Abram Gross Yafeng Peng Jedidiah Shirey
Contents Context Problem Statement Method of Analysis Forecasting Model Way Forward Earned Value
NOVEC Background (1 of 2) Northern Virginia Electric Cooperative (NOVEC) is a distributor of energy to 6 counties in Northern Virginia. Nearly 150,000 customers. Mandated to meet all energy requests in pre-specified zones of obligation. Primary means to service communities is through bulk energy market purchases.
NOVEC Background (2 of 2) Energy Purchases: 1) Bulk purchases via contracts 1 month to 3 years prior to delivery. 2) Spot purchases as needed to meet peak demand up to one day prior to delivery.
Problem Statement Warming trends have caused NOVEC to question whether the current weather-normalization methodology is still the best available model. NOVEC needs a new weather-normalization method that accounts for changing weather trends or a recommendation that the existing model is sufficient. To what extent is the climate and its impact on energy demands changing?
Purpose & Objectives Purpose: NOVEC needs to remove weather-effects from energy consumption to more accurately inform bulk purchases. Explore and recommend a methodology to normalize monthly energy purchases. Objectives: Characterize the relationship between economic variables, weather, customer-base, and energy consumption. Develop a normalization procedure to remove weather effects from energy demand. Recommendation for improvement to current weather-normalization methodology.
Scope Data: NOVEC monthly energy purchases data since 1983. Dulles weather data since 1963. Historic economic factors data since 1980s. 30 years of forecasted economic factors. Model: Parameters: historic energy purchases, weather data, economics, and customer-base. Predictions for energy consumption over a 30-year horizon. Regression: characterize dynamics between parameters. Weather-normalization: remove seasonal weather impacts on NOVEC’s load. Forecast: facilitate testing of varied normalization methodologies. Ensure synergy with NOVEC’s existing suite of models (regression, weather-normalization, forecast).
Key Definitions CDD: Cooling Degree-Day = MAX(ActualDegree – UpperBound, 0) * Duration. HDD: Heating Degree-Day = MAX(LowerBound – ActualDegree, 0) * Duration. Neutral zone: Upper and lower bounds for temperatures that do not impact load. 80 70 CDD 60 50 HDD 40 30 t Actual_Temperature Upper_Bound Lower_Bound * Analysis will explore varied upper/lower bounds for temperature
Weather Data Visualization (1 of 2) Historic weather data was analyzed by average temperature per month to view trends. Average temperature trending upwards from 1960 to the present for each month (graph is for July).
Weather Data Visualization (2 of 2) Variance was also evaluated and no change is seen.
Essential Elements of Analysis & Measures of Effectiveness EEA 1: What is the rate of climate change? MoE 1.1: Seasonal trends and variability. MoE 1.2: Rate of climate change. EEA2: What econ- model best predicts NOVEC’s customer -base? MOE 2.1: Best subset of econometrics to gauge service demand. MOE 2.2: Goodness of fit test for selected model. EEA3: What impact does weather have on energy demand? MOE 3.1: Relationship between weather and load beyond base demand. MOE 3.2: Changes in climate compared to per capita consumption. MOE3.3: Goodness of fit test for selected model.
Methodology I. Relate model parameters II. Develop normalization methodology III. Forecast Model * Assess Model Monthly Demand Employment Economic Housing Stocks Per Capita Forecast Variables Basic Load Demand GDP Remaining Historic Power Demand Power Demand Customer Composite CDD Impact Combination Weather- Residential Customer-Base HDD Impact Normalization Commercial * Model forecast will be verified against 2012- 2013 power demand and compared to existing model’s accuracy.
