Weather Normalization

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Weather Normalization

John Penry, CFC

Conducting an Analysis

• What questions are trying to get answered?

• Collecting your data

• Understanding the analytical tools available

• Using the results

What questions are you trying to answer?

• Are you trying to forecast future loads/demand?

– Different customer classes have different “baseload” demand

– Have different sensitivities to weather

• Are you trying to explain variations in kwh to forecast?

– Understand your forecast and historical data

– Did you forecast ranges of consumption?

• Are you getting a better understanding of customer trends?

– Has the “baseload” demand changed?

– Are your customers becoming more efficient?

– Cost of Service Study implications

Data Collection

• Know Thy Data- the missing 11th commandment

• Load Data

– Where is the data measured?

• Busbar, lowside meter, are you accounting for losses

– What is the interval of measurement?

• Is your data contemporaneous?

– Is your data consistent across classes?

• Weather Data

– Where is the data measured?

– What is HDD, CDD, and GDD

• What is the region’s base temperature

• Sources for weather data

http://climate.usurf.usu.edu/products/data.php

http://www.weatherdatadepot.com/

Calculating HDD and CDD

• The Averaging Method

CD= ((Tmax +Tmin)/2) - 65

HD= 65 - (Tmax +Tmin)/2)

CD= ((82 + 61)/2) – 65 = 71.5 – 65 = 6.5 CD

• Hourly Average Integration

CD= (Sum of all temps/# of readings) - 65

HD= 65 - (Sum of all temps/# of readings)

• Sum the CD or HD across the month or cycle to get CDD and HDD

• Problems to Consider

– Defining Average Temp, Base Temp, Other factors (wind, humidity…)

• TMY- NOAA 30-year normal weather

Spot Check your Data

• Graph it out and see if something sticks out

– Weather data is problematic

– Did the temps really reach 9999F?

– Why is the curve flat?

• Use a second source to get the correct data

• Smoothing with averages

• Sum rows or columns to check for completeness

– 8760 hours per year, 8784 in a leap year

– Months with 31 days have 744 hours

– Months with 30 days have 720 hours

– February has 672/696 hours

Actual Energy and Weather – It’s Hot in Texas

Actual Energy and Weather

August 2009 – Loads & Temps

Determine the Variables

• We have an independent variable (IV) that is the weather

• We have a dependent variable (DV) that is our load

– In order to analyze the impact our IV has on our DV, we must

focus on each one in isolation, then together.

– What other ways can we study the variables.

August 2009 – Loads

Load Duration Curve for August

Load Duration Curves by Month for 2009

Daily Weather for 2009

Maximum, Minimum, and Average Temperatures

Temperature Duration Curve

Maximum, Minimum, and Average Temperatures each sorted separately

August 2009 Load Duration Curve with corresponding Temperatures in F

December 2009 Load Duration Curve

December 2009 Load Duration Curve with corresponding Temperatures in F

Regression Analysis

• A simple linear regression analysis is an attempt to create a formula that uses the independent variable (IV) to predict the

expected outcome of a dependent variable (DV).

• CDD and HDD are our IV

• Consumption or load is our DV

• Multiple Regression would take into consideration the affects of humidity, wind, precipitation… other factors

• An R2 (Coefficient of determination) value of .75 or greater is generally accepted as correlated in the Electric Utility Industry.

• In MS Excel, you must download the analysis tool-pack to do Regression Analysis

What is the optimal temperature to use?

MONTH KWh

Integrated Average

Temperature in F

Max Temperature

in F

Min Temperature

in F Simple

Average 55 58 60 63 65

8/1/2009 4,849,034 59.525 69.8 46.4 58.1 4.525 1.525 0 0 0

8/2/2009 5,229,503 64.625 80.6 53.6 67.1 9.625 6.625 4.625 2.125 0

8/3/2009 6,358,255 71.225 80.6 62.6 71.6 16.225 13.225 11.225 8.725 6.225

8/4/2009 6,137,910 68.75 75.2 62.6 68.9 13.75 10.75 8.75 6.25 3.75

8/26/2009 5,625,920 62.825 73.4 51.8 62.6 7.825 4.825 2.825 0.325 0

8/27/2009 5,648,806 63.35 75.2 51.8 63.5 8.35 5.35 3.35 0.85 0

8/28/2009 5,311,553 62.75 75.2 53.6 64.4 7.75 4.75 2.75 0.25 0

8/29/2009 4,480,163 57.2 66.2 48.2 57.2 2.2 0 0 0 0

8/30/2009 4,388,582 54.275 64.4 42.8 53.6 0 0 0 0 0

8/31/2009 4,856,667 54.275 68 41 54.5 0 0 0 0 0

0.84423 0.850016 0.828441 0.746282 0.647958

In Excel =RSQ($C$3:$C$33,J3:J33)

Correlation between the IV and DV

• Using excel, determine the proper temperature for you base study

– The closer the value is to 1, the more highly correlated the data

• Chart and show trend line

• Caution: Correlation is not necessarily causation!

August CDD

•Y = 140,222x + 4,679,287•Y is the predicted load, x is our HDD/CDD, and

4,679,287 is thought to be the base load.

•R2 = 0.85 so the data is highly correlated

December HDD

•Y = 16,829x + 4,757,887•Y is the predicted load, x is our HDD/CDD, and

4,757,887 is thought to be the base load.

•R2 = 0.47 so the data is not very well correlated

Why might the results work for August but not for December?

• What is the mix of consumers in your area?

• What type of heating/cooling do they have?

• Are the members seasonals?

• Can you segregate the customer classes?

• It goes back to your data: gigo

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