UNBILLED ESTIMATION. UNBILLED REVENUE is revenue which had been recognized but which has not been billed to the purchaser.
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1 UNBILLED ESTIMATION UNBILLED REVENUE is revenue which had been recognized but which has not been billed to the purchaser. Presented By: Laura Ortega Sr. Manager Demand Side Analytics, CPS Energy & Andy Sukenik Principal Forecast Consultant, Itron Courtesy of Andy Sukenik, Itron 1
2 Overview Fundamentals of Unbilled Estimation 2 Daily Unbilled Sales Estimation Approaches Daily AMI Approach Concept Daily Load Research Approach Concept 1 Monthly Unbilled Sales Estimation Approach Monthly Modeling Approach Concept 2
3 Unbilled Estimation Daily AMI Approach Present Daily Load Research Approach None Monthly Financial Approach Monthly Modeling Approach Development Test Verification Implemented Development Test Verification Implemented Development Test Verification Implemented KEY: = completed = in progress = not started 3
4 Fundamentals of Unbilled Estimation 4
5 Unbilled Estimation Unbilled Revenue ($$) Unbilled Sales (KWH, CCF) Accurate Unbilled Sales Estimation # of Unbilled Days Weather Impacts 5
6 June 17 Billing Cycle Geometry District Read Day May 1-31 June 1-30 May Unbilled (m-1) A District Read Day June Unbilled (m) C We are here July 3 B June Sales Package = energy delivered depicting the billing month green parallelogram (A+B) June Unbilled (m) = energy delivered in the unbilled triangle grey triangle (C) May Unbilled (m-1) = prior month unbilled May green triangle (A) June Calendar Month Sales = energy delivered in month of June calendar rectangle (B+C) 6
7 Daily AMI Approach 7
8 Daily AMI Approach Step 1: Extract Daily AMI data for the population by class (e.g. Residential) and compute the Daily Class Average Use per Installation (UPI): Step 2: Determine a District Unbilled Ratio District Unbilled Ratio District Unbilled UPI Profile Sum District Billed UPI Profile Sum - Districts read late in the month will have relative low Unbilled District Ratio. - Districts read early in the month will have relatively high Unbilled District Ratio. Step 3: Estimate the District Unbilled Sales District Unbilled Sales District Unbilled UPI Profile Sum District Billed UPI Profile Sum District Billed Sales 8
9 Daily UPI (KWh) Residential AMI Daily UPI May 17 June 17 9
10 Daily UPI (KWh) Residential AMI UPI Profile Applied to Districts May Unbilled (m-1) June Unbilled (m) District Read Day A District Read Day B C Focus on District 2 10
11 District 2 Unbilled Sales Calculation A B C Residential Daily AMI UPI Profile District 2 Billed Period Start = May 12 Stop = June 13 Billed UPI Profile Sum = 1399 KWh District 2 Unbilled Period Start = June 14 Stop = June 30 Unbilled UPI Profile Sum = 939 KWh June District 2 Unbilled Ratio = Unbilled UPI Profile Sum / Billed UPI Profile Sum = 939 / 1399 = 67% June District 2 Unbilled Sales = 67 % x District 2 Billed Sales = 67% x 31 GWh = 21 GWh Res Billed Sales by District District Res Billed sales (GWH)
12 Daily UPI (KWh) Residential Unbilled Sales Calculation All Districts Res Billed Sales by District District Res Billed sales (GWH) Billing Period Profile Sum Unbilled Period Profile Sum 12
13 Daily AMI Approach Residential Unbilled Sales Estimation Results District Unbilled Period Sum Billing Period Sum Unbilled Ratio Billed Sales (GWh) Unbilled Sales (GWh) % % % % % % % % % % % % % % % % % % % % June Monthly Close Unbilled Sales = 560 GWH 13
14 Daily AMI Approach Summary Leverages AMI Data Improved Accuracy # of Unbilled Days Will account for the Actual # of Unbilled Days Weather Impacts Actual weather and its actual impact on consumption Defendable Easy to explain Sum & Ratio 14
15 Daily Load Research Approach 15
