Monthly Sales Weather Normalization and Estimating Unbilled Sales. Al Bass Kansas City Power & Light EFG Meeting Las Vegas, NV April 2-3, 2014
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1 Monthly Sales Weather Normalization and Estimating Unbilled Sales Al Bass Kansas City Power & Light EFG Meeting Las Vegas, NV April 2-3, 2014
2 Project Objective To develop the ability to more accurately explain monthly sales variance by month, class, and jurisdiction The initial project objective was to develop a set of models for calculating calendar-month weather normalization factors. These factors were to then be applied to Accounting s estimate of class calendar-month sales estimates. But other useful class sales estimates fall out of the calculation process: Direct estimation of unbilled sales Direct estimation of actual calendar-month sales Weather normal billed sales 2
3 The Problem Management requested the ability to generate monthly weather normal class sales estimates for financial reporting. At the time we were estimating quarterly weather-normal sales as the reported calendar-month sales from accounting was rather noisy. A set of weather response models were estimated to develop monthly revenue class adjustment factors Cal Factor mc = Cal_WNAvgUse mc / Cal_AvgUse mc But when the calendar weather adjustment factors were applied you still got noisy monthly sales estimates. If you start with garbage all you get is weather-normalized garbage. The problem was that the method used for calculating calendar and unbilled sales did not adequately account for weather conditions over the billing, calendar month, or unbilled period. 3
4 The Solution Implemented an application with MetrixND and MetrixLT that entailed Calculating billing-month, calendar-month, and unbilled period weather conditions, Estimating monthly revenue class weather-response functions from billed sales and customer data Using the models to calculate actual and weather normal calendar-month and unbilled class sales The biggest challenge is convincing Management (Accounting/Finance in particular) that this is the right way to go. 4
5 Proposed vs. Current Methodology How the new method differs from the current method Current New Approach Top Down Bottom Up Starting point NSI Billed KWh Sales Calculate new unbilled each month Yes Yes Reverse out old unbilled Yes Yes Calculation of unbilled System Calculated at each class Allocation of unbilled Billed KWh Sales Calculated at each class Account for billing cycles Yes Yes Difference between prior month KWh unbilled estimate and recalculated KWh unbilled estimate flows through line loss trueup Yes No Carry prior month unbilled forward Indirectly No Account for actual weather conditions (CDD/HDD) during the unbilled period Account for actual weather conditions (CDD/HDD) in calculating accrued sales No No Yes Yes 5
6 Proposed vs. Current Methodology Cont. How the new method differs from the current method Current New Account for actual weather in allocating billed sales between months No N/A NSI/SPP used in calculation Yes Not needed, only for calibration Jurisdictional/class average Use Per Day Regression Model N/A Based on 9 Yrs. of history Approach design Annual / System Monthly / Class 6
7 Process Map Modeling Process 7
8 Methodology The estimation approach entails using a set of rate class weather response models to predict sales over different periods of time: Predicted sales over the calendar month period for actual and normal weather conditions Predicted sales over the unbilled period for actual and normal weather conditions Predicted sales over the billing period for normal weather conditions Based on factors that drive usage over the unbilled period Billing Cycles Number of Unbilled Days in the month Day of the week, weekend days, holidays, and hours of light Determine the number of unbilled CDD or HDD 8