EEA1: Rate of Climate Change Historic weather data was analyzed to determine trends: Seasonal fluctuations. Overall rate of change. Characterize uncertainty/variability. Heating Degree-Days - 1963 to 2011 Cooling Degree-Days - 1963 to 2011 6000 1600 6000 1600 1400 1400 5000 5000 Total HDD Total CDD 1200 1200 Degree-Days Above 65 °F Degree-Days Below 65 °F 4000 4000 1000 1000 3000 800 3000 800 Average HDD Average CDD 600 600 2000 2000 400 400 1000 1000 200 200 Max HDD Max CDD 0 0 0 0 1963 1965 1967 1969 1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 1963 1965 1967 1969 1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011
EEA2: Economics Model Model to associate economic factors to customer-base in under development. Residential Demand & Metro-D.C. Econometrics 80 140 Customers & Housring Stocks (Thousands) Housing Stocks 70 120 Demand (Millions - kWH) 60 100 50 Customers 80 40 60 30 40 Demand 20 20 10 0 0 Aug-89 Aug-90 Aug-91 Aug-92 Aug-93 Aug-94
EEA3: Impact of Weather Changes to per capita energy demand by customer type: residential and non-residential. Monthly Customer per Capita Demand (2005 - 2010) 16000 Non- Residential 14000 12000 Energy Consumption (kWhr) Residential 10000 8000 HDD 6000 4000 CDD 2000 0 Jan-05 Jan-06 Jan-07 Jan-08 Jan-09 Jan-10
Model Development Pre-processing: Excel Workbook with VBA Macros. Automated computations of HDD and CDD. Allows user- defined “neutral” boundaries for calculating. Ingests data as currently maintained by sponsor. Automated linear transformations for regression models. GUI design facilitates some simple regression models germane to VBA; limited capability. Launch and export conditioned data to R; expanded capability. Statistical Modeling in R: Executable file for regression analysis and improved visualization.
Climate change is more apparent in winter and summer than fall and spring. Variability in yearly weather patters doesn’t appear to change. Degree-Days Below 65 °F Degree-Days Below 65 °F 1000 1200 1400 1600 Preliminary Results 100 120 140 160 200 400 600 800 20 40 60 80 0 0 1963 1963 1963 1963 1963 1966 1966 1966 1966 1966 1966 Summer Heating Degree Days 1969 1969 1969 1969 1969 1969 Spring Heating Degree Days 1972 1972 1972 1972 1972 1972 1975 1975 1975 1975 1975 1975 1978 1978 1978 1978 1978 1981 1981 1981 1981 1981 1981 1984 1984 1984 1984 1984 1984 1987 1987 1987 1987 1987 1990 1990 1990 1990 1990 1990 1993 1993 1993 1993 1993 1993 1996 1996 1996 1996 1996 1996 1999 1999 1999 1999 1999 1999 2002 2002 2002 2002 2002 2005 2005 2005 2005 2005 2005 2008 2008 2008 2008 2008 2008 2011 2011 2011 2011 2011 MAY APR MAR AUG JUL JUN Degree-Days Exceeding 65 °F Degree-Days Exceeding 65 °F 100 150 200 250 300 350 1000 1200 1400 50 200 400 600 800 0 1963 0 1963 1963 1963 1963 1966 1966 1966 1966 1966 1966 1969 1969 Summer Cooling Degree Days 1969 1969 1969 Spring Cooling Degree Days 1969 1972 1972 1972 1972 1972 1975 1975 1975 1975 1975 1975 1978 1978 1978 1978 1978 1978 1981 1981 1981 1981 1981 1981 1984 1984 1984 1984 1984 1984 1987 1987 1987 1987 1987 1987 1990 1990 1990 1990 1990 1990 1993 1993 1993 1993 1993 1996 1996 1996 1996 1996 1996 1999 1999 1999 1999 1999 1999 2002 2002 2002 2002 2002 2005 2005 2005 2005 2005 2005 2008 2008 2008 2008 2008 2008 2011 2011 2011 2011 2011 2011 MAY APR MAR AUG JUL JUN
Way Forward & Risks Data visualization and preliminary analysis completed. Website design completed. Still need to finalize content. Model design/creation under construction. Major focus over next couple of weeks. Excel calling R needs to be completed. Customer transformation Final model fitting Forecast
Earned Value Earned Value 60000.00 50000.00 40000.00 Dollars 30000.00 Earned Value Planned Value 20000.00 Actual Cost 10000.00 0.00
CPI & SPI CPI and SPI 4.00 3.50 3.00 2.50 Ratio 2.00 CPI SPI 1.50 1.00 0.50 0.00 Week 1 Week 2 Week 3 Week 4 Week 5 Week 6 Week 7 Week 8
References www.novec.com NOVEC 25 th Anniversary History Film http://www.youtube.com/watch?v=2qfeOKnPPGg “Improving Load Management Control for NOVEC”; Kozera, Lohr, McInerney, and Pane. http://seor.gmu.edu/projects/SEOR-Spring12/NOVECLoadManagement/team.html
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