16 Daily Load Research Approach The Daily Load Research Approach is identical to the Daily AMI Approach, however: Daily Class Average Use per Installation (UPI) Profile Replaced with a Daily Class Load Research Profile Model Backcast based on Actual Weather The Daily Class Load Research Profile Model estimates UPI based on the following: Annual Binaries (approximating for growth) Calendar Conditions (DOW, Holidays, Month) Actual Weather 16
17 Daily Weather Input Calculations Begin with 24-Hour Temperatures by Station Stinson, Randolph, Kelly, Airport Compute an Average Daily Temperatures by Station Compute multiple CDDs & HDDs by Station CDD 55, 60, 65, 70, 75, 80 HDD 65, 60, 55, 50, 45, 40, 35 Compute 1 CPS Daily Degree Days per Weighted Average for each cutpoint Stinson KSSF (50% Wgt) Randolph KRND (25% Wgt) Kelly KSKF (25% Wgt) Airport KSAT(0% Wgt) 17
18 Daily Weather Input Calculation Example Example: May 21, 2017 for CDD 65 cutpoint KSSF KRND KSKF KSAT Max ( Deg F CDD 65 = CDD, 0 ) % 25% 25% 0% 8 HR KSSF KRND KSKF KSAT Avg
19 Daily UPI (KWh) Residential Use vs Temperature Weekdays Saturdays Sundays Holidays Daily Temperature (Deg F) 19
20 Daily UPC UPI (KWh) Residential Use vs CDD & HDD 65 Res Load Research Data Single CDD and HDD cut points overestimate at low powered degrees (close to 65) and underestimate at high powered degrees (extreme heat and cold). HDD65 d CDD65 d Daily Temperature (Deg F) 20
21 Daily Daily UPI UPC (KWh) (KWh) Res Weather Response Functions Res Load Research Data Multi-part slopes can be estimated from load research data for each class. These slopes summarize the relative impact of low powered and high powered degrees. HDDSpline.10 HDD65.24 HDD60.25 HDD55.17 HDD50.24 HDD40 d d d d d d CDDSpline.34 CDD65.14 CDD70.28 CDD75.24 CDD80.25 CDD85 d d d d d d Daily Temperature (Deg F) 21
22 Res UPI (KWh) Daily Residential Load Research Profile Model Results Load Research Data FY 17 Model Stat R-Squared Adjusted R-Squared Mean Abs. Dev. (MAD) 1.14 kwh/day 0 Mean Abs. % Error (MAPE) 3.35% 0 Perfect Stat 22
23 Res UPI (KWh) Residential Daily Load Research Profile Model Backcast» Daily Class Profile Model Backcast estimation requires Actual Daily Weather Data» It does NOT require Actual Load Research Data. Load Research Data FY 17 Backcast Through June 17 23
24 Residential Load Research Daily UPI May 17 June 17 24
25 Daily UPI (KWh) Residential Load Research UPI Profile Applied to Districts 25
26 Daily UPI (KWh) Residential Load Research UPI Profile Applied to Districts May Unbilled (m-1) June Unbilled (m) District Read Day A District Read Day B C Focus on District 2 26
27 District 2 Unbilled Sales Calculation A B C District 2 Billing Period Start = May 12 Stop = June 13 District 2 Unbilled Period Start = June 14 Stop = June 30 Residential Daily LR UPI Profile Billing UPI Profile Sum = 1400 KWh Unbilled UPI Profile Sum = 941 KWh June District 2 Unbilled Ratio = Unbilled UPI Profile Sum / Billing UPI Period Sum = 941 / 1400 = 67% June District 2 Unbilled Sales = 67 % x District 2 Billed Sales = 67% x 31 GWh = 21 GWh Res Billed Sales by District District Res Billed sales (GWH)
28 Daily UPI (KWh) Residential Unbilled Sales Calculation All Districts Res Billed Sales by District District Res Billed sales (GWH) Billing Period Profile Sum Unbilled Period Profile Sum 28
29 Daily Load Research Approach Residential Unbilled Sales Estimation Results District Unbilled Period Sum Billing Period Sum Unbilled Ratio Billed Sales (GWh) Unbilled Sales (GWh) % % % % % % % % % % % % % % % % % % % % June Monthly Close Unbilled Sales = 562 GWH 29
30 Daily Load Research Approach Summary Leverages AMI Data The Load Research Profile utilizes some AMI sampling It can be used to validate the AMI method Improved Accuracy # of Unbilled Days Will account for the Actual # of Unbilled Days Weather Impacts Actual weather and a modeled impact on consumption Defendable Easy to explain Sum & Ratio 30
31 Monthly Model 31
32 Residential Unbilled & Calendar Sales Calculations ResCalendarSales m ResSalesPackage m ResUnbilledSales m ResUnbilledSales m 1 Current Month Sales A & B Actual Sales Package Data Used in Calendar Sales Calculation Used to build and fit the Monthly Model Current Month Unbilled C Prior Month Unbilled A Monthly Model will give a predicted value for all these 3 pieces 32
