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NRLP Practicum Presentation

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NRLP Practicum Presentation

  1. 1. Predicting Rural Mountain Energy Consumption Based on Substation, Weather, Geography and Tourism Data By: Jeremy Moore
  2. 2. Introduction • Peak power refers to the total power consumption when total power usage is at its highest for a given period of time. • Peak power usage is approximately 300 times more expensive per kWh than non-peak power. • Peak power tends to be the dirtiest source of energy. • The impact of peak power necessitates the isolation and understanding of conditions that indicate peak power usage. • The focus of my work was to develop power prediction capability for our town particularly when we are at peak, so we know where to address and lower the town’s consumption.
  3. 3. Project Objectives & Constraints • Isolate where to address and lower the town’s power consumption when at peak consumption. • Look for correlation between substations. • Develop equation to predict power consumption, while incorporating daily weather forecast data to alert proper channels of upcoming, high consumption days. • Identify variables that significantly drive power consumption.
  4. 4. Methods • Acquisitioned total KWH usage data for each of the region’s five substations in hour intervals 2007-2015 • Brought in weather data for 2007-2015 • Condensed hourly KWH interval readings into daily substation totals, which was matched with corresponding weather data • Constructed a correlation table to examine the relationship of each substation • Discovered minimal correlation between some substations • Compared substations based on geographic location • Four variables were common drivers for all equations: temperature variable, percent change from previous day’s average temperature, previous three day’s average temperature and next three day’s average temperature • The variables identified in our model demonstrate an R2 > .80, except for substation one • Weather, trend and seasonal variables explained high amounts of the variation in energy consumption
  5. 5. Geographical Map Rural Residential Established Neighborhood Campus Central Business Medical Offices
  6. 6. 5 Substation Description and Correlation
  7. 7. Weather, Trend, Seasonality Algorithm: Campus = β0 + β1temperature variable + β2∆% from previous day’s temperature + β3previous 3 day’s avg temp + β4next 3 day’s avg temp + β5∆ from previous day avg temp + β6Trend + β7Trendsq + β8Jan + β9Feb + β10Mar + β11Apr + β12May + β13Jun + β14Jul + β15Aug + β16Oct + β17Nov + β18Dec
  8. 8. Predicted Consumption (Fitted Values) vs. Actual Consumption (Campus)
  9. 9. Conclusion • Absolute value of the difference between the average daily temperature and yearly temperature mean, average of previous 3 days average temperature, and average of next three days average temperature were drivers for all substations except Central Business, which may be driven by other variables. • The effect of each driver in relation to power consumption may assist in marketing behavioral change to reduce costs for local businesses and consumers. Therefore, the next step to improve our model will be to include other variables that may influence energy consumption, including: tourism events, football games, school schedules, geographic conditions and festivals. • We can use our model, in tandem with our peak day prediction model, to predict energy consumption for the mountain region and decrease energy consumption during high-cost periods. • After predicting power consumption and alerting proper channels, incentives may be offered to residents to reduce energy usage during specific time intervals to drive cost savings.

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