9 Weather Response Simulated use and predicted bill use are derived using a set of monthly weather response functions estimated from historical billing data Average monthly use vs. Average temperature Residential Commercial Large Commercial Industrial 9
10 Estimated Residential Model An example of the residential model and predicted outcome to historical sales Variable Coefficient StdErr T Stat P Value CONST % HDD % HDD % TDD % TDD % Sep % Feb % Yr % Sept % Jun % JunToSep % Oct09ToJun % Model Results 10
11 KWh Sales Class Model Variables Regression model variables and weather KCPL_KS ResAvgUsePD CSAvgUsePD POSalesPD MPSalesPD MOSalesPD SFRSalesPD LoadPD CONST CONST CONST CONST CONST CONST CONST mwthrrevpd.hdd60 mwthrrevpd.hdd50 mwthrrevpd.hdd55 mwthrrevpd.cdd60 mwthrrevpd.cdd60 mwthrrevpd.cdd65 mwthrcalpd.hdd55 mwthrrevpd.cdd60 mwthrrevpd.cdd60 mwthrrevpd.cdd60 mbin.trendvar mbin.trendvar mbin.trendvar mwthrcalpd.cdd70 mwthrrevpd.cdd70 mbin.yr07 mbin.yr07 mbin.feb mbin.may mbin.mar mwthrcalpd.cdd60 mbin.mar mbin.yr08 mbin.yr10 mbin.mar mbin.jan mbin.apr mbin.beforemay09 mbin.sep mbin.jul10 mbin.oct mbin.apr mbin.mar mbin.jul mbin.mar12 mbin.dec mbin.jun13 mbin.mar13 mbin.may mbin.dec mbin.aug mbin.wkendcaldayspd mbin.jan07 mbin.aftermar11 mbin.apr13 mbin.dec mbin.aug10 mbin.sep AR(1) mbin.juntosep10 mbin.may13 mbin.sep07 mbin.afterjan10 mbin.oct mbin.oct09tojun10 mbin.trendvar AR(1) mbin.afterapr12 mbin.nov mbin.trendvar mbin.afterapr12 AR(1) KCPL_MO ResAvgUsePD CSAvgUsePD POSalesPD MPSalesPD MOSalesPD SFRSalesPD LoadPD CONST CONST CONST CONST CONST CONST CONST mwthrrevpd.hdd45 mwthrrevpd.hdd55 mwthrrevpd.hdd55 mwthrrevpd.cdd60 mwthrrevpd.cdd60 mwthrrevpd.hdd45 mwthrcalpd.hdd55 mwthrrevpd.hdd55 mwthrrevpd.cdd60 mwthrrevpd.cdd60 mbin.trendvar mwthrrevpd.hdd45 mbin.trendvar mwthrcalpd.cdd60 mwthrrevpd.cdd60 mwthrrevpd.cdd70 mbin.trendvar mbin.mar mbin.jan mbin.jun mwthrcalpd.cdd70 mwthrrevpd.cdd70 mbin.feb mbin.may mbin.may mbin.mar mbin.jul mbin.trendvar mbin.sep mbin.mar mbin.oct mbin.aug mbin.afterapr12 mbin.aug mbin.calmay mbin.feb mbin.sep mbin.yr10 mbin.sep mbin.jun13 mbin.mar08tojul09 mbin.jun07may09 mbin.yr06 mbin.oct mbin.jul10 mbin.dec mbin.nov09 mbin.jul07 AR(1) mbin.sep09 mbin.yr06 mbin.jul07tomay10 mbin.may09 mbin.aug09 mbin.jun13 mbin.juntosep10 mbin.jan08 mbin.feb09 mbin.juntosep10 mbin.nov12tojan13 mbin.oct09tojun10 mbin.jun13 GMO_MPS ResAvgUsePD ComAvgUsePD INDSalesPD PubAuthSalesPD SFRSalesPD LoadPD CONST mwthrrevpd.hdd55 CONST CONST CONST CONST mwthrrevpd.hdd55 mwthrrevpd.cdd60 mwthrrevpd.cdd65 mwthrrevpd.hdd55 mwthrrevpd.hdd60 mwthrcalpd.hdd50 mwthrrevpd.cdd60 mbin.trendvar mbin.jan mwthrrevpd.cdd60 mwthrrevpd.cdd60 mwthrcalpd.hdd60 mwthrrevpd.cdd70 mbin.jun mbin.feb mbin.jan10 mbin.feb mwthrcalpd.cdd60 mbin.sep mbin.dec mbin.may mbin.feb10 mbin.mar mwthrcalpd.cdd70 mbin.oct10 mbin.beforejul07 mbin.nov mbin.jul07 mbin.jun mbin.afterjune09 mbin.aug12 mbin.aug06oct07 mbin.jul06 mbin.aug07 mbin.jul AR(1) mbin.may10tojan11 mbin.jan10 mbin.yr13 mbin.sep mbin.aftermay11 mbin.feb10 mbin.nov mbin.yr09 mbin.may09 mbin.beforenov08 mbin.jun09 GMO_SJLP ResAvgUsePD ComAvgUsePD INDSalesPD LoadPD CONST CONST CONST CONST mwthrrevpd.hdd55 mwthrrevpd.hdd55 mwthrrevpd.cdd65 mwthrcalpd.hdd50 mwthrrevpd.cdd60 mwthrrevpd.cdd60 mbin.trendvar mwthrcalpd.hdd60 mwthrrevpd.cdd70 mbin.feb mbin.jan mwthrcalpd.cdd60 mbin.mar mbin.aug mbin.feb mwthrcalpd.cdd70 mbin.apr mbin.dec mbin.nov mbin.caldec mbin.sep mbin.nov10tojul11 mbin.apr06 mbin.sep11 mbin.dec07 AR(1) mbin.sep07 mbin.afterapr10 mbin.nov09tomay10 mbin.oct07 mbin.jun07may09 mbin.may10toapt11 mbin.jun12 mbin.afterapr12 mbin.yr08 11
