time series forecasting analysis and prediction on household electric power consumption

Dataset Information

Household Electric Power Consumption

I'm trying to learn and build a deep learning approach with RNN Architecture using LSTM type with Bidirectional LSTM (BiLSTM) variant and the SGD optimizer to fit the data and then used to predict the image data.

Context:

Measurements of electric power consumption in one household with a one-minute sampling rate over a period of almost 4 years. Different electrical quantities and some sub-metering values are available.

Data Set Characteristics:

Multivariate, Time-Series

Associated Tasks:

Regression, Clustering

Database information:

• This database have 2,075,259 rows and 7 columns.
• date: Date in format dd/mm/yyyy
• time: time in format hh:mm:ss
• global_active_power: household global minute-averaged active power (in kilowatt)
• global_reactive_power: household global minute-averaged reactive power (in kilowatt)
• voltage: minute-averaged voltage (in volt)
• global_intensity: household global minute-averaged current intensity (in ampere)
• 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).
• 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.
• 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.