COMPARISON OF PEAK FORECASTING METHODS. Stuart McMenamin David Simons
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1 COMPARISON OF PEAK FORECASTING METHODS Stuart McMenamin David Simons Itron Forecasting Brown Bag March 24, 2015
2 PLEASE REMEMBER» Phones are Muted: In order to help this session run smoothly, your phones are muted.» Full Screen Mode: To make the presentation portion of the screen larger, press the expand button on the toolbar. Press it again to return to regular window.» Feedback and Questions: If you want to give feedback to the presenter during the meeting or if you have a question, please type your question in the Q&A box. We will address it as soon as we can.» Recording will be posted online. We will send you a link to download the recording and pdf.
3 Workshop, Meeting, Webinar Schedule
4 Survey Schedule» Accuracy Benchmarking Survey Currently in the field 2014 variance from forecast 2014 variance after weather adjustment Current sales and peak forecasts for 2015 and beyond If you have not been invited, please Industry Practices Survey on Peak Forecasting Methods Planned for April Data frequency used Explanatory variables used Modeling approach and normalization methods
5 Agenda for Today» Peak Data and Economic Drivers» Weather Data Daily Max, Min, Average Temperatures Humidity, Wind, Clouds» Exploratory Analysis Using neural networks to see what matters» Structuring Regression Models Weather splines Weather indexes and interactions» Modeling Approaches and Results Modeling subsets of the data In sample subset statistics Out of sample statistics» Conclusions
6 Does your Company Develop Forecasts of Peak Demand? Yes No
7 PEAK DATA AND ECONOMIC DRIVERS
8 Peak Data and Economic Drivers» Start with hourly data Find daily peaks, monthly peaks, annual peaks» Economic Data Alternative scale drivers Combining multiple drivers into an index Understanding how models see the data» Modeling strategy Detecting structural change
9 Daily Energy and Peaks 1998 to 2014 Daily Energy (GWh) 365 Day Moving Average Weather Normal Energy Daily Peaks (MW)
10 Daily Peaks 2009 to 2014 Peak = 4,223 Peak = 4361 Peak = 4727 Peak = 4697 Peak = 4435 Peak = 3924
11 What is the Main Economic Variable in your Peak Model?
12 Economic Drivers Total Respondents = 64 Survey conducted by Itron for PJM in 2010 Please approximate the relative importance of each economic driver on the total system sales forecast using weights that sum to 100%
13 Economic Variables and Index Variable Orange line is an economic index variable constructed using weights from the survey. GDP HHInc GMP Households Emp Manuf Economic Index Emp NonManuf
14 Energy vs. Economic Index 2000 to 2007 Dec, 2007 Jan, Recession
15 Energy vs. Economic Index Add 2008
16 Energy vs. Economic Index Add 2009
17 Energy vs. Economic Index Add 2010
18 Energy vs. Economic Index Add 2011
19 Energy vs. Economic Index Add 2012
20 Energy vs. Economic Index Add 2013
21 Energy vs. Economic Index Add 2014
22 Economic Variables and Index Variable This does not mean that a stronger economy means less energy use. It means that something else is in play.
23 The Slowdown is Widespread Historical Linear Growth through 2008 Annual Gain = 62 TWh/year Survey Says: Annual Gain = 30 TWh/year About 0.65% Growth Results from the 2014 accuracy benchmarking survey
