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.
","abstract_html":"<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>","abstract_has_math":false,"creators":["Winicki, Elliott"],"institution":null,"degree_name":"MS in Electrical Engineering","degree_level":null,"degree_discipline":"Electrical Engineering","degree_department":null,"school":null,"contributors":["Xiao-Hua Yu","Electrical Engineering","College of Engineering"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2020,"date_issued":"2020-03-01T08:00:00Z","date_published":"2020-03-01T08:00:00Z","updated_at":"2026-07-24T01:32:37Z","subjects":["Convolutional neural network","electricity price forecasting","time series analysis","Other Electrical and Computer Engineering"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["10.15368/theses.2020.21"],"render_values":[{"text":"10.15368/theses.2020.21","href":"https://doi.org/10.15368/theses.2020.21","code":true}]}]},"links":{"outbound_url":"https://digitalcommons.calpoly.edu/theses/2126","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Xiao-Hua Yu","Electrical Engineering","College of Engineering"]},{"key":"dc:creator","label":"Author","values":["Winicki, Elliott"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2023-03-20T07:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical Engineering"]},{"key":"thesis:degree_name","label":"Degree Name","values":["MS in Electrical Engineering"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Convolutional neural network","electricity price forecasting","time series analysis","Other Electrical and Computer Engineering"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://digitalcommons.calpoly.edu/theses/2126","10.15368/theses.2020.21"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["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.
"]},{"key":"dc:title","label":"Title","values":["Electricity Price Forecasting Using a Convolutional Neural Network"]}]}],"canonical_facts":{"dc:contributor":["Xiao-Hua Yu","Electrical Engineering","College of Engineering"],"dc:creator":["Winicki, Elliott"],"dc:date.available":["2023-03-20T07:00:00Z"],"dc:description.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.
"],"dc:identifier":["https://digitalcommons.calpoly.edu/theses/2126","10.15368/theses.2020.21"],"dc:subject":["Convolutional neural network","electricity price forecasting","time series analysis","Other Electrical and Computer Engineering"],"dc:title":["Electricity Price Forecasting Using a Convolutional Neural Network"],"thesis:degree_discipline":["Electrical Engineering"],"thesis:degree_name":["MS in Electrical Engineering"]},"updated_at":"2026-07-24T01:32:37Z"}