jusiajusia / analysis-prediction-clustering-on-power-consumption

Analysis, Prediction & Clustering on an individual household's Electrical Power Consumption

About the Dataset:

It contains 2075259 measurements gathered between December 2006 and November 2010 (47 months).

Attributes:

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.

Notes:

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.

Steps:

Sequence of Files to be run:

1. Create an empty folder named 'dataset' write outside the git-cloned folder, and place the initial dataset inside the folder
2. Files in Cleaning and Preprocessing
3. Files in Prediction - SARIMA, LSTM and Hybrid in that order
4. Files in Clustering