Abram Gross Yafeng Peng Jedidiah Shirey
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1 Abram Gross Yafeng Peng Jedidiah Shirey
2 Contents Context Problem Statement Method of Analysis Forecasting Model Way Forward Earned Value
3 NOVEC Background (1 of 2) Northern Virginia Electric Cooperative (NOVEC) is a distributor of energy to 6 counties in Northern Virginia. Nearly 15, customers. Mandated to meet all energy requests in pre-specified zones of obligation. Primary means to service communities is through bulk energy market purchases.
4 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.
5 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?
6 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.
7 Scope Data: NOVEC monthly energy purchases data since Dulles weather data since Historic economic factors data since 198s. 3 years of forecasted economic factors. Model: Parameters: historic energy purchases, weather data, economics, and customer-base. Predictions for energy consumption over a 3-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).
8 Key Definitions CDD CDD: Cooling Degree-Day = MAX(ActualDegree UpperBound, ) * Duration. HDD: Heating Degree-Day = MAX(LowerBound ActualDegree, ) * Duration. Neutral zone: Upper and lower bounds for temperatures that do not impact load. 8 HDD 7 CDD 6 5 HDD 4 3 Actual_Temperature Upper_Bound Lower_Bound t * Analysis will explore varied upper/lower bounds for temperature Actual_Temperature Upper_Bound Lower_Bound
9 Weather Data Visualization (1 of 2) Historic weather data was analyzed by average temperature per month to view trends. Average temperature trending upwards from 196 to the present for each month (graph is for July).
10 Weather Data Visualization (2 of 2) Variance was also evaluated and no change is seen.
11 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.
12 Methodology I. Relate model parameters II. Develop normalization methodology III. Forecast Model Assess Model * Employment Housing Stocks GDP Economic Variables Per Capita Demand Basic Load Monthly Demand Forecast Historic Power Demand Remaining Power Demand Residential Commercial Customer Combination Composite Customer-Base Weather- Normalization CDD Impact HDD Impact * Model forecast will be verified against power demand and compared to existing model s accuracy.
13 Degree-Days Below 65 F Degree-Days Above 65 F EEA1: Rate of Climate Change Historic weather data was analyzed to determine trends: Seasonal fluctuations. Overall rate of change. Characterize uncertainty/variability. 6 Heating Degree-Days to Cooling Degree-Days to Total HDD 12 Total CDD Average HDD 2 6 Average CDD Max HDD 1 2 Max CDD
14 Customers & Housring Stocks (Thousands) Demand (Millions - kwh) EEA2: Economics Model Model to associate economic factors to customer-base in under development. Residential Demand & Metro-D.C. Econometrics Aug-89 Aug-9 Aug-91 Aug-92 Aug-93 Aug Housing Stocks Customers Demand
15 Energy Consumption (kwhr) EEA3: Impact of Weather Changes to per capita energy demand by customer type: residential and non-residential. Monthly Customer per Capita Demand (25-21) Non- Residential 12 1 Residential 8 HDD 6 4 CDD 2 Jan-5 Jan-6 Jan-7 Jan-8 Jan-9 Jan-1
16 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.
17 Degree-Days Degree-Days Below Below 65 F 65 F Degree-Days Degree-Days Exceeding Exceeding 65 F 65 F Degree-Days Below Below F F Degree-Days Exceeding F F Degree-Days Below F F Degree-Days Exceeding F F Preliminary Results FEB 4 Variability in yearly weather patters doesn t appear to change. 5 Winter Heating Degree Days DEC JAN Climate change is more apparent in winter and summer than fall and spring Winter Cooling Degree Days DEC JAN FEB Spring Heating Degree Days MAR MAR APR APR MAY MAY Spring Cooling Degree Days MAR MAR APR APR MAY MAY Summer Heating Degree Days Summer Heating Degree Days JUN JUL JUN AUG JUL AUG Summer Cooling Degree Days Summer Cooling Degree Days JUN JUL JUN AUG JUL AUG 16 Fall Heating Degree Days Fall Heating Degree Days 45 Fall Cooling Degree Days Fall Cooling Degree Days
18 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
19 Dollars Earned Value 6. Earned Value Earned Value Planned Value Actual Cost 1..
20 Ratio CPI & SPI 4. CPI and SPI CPI SPI Week 1 Week 2 Week 3 Week 4 Week 5 Week 6 Week 7 Week 8
21 References NOVEC 25 th Anniversary History Film Improving Load Management Control for NOVEC ; Kozera, Lohr, McInerney, and Pane.
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Climate Division: CA 6 NWS Call Sign: Month (1) Min (2) Month(1) Extremes Lowest (2) Temperature ( F) Lowest Month(1) Degree s (1) Base Temp 65 Heating Cooling 100 Number of s (3) Jan 69.4 46.6 58.0 92
Climatography of the United States No
Climate Division: CA 4 NWS Call Sign: Month (1) Min (2) Month(1) Extremes Lowest (2) Temperature ( F) Lowest Month(1) Degree s (1) Base Temp 65 Heating Cooling 1 Number of s (3) Jan 58.5 38.8 48.7 79 1962
Lesson Adaptation Activity: Analyzing and Interpreting Data
Lesson Adaptation Activity: Analyzing and Interpreting Data Related MA STE Framework Standard: 3-ESS2-1. Use graphs and tables of local weather data to describe and predict typical weather during a particular
Climatography of the United States No
Climate Division: CA 6 NWS Call Sign: Month (1) Min (2) Month(1) Extremes Lowest (2) Temperature ( F) Lowest Month(1) Degree s (1) Base Temp 65 Heating Cooling 1 Number of s (3) Jan 67.5 42. 54.8 92 1971
Time Series and Forecasting
Time Series and Forecasting Introduction to Forecasting n What is forecasting? n Primary Function is to Predict the Future using (time series related or other) data we have in hand n Why are we interested?
What is the difference between Weather and Climate?
What is the difference between Weather and Climate? Objective Many people are confused about the difference between weather and climate. This makes understanding the difference between weather forecasts