Variables For Each Time Horizon

Variables For Each Time Horizon Andy Sukenik Itron s Forecasting Brown Bag Seminar December 13th, 2011

Please Remember In order to help this session run smoothly, your phones are muted. To make the presentation portion of the screen larger, press the expand button on the toolbar. Press it again to return to regular window. If you need to give other feedback to the presenter during the meeting, such as, slow down or need to get the presenters attention for some other reason, use the pull down menu in the seating chart and we will address it right away. If you have questions, please type your question in the Q&A box in the bottom, right corner. We will try to answer as many questions as we can. 2009, Itron Inc. 2

2011 2012 Brown Bag Seminars Modeling Error Variances Understanding CH, ARCH and GARCH models March h29, 2011 What is a Good Model? June 21, 2011 System Operations Forecasting September 13, 2011 Variables For Each Time Horizon December 13, 2011 Using Conditional Heteroskedastic Variance Models in Load Research Sample Design March 6, 2012 Commercial Sales Modeling and Forecasting June 19, 2012 Forecast Accuracy vs. Forecast Stability September 18, 2012 Smart Grid Forecasting Dec 11, 2012 All at noon, Pacific Time All are recorded and available for review after the session. 2009, Itron Inc. 3

What Factors Drive Changes in Energy Usage? Calendar Conditions Solar Conditions Weather Conditions Equipment Stock Characteristics Economic Activity Price Demand dside Management t(dsm) Demand Response (DR) Solar Photovoltaics 2009, Itron Inc. 4

Business Processes Supported by Forecasting Operational Financial/Budget Capacity Planning Forecasting Forecasting Short-term Forecasting to support System Operations and Energy Trading Hourly Load One to Three year-ahead Sales forecasts Revenue forecasts Variance analysis Daily Sales Long-term Sales, Peak and Hourly Load Forecasting Demand Response Wind Monthly Revenue Long Term Forecast Short Term (e.g, Day ahead) Medium Term (e.g, Budget Forecast) Long Term (e.g, 5 20 years) 2009, Itron Inc. 5

Forecast Driver Outline Forecast Horizon Calendar Solar Weather Equipment Stock Economics / Price DSM Photovoltaics DR Level Adjustment Operational Financial Capacity Planning (Short Term) (Medium Term) (Long Term) 2009, Itron Inc. 6

Operational Forecasting Forecast Horizon Calendar Solar Weather Equipment Stock Economics / Price DSM Photovoltaics DR Level Adjustment Operational Financial Capacity Planning (Short Term) (Medium Term) (Long Term) 2009, Itron Inc. 7

Operational Forecasting Overview Operational Forecasts are short term term (5 minutes ahead to 10 days ahead). Emphasis is on Accuracy! Modeling Frameworks use Interval Data as the dependent variable. (e.g. 5 Minute, 15 Minute, 30 Minute, and Hourly loads.) Itron s approach involves a single specification, but model equations for each interval of the day. Estimation Period uses 3 5 years of data, but may use less. 2009, Itron Inc. 8

Forecast Drivers Operational Forecasting Forecast Horizon Calendar Solar Weather Equipment Stock Economics / Price DSM Photovoltaics DR Level Adjustment Operational Financial Capacity Planning (Short Term) (Medium Term) (Long Term) 2009, Itron Inc. 9

Operational Forecasting Calendar Variables DayType Binaries DOW (Mon, Tue,.. Sun) DayType (Weekday, Weekend) Holiday Variables (e.g. Thanksgiving, Christmas, New Year s) Daylight yg Savings Binary Industrial Shutdown Binaries Monthly Binaries (Jan, Feb.. Dec) 2009, Itron Inc. 10

Operational Forecasting Solar (Demand) Lighting loads depend on SolarConditions Approaches for incorporating Solar Conditions Short Term Models use Fraction Dark Variables (e.g. Hr5_FD, Hr6_FD, HR7_FD, Hr17_FD, HR18_FD) Monthly Extension Variables (Jan DOM, Feb Day.. Dec DOM) 2009, Itron Inc. 12

Operational Forecasting Weather Variables Heating and Cooling Degree Day Day Variables Cut points that vary throughout the day Weather Variable Frameworks 1. Time of Day Weather Variables (NightDD, MorningDD, AfternoonDD, EveningDD) 2. Rolling Weather Variables (e.g. 3 Hour Rolling, 6 Hour Rolling) Lagged Variables Calendar Interactions Night Morning Afternoon Evening 2009, Itron Inc. 14

Operational Forecasting Wind and Clouds Wind Speed and Cloud Cover also impact demand. Wind Speed Increases load when it s Cold Decreases load when it s Hot Cloud Cover Increases load when it s Cold Decreases load when it s Hot Wind Speed and Cloud Cover variables should be interacted with Hot and Cold day binaries. 2009, Itron Inc. 15

