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A model that predicts the output of a solar power system installed in Berkeley, CA. The data was compiled by Ph.D. candidate Alexandra Constantin and is available at www.eecs.berkeley.edu/~alexacon/.
Electric consumption in New York by ZIP code (latitude and longitude), and building type
This archive contains 2075259 measurements gathered between December 2006 and November 2010 (47 months).
1.(global_active_power*1000/60 - sub_metering_1 - sub_metering_2 - sub_metering_3) represents the active energy consumed every minute (in watt hour) in the household by electrical equipment not measured in sub-meterings 1, 2 and 3.
2.The dataset contains some missing values in the measurements (nearly 1,25% of the rows). All calendar timestamps are present in the dataset but for some timestamps, the measurement values are missing: a missing value is represented by the absence of value between two consecutive semi-colon attribute separators. For instance, the dataset shows missing values on April 28, 2007.
1.date: Date in format dd/mm/yyyy
2.time: time in format hh:mm:ss
3.global_active_power: household global minute-averaged active power (in kilowatt)
4.global_reactive_power: household global minute-averaged reactive power (in kilowatt)
5.voltage: minute-averaged voltage (in volt)
6.global_intensity: household global minute-averaged current intensity (in ampere)
7.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).
8.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.
9.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.
Electricity Price by State and rate type
Predictive maintenance dataset about oil wells downhole equipment failures using sensor data. Source including more information: https://www.kaggle.com/c/equipfailstest/overview Actual source accessible data: https://raw.githubusercontent.com/geooot/tamudatathon2019/master/equip_failures_training_set.csv Missing values have been either removed or replaced with the mean as a cleaning step providing good anomaly detectors results.