Artificial Intelligence-Based Short-Term Electric Load Forecasts for Experimental Smart Homes Including HVAC and PV Components

Authors

Rosemary E. Alden, University of KentuckyFollow
Cristinel Ababei, University of Kentucky
Dan M. Ionel, University of KentuckyFollow

Abstract

Problem Formulation

• To predict electric load of the total average power as well as individual components for two residencies from experimental data
• Individual residential forecasting is difficult due to high variability of appliance usage and random human behavior influences
• Separate the HVAC load from total load as a desired profile using weather relationship and minimum HVAC load at night
• Data driven approach to reduce the amount of information about the home required

Document Type

Presentation

Publication Date

7-2021

Notes/Citation Information

A virtual poster presentation at the 2021 IEEE Power & Energy Society General Meeting.

Funding Information 

This work was supported by the NSF, grant ECCF 1936494.

Repository Citation

Alden, Rosemary E.; Ababei, Cristinel; and Ionel, Dan M., "Artificial Intelligence-Based Short-Term Electric Load Forecasts for Experimental Smart Homes Including HVAC and PV Components" (2021). Power and Energy Institute of Kentucky Presentations. 1.
https://uknowledge.uky.edu/peik_present/1