Forecast Drivers Operational Forecasting Forecast Horizon Calendar Solar Weather Equipment Stock Economics / Price DSM Operational Financial Capacity Planning (Short Term) (Medium Term) (Long Term) NA NA NA Photovoltaics DR Level Adjustment 2009, Itron Inc. 17

Operational Forecasting Solar PV s (Supply) SolarConditions also impactembedded PV generation. PV Generation drivers: Sun Angles Solar Irradiance Cell Temperature (Higher Temperatures = Lower Output) Model per KW Unit of Capacity 2009, Itron Inc. 18

Operational Forecasting Demand Response DemandResponse (DR) Programsdrive abrupt shifts in load. Peak Saver Programs Load Curtailments The short term forecaster must be informed of historical and future dispatch levels. There are three approaches for incorporating DR: 1. Binary 2. Add Back 3. Demand Response Variable 2009, Itron Inc. 20

Operational Forecasting Level Adjustment End Shift Autoregressive Models (Lagged Interval Loads) Lagged Dependents (Yesterday s s Load) MetrixIDR, Itron s Automated Short term Forecasting System, contains special functionality designed for calibrating the short term forecast into recent actual observations: Across Day Back Calibration i Within Day Forward Tuning 2009, Itron Inc. 22

Operational Forecasting Summary Forecast Horizon Calendar Solar Weather Equipment Stock Economics / Price DSM Photovoltaics DR Level Adjustment Operational (Short Term) NA NA NA Calendar Variables and Weather Response functions are key. Demand Response impacts should be managed. A Level Adjustment process leverages the most recent load data. 2009, Itron Inc. 23

Financial Forecasting Forecast Horizon Calendar Solar Weather Equipment Stock Economics / Price DSM Operational Financial Capacity Planning (Short Term) (Medium Term) (Long Term) NA NA NA Photovoltaics DR Level Adjustment 2009, Itron Inc. 24

Financial Forecasting Overview Financial Forecasts are Medium term (1 3 years ahead) Estimation Range is 10 15 Years Class level Budget Customers, Sales, and Revenue Financial Variance Analysis General Rate Case Filings Dual Focus Forecast Models and underlying assumptions must withstand Regulatory Scrutiny Accuracy and Financial Expectations 2009, Itron Inc. 25

Financial Forecasting Billing Days FinancialForecasting Forecasting Models must account for variation in the number of Billing Days in each month. Options for handling variations in Billing Days: Model Use per Day Interact with every right-hand side variable Integrates Forward Looking Meter Read Schedule into the Monthly Sales Forecast 2009, Itron Inc. 26

Forecast Drivers Financial Forecasting Forecast Horizon Operational Financial Capacity Planning (Short Term) (Medium Term) (Long Term) Calendar Solar NA Weather Equipment Stock Economics / Price DSM NA NA NA Photovoltaics DR Level Adjustment 2009, Itron Inc. 27

Financial Forecasting Calendar & Solar Monthly Binaries (Jan, Feb.. Dec) capture the impact of seasonality on the monthly sales usage patterns. Monthly Binaries proxy for changes in Sunlight, which drives changes in Lighting Loads 2009, Itron Inc. 28

Financial Forecasting Weather Variables Compute Daily Heating and Cooling Degree Days (HDD s and CDD s (or TDD s) ) for multiple bases (e.g. CDD 60, CDD 65, CDD 70). Evaluate Load Research data to develop multi part weather response functions. Aggregate the Daily HDD s and CDD s over: Billing Months Calendar Months Unbilled Period Load Research Data 2009, Itron Inc. 30

Forecast Drivers for Financial Forecasting Forecast Horizon Operational Financial Capacity Planning (Short Term) (Medium Term) (Long Term) Calendar Solar NA Weather Equipment Stock NA? Economics / Price DSM NA NA Photovoltaics DR Level Adjustment 2009, Itron Inc. 31

Financial Forecasting Building Shell & Equipment Stock Generally, medium term (1 3 year ahead)forecast accuracy is not improved by building shell and equipment stock inputs. However, their integration can enhance the forecast Single Forecast for Medium and Long Term End Use Information More Accurate Weather Slopes 2009, Itron Inc. 32

Forecast Drivers Financial Forecasting Forecast Horizon Operational (Short Term) Financial (Medium Term) Calendar Capacity Planning (Long Term) Solar NA Weather Equipment Stock NA Economics / Price NA DSM Photovoltaics DR Level Adjustment NA 2009, Itron Inc. 33

