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