Date of Award


Degree Name

MS in Electrical Engineering


Electrical Engineering


College of Engineering


Xiao-Hua Yu

Advisor Department

Electrical Engineering

Advisor College

College of Engineering


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.