33 Monthly Model What are we modeling? Use / Bill / Day (UPBD) for the Billing Period: Use / Bill / Day (UPBD): Source Use: Class Level Sales Data Sales Package Bill: Class Level Number of Bills Data Sales Package Day: Average Billing Days Calculation Why? Level at which we have class data Establishes weather relationships based on monthly sales package data These relationships will be leveraged to estimate the unbilled period 33
34 June 17 Billing Days Billing Period District 2 May 12th June 13th = 33 Days Monthly Average Average Billing Period Days = 32.2 Days May Unbilled (m-1) June Unbilled (m) Cycle Read Day A Cycle Read Day C B 34
35 June 17 Billing & Unbilled Days Billing Period District 2 May 12th June 13th = 33 Days Monthly Average Average Billing Period Days = 32.2 Days Unbilled Period June 14th June 30 th = 17 Days Average Unbilled Period Days = 15.3 Days May Unbilled (m-1) June Unbilled (m) Cycle Read Day A Cycle Read Day C B 35
36 Monthly Model How do we predict it? Leverage existing Demand Side Analytics Forecasting Models Uses inputs: Economics & Price End Use Equipment Stock and Building Shell Characteristics Energy Efficiency Seasonality Weather 36
37 Daily Weather Input Calculation Example Example: May 21, 2017 for CDD 65 cutpoint KSSF KRND KSKF KSAT Max ( Deg F CDD 65 = CDD, 0 ) % 25% 25% 0% 8 HR KSSF KRND KSKF KSAT Avg
38 Daily DDs Applied to Districts - Billing Period May 21 CDD 65 = 8 Value is applied to all districts active on that Daily CDD 65 day. 38
39 Weather Variables - Billing Period Daily CDD 65 Total CDD 65 for all Billing Period Districts =448 39
40 Weather Variables Billing Period Daily CDD 65 June Cycle CDD = 448 Days = 32.2 CDD/Day Deg F 40
41 For a Billing Month UPBD (KWh) Res Weather Response Functions Each point is one Month CDD & HDD for each cutpoint HDDSpline.10 HDD65.24 HDD60.25 HDD55.17 HDD50.24 HDD40 m m m m m m CDDSpline.34 CDD65.14 CDD70.28 CDD75.24 CDD80.25 CDD85 m m m m m m Billing Month Average Temperature (Deg F) 41
42 Res UPBD (KWh) Residential UPBD Model Results Model Stat R-Squared Adjusted R-Squared Mean Abs. Dev. (MAD) 0.5 kwh/day 0 Mean Abs. % Error (MAPE) 1.38% 0 Perfect Stat 42
43 Weather Variables - Unbilled Period June Unbilled CDD = 279 Days = 15.3 CDD/Day Deg F Daily CDD 65 43
44 Monthly UPBD (KWh) Res Weather Response Functions Weather Response is a unique estimate of UPBD for the Billing Period and Unbilled Period June 17 Res Unbilled Period UPBD estimate accounts for the relatively severe weather in Unbilled Period (83 Deg F vs 79 Deg F) Billing Period Unbilled Period Billing Month Average Temperature (Deg F)
45 Residential Unbilled Period Model Simulations Build Monthly Model With Billing Info To get Unbilled Info June 17 Res Unbilled Period UPBD estimate accounts for the relatively severe weather in Unbilled Period ( 83 Deg F vs 79 Deg F). 45
46 Residential Unbilled & Calendar Sales Calculations ResUnbilledSales m ResUPBD _ UnbilledSimulation m UnbilledDays m ResBills m Monthly Model Calculated Per Sales Package ResCalendarSales m ResSalesPackage m ResUnbilledSales m ResUnbilledSales m 1 46
47 Commercial classes Commercial classes will be similar method Largest Accounts - calculated at individual customer level 47
48 Monthly Model Approach Summary Does not leverage AMI Data however it will validate the upcoming Load Research and AMI Approaches. Improved Accuracy 1. Usage Model Economics & Price End Use Equipment Stock and Building Shell Characteristics Energy Efficiency 2. Rigorous Weather Response Model Unique UPBD estimates for the Billing Period and Unbilled Period 3. Large Accounts at individual customer level 48
49 Thank You Laura Ortega Demand Side Analytics CPS Energy 145 Navarro St. San Antonio, Texas MD: Office: Mobile: cpsenergy.com
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