24 WEATHER DATA
25 Daily Peaks and Weather Daily Peak Daily Max, Avg, Min Temperature
26 Daily Energy vs. Avg Temperature 1998 to 2014
27 Daily Peak vs. Avg Temperature 1998 to 2014 Daily peak weather response looks like daily energy response.
28 What Weather Variables do you use in your Peak Model?
29 EXPLORATORY ANALYSIS USING NEURAL NETWORK MODELS
30 Exploratory Analysis using Neural Networks» Neural networks are flexible nonlinear models» They work well for energy and load data» One linear node Day of week, month, holidays, economic index» Two nonlinear nodes Average temperature Max temperature Min temperature Humidity Lagged avg temperature Wind speed Cloud cover WkEnd Binary Swing Month Binaries
31 Neural Network Derivatives Daily Peaks dpeak/ davgdb Peak vs. AvgDB dpeak/davgdb vs. AvgDB
32 Neural Network Derivatives (dpeak/dx) MW/deg 37 MW/deg AvgDB MaxDB -12 MW/deg MinDB Humidity 9 MW/%
33 Neural Network Derivatives Lag AvgDB Lag2 AvgDB 16 MW/deg 7 MW/deg DayTime Clouds WindSpeed.7 MW/% 7.3 MW/MPH -1.6 MW/% -6.5 MW/MPH
34 STRUCTURING A REGRESSION MODEL
35 Modeling Issues Addressed» Optimized Temperature/Humidity Index (THI)» Building Temperature splines for Regression models THI and Max temperature CDD and HDD» Building Temperature Indexes for Regression models THI CDD Spline HDD Spline Max CDD Spline, Max HDD Spline Min CDD, Min HDD Lag CDD, Lag HDD Lag2 CDD, Lag2 HDD» Interacting Temperature Indexes for slope shifts Swing month binary variables Weekend binary variables Wind and Cloud Cover variables
36 Including Humidity» Optimize THI for modeling daily peaks T + a (Hum -.5) (Max(T-b,0)) Standard parameters - a =.55 - b = 58 Estimate neural network models with THI in nonlinear nodes Optimal parameters for peak - a =.30 - b = 59 (not strongly defined) Standard parameter (a =.55) is worse than temperature only (a =.00) - Not much benefit to using THI if you don t optimize the parameters
37 THI Splines» Estimate model with multiple TDD» Use estimated coefficients to calculate relative power» Construct spline variable Daily Peak vs THI Daily Peak vs THISpline
38 HDD Splines» Estimate model with multiple HDD» Use estimated coefficients to calculate relative power» Construct spline variable Daily Peak vs THI Daily Peak vs HDDSpline
39 Max Temperature Splines» Estimate model with multiple MaxCDD» Use estimated coefficients to calculate relative power» Construct spline variable Daily Peak vs MaxDB Daily Peak vs MaxDDSpline
40 Building CDD and HDD Indexes» Indexes combine Average, Max, Min, and Lag Temperature variables HDD Index CDD Index Slope Coefficients are similar to derivatives from Neural Network Models
41 Interacting Indexes for Slope Shifts» CDD and HDD Index variables can then be interacted with binary variables (for month slope shifts) and other weather variables (wind and clouds)
42 In Sample Estimation Results» Start with THI Spline, HDD Spline» Add Max CDD, HDD Splines» Add Min CDD55, HDD45» Add Lag CDD65, LagHDD50» Add Lag2 CDD65, Lag2HDD50» Construct CDD and HDD Index» Add Index * Weekend, SwingMonth» Add CDD, HDD Index * Wind» Add CDD, HDD Index * Clouds
43 Estimated Coefficients HDD Index Coefficients CDD Index Coefficients
44 MODELING APPROACHES AND RESULTS
45 Topics» Data subset options All days, weekdays only, peak season only Top load days, hottest days only, coldest days only» Modeling Approaches Used Hourly Neural Network (using hourly temperature) Daily Neural Network Daily Regression Daily Regression with AR Quantile» Evaluation of Models In Sample ( ) and Out-of-Sample ( ) statistics All days, monthly peak days, annual peak days
46 Subset Data 1999 to 2014 All Days, All Months (365 days per year) Monthly Peaks (1 value each month) Top 10 Seasonal (10 values from each summer) (10 values from each winter) Top 3 Monthly (3 values each month)
47 What Subset of Days are used to Estimate your Model?
48 Test Statistics All Days
49 Test Statistics Summer Month Peaks In Sample 1998 to 2012 Out of Sample 2013 and 2014
50 Test Statistics Annual Peaks In Sample 1998 to 2012 Out of Sample 2013 and 2014
51 Conclusions» The response to the economy has changed since the recession. This needs further study Good candidate for SAE content» No advantage to hourly modeling Models of daily peaks work a bit better» Regression models using all days require structured variables Spline variables for main temperature drivers Indexed variables for interactions (slope shifts) Regression, RegAR, and Quantile (50%) work about the same» Subset models (Top N days) can also work well with structured variables. Estimate weights with extra days or all days to get the nonlinearities and interactions right
52 Questions? Press *6 to ask a question SURVEYS» Accuracy Benchmarking currently in the field» Industry Practice on Peak Forecasting Methods April launch 2015 HANDS-ON DOMESTIC WORKSHOPS» One-Day Modeling Workshop Introduction to SAE Baltimore, MD; May 5» One-Day Modeling Workshop Weather Normalization Baltimore, MD; May 5» Fundamentals of Long-Term Forecasting for Planning Applications Chicago, IL; Sept 29-30» Fundamentals of Short-Term Operational Forecasting San Diego, CA; Sept 29-30» Fundamentals of Budget Sales & Revenue Forecasting San Diego, CA ; Nov 4-5 OTHER DOMESTIC FORECASTING MEETINGS» 13 th Annual Energy Forecasting Training and Meeting Baltimore, MD; May 6-7» 9 th Annual ISO/RTO/TSO Forecasting Summit Albany, NY; May 12-14» Itron Utility Week Los Angeles, CA; October forecasting@itron.com
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