Electricity Price Forecasting Using a Convolutional Neural Network

<p>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.</p> <p><strong>This document fulfills both MSEE Master's Thesis and BSCPE Senior Project requirements</strong>.</p>

Name thesis:degree_name

MS in Electrical Engineering

Discipline thesis:degree_discipline

Electrical Engineering

Year dc:date.available

2020

Author dc:creator

• Winicki, Elliott

Contributors dc:contributor

• Xiao-Hua Yu
• Electrical Engineering
• College of Engineering

• Convolutional neural network
• electricity price forecasting
• time series analysis
• Other Electrical and Computer Engineering

Identifier

10.15368/theses.2020.21

OAI identifier oai:identifier

oai:digitalcommons.calpoly.edu:theses-3594