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

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

3. 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

4. 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.

5. 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

6. End-Uses by Class

7. Major Assumptions

8. 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

9. SAE Results

10. Commercial SAE Model Results

11. Residential SAE Model Results

12. SDG&E Residential Use per Customer
Great Recession
Residential UPC has declined since 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

13. Residential End-Use Trends

14. Next Steps

15. 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.