle problems with large numbers of inputs, and convolutional neural networks conspicuously lacking from current literature in this field, convolutional neural networks are used for this time series forecasting problem and show some promising results.
This document fulfills both MSEE Master's Thesis and BSCPE Senior Project requirements.