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This project explores the application of Long Short-Term Memory (LSTM) networks in predicting household power consumption. Using data collected at one-minute intervals, we demonstrate how LSTM can be leveraged for accurate forecasting.

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sushantdhumak/LSTM_for_Household_Power_Consumption

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Forecasting Household Electric Power Consumption using LSTM


This dataset contains 2,075,259 one-minute measurements of electric power consumption in a single household over almost four years (December 2006 to November 2010). The data includes various electrical quantities and sub-metering values.

Dataset Characteristics

  • Multivariate
  • Time-Series

Dataset Information

The archive was gathered from a house in Sceaux, France, with some missing values present. Additional notes:

  1. The active energy consumed every minute is represented by (global_active_power * 1000 / 60 - sub_metering_1 - sub_metering_2 - sub_metering_3).
  2. Missing values occur about 1.25% of the time, marked with a missing value between consecutive semi-colon attribute separators.

Variables Table

Variable Name Role Type
Date Feature Date
Time Feature Categorical
Global_active_power Feature Continuous
Global_reactive_power Feature Continuous
Voltage Feature Continuous
Global_intensity (Current) Feature Continuous
Sub_metering_1 Sub-metering No. 1 (kW-h) Energy sub-metering for kitchen Feature Continuous
Sub_metering_2 Sub-metering No. 2 (kW-h) Energy sub-metering for laundry room Feature Continuous
Sub_metering_3 Sub-metering No. 3 (kW-h) Energy sub-metering for electric water-heater and air-conditioner Feature Continuous

Dataset Link:

https://archive.ics.uci.edu/dataset/235/individual+household+electric+power+consumption


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This project explores the application of Long Short-Term Memory (LSTM) networks in predicting household power consumption. Using data collected at one-minute intervals, we demonstrate how LSTM can be leveraged for accurate forecasting.

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