This presentation deals with two simple inverse modeling methods and data monitoring protocols which can be used to identify statistical models that would result in accurate daily energy use predictions. The Dry Bulb Temperature Analysis (DBTA) method only requires measuring dry-bulb ambient temperature for 2-3 months but the monitoring period and length have to be selected judiciously. The Hybrid Inverse Model using daily data (HIM-D) only requires about one month of monitoring and utility bills. The model combines information from recent year-long utility bill data along with a few weeks of monitored building energy use, weather variables and internal loads.