Financial Forecasting Economic Variables The integration of Economic Activity into Financial Forecast Models requires identifying Economic Variable(s) that meet the following criteria: Highly correlated historically with Sales trends Forecasted with accuracy by Economic Vendors Recent research indicates using a composite economic drivers tends to outperform a single driver using forecast accuracy and stability as equally weighted evaluation measures. Residential Drivers Households, Real Income per Household, People per Household Commercial l& Industrial ldi Drivers Population, Employment, Real GMP, Real GDP, Industrial Production 2009, Itron Inc. 34

Financial Forecasting Price Theory: Energy Consumption is inversely related to Real Prices Practice: It s often difficult to get a reasonable coefficient on a real price term. Alternative approaches include: Index and Impose an Elasticity Use Shift variables to account for abrupt changes in price 2009, Itron Inc. 35

Forecast Drivers Financial Forecasting Forecast Horizon Operational Financial Capacity Planning (Short Term) (Medium Term) (Long Term) Calendar Solar NA Weather Equipment Stock NA Economics / Price NA DSM NA Photovoltaics DR Level Adjustment 2009, Itron Inc. 36

Financial Forecasting DSM Integration Base assumption: DSM will maintainits its historical pace throughout the forecast period. The forecaster must determine whether this is a reasonable assumption If adjustment is required, the forecaster may choose to implement one of the following three approaches: Add Back DSM Variable Trend 2009, Itron Inc. 37

Forecast Drivers Financial Forecasting Forecast Horizon Operational Financial Capacity Planning (Short Term) (Medium Term) (Long Term) Calendar Solar NA Weather Equipment Stock NA Economics / Price NA DSM NA Photovoltaics DR Level Adjustment 2009, Itron Inc. 38

Financial Forecasting Solar PV s Inthe medium term framework, PVhandlingis is conceptually very similar to DSM. Base assumption: Photovoltaic Generation will maintain its historical pace throughout the forecast period. The same DSM integration methods apply. Add Back DSM Variable Trend 2009, Itron Inc. 39

Forecast Drivers Financial Forecasting Forecast Horizon Operational Financial Capacity Planning (Short Term) (Medium Term) (Long Term) Calendar Solar NA Weather Equipment Stock NA Economics / Price NA DSM NA Photovoltaics DR NA Level Adjustment 2009, Itron Inc. 40

Forecast Drivers Financial Forecasting Forecast Horizon Operational Financial Capacity Planning (Short Term) (Medium Term) (Long Term) Calendar Solar NA Weather Equipment Stock NA Economics / Price NA DSM NA Photovoltaics DR NA Level Adjustment 2009, Itron Inc. 41

Financial Forecasting Level Adjustment An End Shift variable ties the forecast into recent data End shift variable position can be determined based on the Model Residual pattern. ARMA corrections also adjust levels within the estimation period, but they die out in the forecast. Level shifts live on. 2009, Itron Inc. 42

Financial Forecasting Summary Forecast Horizon Calendar Solar Financial (Medium Term) NA Billing Day management and Weather Response functions explaintemporary fluctuations Economic variables should be evaluated carefully. Weather Equipment Stock y Economics / Price DSM Photovoltaics DR NA Level ladjustment t Weather < >Trend interactions should be included. End Shifts further support the forecast. 2009, Itron Inc. 43

Capacity Planning Forecasting Forecast Horizon Operational Financial Capacity Planning (Short Term) (Medium Term) (Long Term) Calendar Solar NA Weather Equipment Stock NA Economics / Price NA DSM NA Photovoltaics DR NA Level Adjustment 2009, Itron Inc. 44

Capacity Planning Forecasting CapacityPlanningForecasts are Long term (5 years ahead and beyond) Capacity Planning Models are developed to forecast monthly energy and peaks, as well as 8760 s. The emphasis is on the long term projections. Billing Days should be managed if the forecast is based on Sales data. 2009, Itron Inc. 45

Forecast Drivers for Capacity Planning Forecasting Forecast Horizon Operational Financial Capacity Planning (Short Term) (Medium Term) (Long Term) Calendar Solar NA Weather Equipment Stock NA Economics / Price NA DSM NA Photovoltaics DR NA Level Adjustment 2009, Itron Inc. 46

Capacity Planning Forecasting Calendar & Solar Monthly Binaries (Jan, Feb.. Dec) capture the impact of seasonality on the monthly sales usage patterns. They proxy for changing Solar Conditions Long term 8760 s can be enhanced using Daytype Variables as well as Fraction Dark or Monthly Extension terms 2009, Itron Inc. 47

Capacity Planning Forecasting Energy Weather Variables Compute Daily Heating and Cooling Degree Days Days (HDD s and CDD s (or TDD s) ) for multiple bases (e.g. CDD 60, CDD 65, CDD 70) Aggregate the Daily HDD s and CDD s over: Calendar Months Interact Degree Day variables with Seasonal Binaries, and Long term Trend variables 2009, Itron Inc. 49

