Predicting Rural Mountain
Energy Consumption Based
on Substation, Weather,
Geography and Tourism
By: Jeremy Moore
• 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.
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.
• 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
• 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
• The variables identified in our model demonstrate an R2 > .80, except for
• Weather, trend and seasonal variables explained high amounts of the
variation in energy consumption
Rural Residential Established
Predicted Consumption (Fitted Values)
vs. Actual Consumption (Campus)
• 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.