Project: Using time series regression and clustering to detect correlation among time, voltage imbalance, waste power and the energy usage for different types of appliances used in a household.
The data set was obtained from the UC Irvine Machine Learning Repository https://archive.ics.uci.edu/ml/datasets/Individual+household+electric+power+consumption. It contains the measurements of electric power consumption in one household with one-minute sampling rate over a period of almost 4 years. This archive contains 2075259 measurements gathered between December 2006 and November 2010 (47 months).
This dataset will provide significant insights into utility consumption trend and the correlation between minutes, hours, days or months and the energy usage of different types of appliances used at home. It, in turn, will help families or companies make better use of the power they are consuming.
Below is some attribute information of the dataset:
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Date in format dd/mm/yyyy
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Time in format hh:mm:ss
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Global Active Power: household global minute-averaged active power (in kilowatt)
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Global Reactive Power: household global minute-averaged reactive power (in kilowatt)
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Voltage: minute-averaged voltage (in volt)
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Global Intensity: household global minute-averaged current intensity (in ampere)
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Sub Metering 1: energy sub-metering No. 1 (in watt-hour of active energy). It corresponds to the kitchen, containing mainly a dishwasher, an oven and a microwave (hot plates are not electric but gas powered).
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Sub Metering 2: energy sub-metering No. 2 (in watt-hour of active energy). It corresponds to the laundry room, containing a washing-machine, a tumble-drier, a refrigerator and a light.
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Sub Metering 3: energy sub-metering No. 3 (in watt-hour of active energy). It corresponds to an electric water-heater and an air-conditioner.