Capacity Planning Forecasting Peak Weather Variables Extract Daily Temperature (or Daily THI) on the peak day and two prior days Evaluate scatter plot of Monthly Peaks vs. Peak Day Temperature to define cut points for weather variables. Single or Multiple Breakpoint 2009, Itron Inc. 50

Capacity Planning Forecasting Building Shell & Equipment Stock Long term forecasts should account for changes in Building Shell Characteristics and End Use Equipment Stock Through Itron s Energy Forecasting Group (EFG), members gain access to the following EIA data on a Regional basis: Residential Square Footage and Thermal Shell Integrity Trends Residential End Use Saturations and Efficiencies Commercial Floor Stock and Building Shell Integrity Trends Commercial End Use Fuel Shares and Intensities 2009, Itron Inc. 52

Capacity Planning Forecasting Economic and Price Variables Capacity Planning models should account for long term changes in economic activity levels and real prices However, these terms must be properly interacted with BuildingandEquipment Stocktrends trends. SAE Modeling Framework facilitates this interaction 2009, Itron Inc. 54

Residential SAE Modeling Framework nd tock Building an uipment S B Eq Thermal Efficiency Home Square Footage AC Saturation Central Heat Pump Room AC AC Efficiency Thermal Efficiency Home Square Footage Heating Saturation Resistance Heat Pump Heating Efficiency Saturation Levels Wt Water Heat Appliances Lighting Densities Plug Loads Appliance Efficiency Utilization Real Income / HH Household Size Price CDD Spline Real Income / HH Real Income / HH Household Size Household Size Price Price HDD Spline XCool XHeat XOther Sales m a b c XCool m b h XHeat 2009, Itron Inc. 55 m b o XOther m e m

Capacity Planning Forecasting DSM Integration Inthe SAE framework, the DSMshould represent savings above and beyond the SAE efficiencies. Base assumption: DSM will maintain its historical pace throughout the forecast period. The forecaster must determine whether this is a reasonable assumption and adjust if using one of the three recommended methods. Add Back DSM Variable Trend 2009, Itron Inc. 57

Capacity Planning Forecasting Solar PV s Inthe long term framework, PVhandlingis is conceptually very similar to DSM. Base assumption: Photovoltaic Generation will maintain its historical pace throughout the forecast period. The forecaster must determine whether this is a reasonable assumption and adjust if necessary 2009, Itron Inc. 59

Capacity Planning Forecasting Demand Response Demand Response programs are designed to reduce Peak loads. There impact on Energy is minimal Base assumption: DR will maintain its historical pace throughout the forecast period. If DR is to be incorporated explicitly, the forecaster may choose to implement one of the following three approaches: Add Back DSM Variable Trend 2009, Itron Inc. 61

Capacity Planning Forecasting Level Adjustment An End Shift variable ties the forecast into recent data 2009, Itron Inc. 63

Capacity Planning Summary Capacity Planning Forecast Horizon Capacity Planning (Long Term) Forecast Models requires Calendar the forecaster to examine Solar each driving factor. Weather Equipment Stock Economics / Price DSM Photovoltaics DR Level Adjustment Building and Equipment Stock, and Economics are important driving factors. SAE Modeling Framework integrates the key elements. 2009, Itron Inc. 64

Forecast Driver Summary Forecast Horizon Operational Financial Capacity Planning (Short Term) (Medium Term) (Long Term) Calendar Solar NA Weather Equipment Stock NA Economics / Price NA DSM NA Photovoltaics DR NA Level Adjustment 2009, Itron Inc. 65

Forecasts Must Answer Two Questions What s the Base Level? What s the Forecasted Growth Trajectory? 2009, Itron Inc. 69

2012 HANDS ON WORKSHOPS Forecasting Workshop February 8 9, Sydney Forecasting 101 April 18 20, Orlando Forecasting Workshop April 19 20, Brussels Energy Forecasting Week May 7 11, Las Vegas > Two One Day Workshops topics to be announced soon May 9 Fundamentals of MetrixND June 11 12, 12 Boston Questions? Press *6 to ask a question Fundamentals of Sales & Demand Forecasting September 20 21, Boston Fundamentals of Short Term and Hourly Forecasting October 11 12, San Diego Forecasting 101 November 7 9, San Diego OTHER FORECASTING MEETINGS Australian Users Meeting February 10, Sydney European Users Meeting April 25 27, Prague Energy Forecasting Week May 7 11, Las Vegas > Annual ISO/RTO Forecasting Summit May 7 8 > Long Term Forecasting/EFG Meeting May 10 11 Itron Users' Conference October 21 23, San Antonio For more information and registration: www.itron.com/forecastingworkshops Contact us at: 1.800.755.9585, 1.858.724.2620 or forecasting@itron.com 2009, Itron Inc. 70