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DAT264x: Identifying Appliances from Energy Use Patterns
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Identifying Appliances from Energy Use Patterns


According to a 2017 report, the U.S. Energy Information Administration projects a 28% increase in world energy consumption by 2040. And the energy sector is a major contributor to climate change. For example, energy production and use accounts for more than 84% of U.S. greenhouse gas emissions.

Increasing the efficiency of energy consumption has benefits for consumers, providers, and the environment. With an increasing number of IoT devices coming online in the energy sector, there is more and more data that can be used to monitor and track energy consumption. Ultimately, this type of data can be used to provide consumers and businesses with recommendations on ways to save energy, lower costs, and help the planet.

In this challenge, you will use standard AI tools to identify 11 different types of appliances from their electric signatures, quantified by current and voltage measurements.

Background

This plug load dataset contains current and voltage measurements sampled at 30 kHz from 11 different appliance types present in more than 60 households in Pittsburgh, Pennsylvania. Plug load refers to the energy used by products that are powered by means of an ordinary AC plug (i.e., plugged into an outlet). For each appliance, plug load measurements were post-processed to extract a two-second-long window of measurements of current and voltage. For some observations, the window contains both the startup transient state (turning the appliance on) as well as the steady-state operation (once the appliance is running). For others, the window only contains the steady-state operation. The observations were then transformed into two spectrograms, one for current, and one for voltage.

A spectrogram is a visual representation of the various frequencies of sound as they vary with time. The x-axis represents time (2 seconds in our case), and the y-axis represents frequency (measured in Hz). The colors indicate the amplitude of a particular frequency at a particular time (i.e., how loud it is). We're measuring amplitude in decibels, with 0 being the loudest, and -80 being the softest. So in the example spectrogram below, lower frequencies are louder than higher frequencies. Our spectrograms tend to have horizontal lines given that we are capturing appliances in their steady-state. In other words, the amplitudes of various frequencies are fairly constant over time.

spectrograms

Spectrograms were created using librosa, a python package for music and audio analysis. The code to generate a spectrogram looks like this:

S = librosa.feature.melspectrogram(y=obs, sr=30000)
spectrogram = librosa.power_to_db(S)
plt.imsave(file_path, arr=spectrogram)

Under the hood, this process:

  • Takes the fourier transform of a windowed excerpt of the raw signal, in order to decompose the signal into its consistuent frequencies.
  • Maps the powers of the spectrum onto the mel scale. The mel scale is a perceptual scale where pitches are judged to be equal in distance from one another based on the human ear.
  • Takes the logs of the power (amplitude squared) at each of the mel frequencies to convert to decibel units.
  • Plots and saves the resulting image.

There is a lot of useful information encoded in these spectrograms. Now it's time to use your deep learning skills to parse out which patterns correspond to which types of appliances.