12 Calculated Cooling Degree-Day CDD and HDD are derived for actual and normal weather conditions Calculated CDD Actual Calculated CDD Normal Calendar Month Billing Month Calendar Month Billing Month Unbilled Period Unbilled Period 12
13 Constructing Unbilled Corner CDD Daily Average Temperature Number of unbilled days = 16.6 Number of unbilled CDD =
14 Estimating Class Calendar (Accrued) Sales Estimate weather response models to simulate usage for the calendar month Billing days Calendar Days RevMoHDD Calendar-month HDD RevMoCDD Calendar-month CDD Residential Model Example Calendar month average use Billing month average use 14
15 Estimating Class Unbilled Sales Estimate weather response models to simulate usage for the unbilled period Billing days Unbilled Days RevMoHDD Unbilled HDD RevMoCDD Unbilled CDD Residential Model Example billing month average use predicted unbilled average use 15
16 Model Results How the model results are used to estimate weather normal class sales Estimates are derived by applying monthly adjustment factors to billed sales WNBillSales = (Simulated WN Bill Use / Predicted Bill Use) * BillSales CalendarSales = (Simulated Cal Use / Predicted Bill Use) * BillSales WNCalSales = (Simulate WN Cal Use) / Predicted Bill Use) * BillSales UBSales = (Simulated Unbill Use / Predicted Bill Use) * BillSales WNUBSales = (Simulated WNUnbill Use/ Predicted Bill Use) * BillSales 16
17 Residential Sales Estimates billing month calendar month unbilled 17
18 Comparison Against NSI NSI Total Estimate Commercial Residential Industrial 18
19 Key Findings Class results have larger variance due to allocating across class and months vs. retail which has a smaller variance Differences between the Current and New Method are smallest at on annual basis, and largest on a monthly basis Annual little variance (<0.3%) Quarterly more variance (<1.0%) Monthly largest variance (>1.0%) Class results have a larger variance than total retail Allocating across classes and month Unbilled results had the most volatility Difference in methodology calculation of unbilled 19
20 Variance Between New and Current Method Jurisdiction & class differences KCPL Annual Quarterly Monthly Class Avg '12-'13 Range Avg '13 Range Avg '13 Range RES 0.1% [0.4%, 0.7%] 0.2% [5.2%, 5.9%] 0.7% [10.3%, 11.3%] Accrued COM 0.1% [0.1%, 0.1%] 0.2% [3.5%, 2.5%] 0.1% [5.4%, 6.3%] IND 0.1% [0.1%, 0.1%] 0.0% [4.4%, 4.3%] 0.6% [13.2%, 19.7%] WN Accrued RET 0.0% [0.3%, 0.2%] 0.2% [1.7%, 0.8%] 0.2% [2.7%, 1.3%] RES 0.3% [1.0%, 1.7%] 0.3% [5.3%, 6.0%] - COM 0.5% [0.9%, 0.0%] 0.1% [3.3%, 2.8%] - IND 0.2% [0.2%, 0.1%] 0.0% [4.5%, 4.4%] - RET 0.1% [0.4%, 0.1%] 0.4% [2.0%, 0.9%] - RES 2.2% [1.4%, 2.9%] 1.9% [2.5%, 9.5%] 1.4% [11.7%, 20.4%] Unbilled COM 0.7% [0.1%, 1.3%] 0.1% [3.2%, 2.0%] 0.3% [6.4%, 9.6%] IND 0.2% [0.2%, 0.7%] 0.0% [4.1%, 4.2%] 0.9% [16.0%, 14.8%] RET 1.2% [0.5%, 1.8%] 0.6% [1.3%, 3.0%] 0.6% [2.3%, 4.5%] 20
21 Conclusion The new method is theoretically strong Differences between billed, calendar (accrued), and unbilled sales is a result of a different defined time periods and the weather conditions within these defined periods Results show that the new method generates reasonable estimates of calendar month sales (as the sum of the calendar-month sales estimates is close to delivered energy estimates) and so by default generates reasonable estimates of unbilled sales If you calculate reasonable calendar-month sales estimates you can then calculate reasonable estimates of weathernormal calendar-month sales. Now if we can only convince Management (they hate change) 21
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