Today, demand for electrical energy is higher than ever, but so are the economic and environmental costs producing energy. Thus, we must seek to mitigate the negative impacts of energy production and misuse. One sector with large potential to improve energy efficiency is residential usage, which accounts for 37.7% of total US energy end-use sales.
Surges and declines in electricity demand significantly affect energy efficiency. Because electricity providers cannot store the energy they produce (it would be 100 to 1000 times more expensive to do so), they must accommodate demand surges during peak hours by turning on environmentally unsound coal plants. Furthermore, cleaner wind energy often goes unused during non-peak night-time hours.
This summer, we analyzed domestic energy use data with the goal of encouraging consumers to change their consumption patterns and thus drastically reduce costs for themselves and the environment. We sought to address the questions: How do we organize immense quantities of smartgrid data and package it in a way the average consumer will understand? How do we persuade them to change their habits?
We acquired domestic energy use data from 30 homes in Austin, TX from WikiEnergy, the world’s largest database of original data on customer electricity use. The data was disaggregated into 15 minute intervals over the course of 13 months, totaling 38,016 data entries.
We created a MySQL database to compile the information and then used Python’s pandas module to reorganize and plot the data.
Data over time
Residential energy consumption patterns seem to follow expectations.
● Residents use more energy during weekends compared to weekdays, mostly during the evening/night hours.
● There is a solid correlation between energy use and outside temperature. Energy use is highest during the months of July-September, when the temperature is highest. Most of this increase in energy use is due to increased use of air conditioning.
● The major users of energy include the pool, air conditioning, subpanel, dryer, and water heater.
● Some holidays, such as Labor Day, Thanksgiving, Christmas Eve, and Memorial Day, showed an increase in total energy use. Other holidays, such as Mother’s Day, showed a decrease.
● Earth Day experienced an increase in total energy use.
In continuing our research, we would like to
● examine the influence of pricing incentives, smart thermostats, and text message notifications on decreasing energy use
● calculate the economic and environmental effects of switching use away from peak hours
● determine the possible correlations between economic background, family type, education level, and age range of the house occupants on their energy consumption patterns
● examine the possible effects of geography and climate on energy use
● compare the patterns of US residents with residents in other countries