SDG&E’s Statistically Adjusted End-Use (SAE) Sales Forecasting Presented by: Ken Schiermeyer September 14, 2015

Overview Sales Forecasting Process Overview of SAE Data Inputs/End Use Data Sources Major Assumptions Results Next Steps

Sales Forecasting Process The SAE framework represents SDG&E’s two largest customer classes. Various other models estimate the remaining customer classes. Residential SAE Model Commercial SAE Model Industrial Model (Linear Trend) Top 30 Non-Weather Responsive Industrial Customers Agriculture Model (Regression) Lighting Model (Use-Per-Customer) 2015 IEPR was the first time SAE was used by SDG&E

Overview of SAE A modeling framework that incorporates structured explanatory variables: Aggregate end-use inputs Equipment Saturation Equipment Efficiency Thermal Efficiency Home size (Square Footage) Economic drivers: Prices Household size, Economic Activity (Square footage, Personal Income) Monthly weather (HDD, CDD variables) A statistical step that syncs model coefficients to historical consumption.

Saturation and Efficiency Data Sources EIA Regional inputs based on National Energy Modeling System (NEMS) output Share and Efficiency trends by end-use Building Shell trends (thermal efficiency and home size) Saturation Surveys Residential RASS Commercial CEUS

End-Uses by Class

Major Assumptions

EV/PV SAE Integration EV/PV’s are important elements of SDG&E’s operations, making the isolation of their forecasted impacts essential. The objective is to ensure a unit elasticity on these inputs. EV Integration Approach Subtract Historical Monthly EV Impacts from the Residential UPC series. Model Legacy Demand Add the EV Forecast to the model result PV Integration Approach Add Historical Monthly PV Impacts to the Residential UPC series Model Demand Subtract PV Forecast from Model Result to generate Delivery Forecast

SAE Results

Commercial SAE Model Results

Residential SAE Model Results

SDG&E Residential Use per Customer Great Recession Residential UPC has declined since 2008. What is driving the decling? California Energy Crisis Between 1974 to 2008 – 3.4% growth. Between 1998 and 2008 – 1.5% growth. 38 TWh – roughly 40% reduction from that seen in the past

Residential End-Use Trends

Next Steps

Next Steps: Saturation and Efficiency Data Sources EIA Regional inputs based on National Energy Modeling System (NEMS) output Share and Efficiency trends by end-use Building Shell trends (thermal efficiency and home size) Saturation Surveys Residential RASS Commercial CEUS CPUC 2013 Potential and Goals Study Market Potential Case. The energy efficiency savings that could be expected in response to specific levels of incentives and assumptions about market influences and barriers.