College - Author 1
College of Engineering
Department - Author 1
Electrical Engineering Department
Degree Name - Author 1
BS in Electrical Engineering
College - Author 2
College of Engineering
Department - Author 2
Electrical Engineering Department
Degree - Author 2
BS in Electrical Engineering
College - Author 3
College of Engineering
Department - Author 3
Electrical Engineering Department
Degree - Author 3
BS in Electrical Engineering
Date
6-2022
Primary Advisor
Xiao-Hua (Helen) Yu, College of Engineering, Electrical Engineering Department
Abstract/Summary
The price of electricity can be very unpredictable as it is determined by many different factors including the usage at that time, weather, outages, location, and even the state of the economy. The ability to predict the price of electricity presents great value for both the consumers of electricity as well as the utility company themselves. All the factors in electricity pricing that were mentioned earlier add significant difficulty to this task. This prediction model can predict electricity prices while considering all these complex factors by using computational intelligence alongside a neural network processing past electrical pricing data to predict what the price will be in the future. This paper will differentiate itself from studies in the past by considering temperature data, a factor that has been an overlooked factor in the past and plays a large role in the transmission of electricity and in turn, the price of electricity.
URL: https://digitalcommons.calpoly.edu/eesp/565