Dataset Used: Appliances Energy Prediction dataset from the UCI Machine Learning Repository.

It contains data collected from a house over 4.5 months (from 2016-01-11 to 2016-05-27), at 10-minute intervals, aiming to predict energy use of appliances based on environmental conditions inside and outside the house.

Dataset Link : https://archive.ics.uci.edu/dataset/374/appliances+energy+prediction

Here’s a quick overview:

• Target variable: Appliances — Energy use in Wh (watt-hour).
• Features:
  • Indoor temperature (T1, T2, …, T9) in °C.
  • Indoor humidity (RH_1, RH_2, …, RH_9) in %.
  • Outdoor temperature (T_out) and humidity (RH_out).
  • Weather-related variables: visibility, pressure, wind speed, etc.
  • lights: also recorded separately — but usually a secondary target.
• Datetime: date field captures timestamp.

The dataset is often used for:

• Regression tasks (predicting energy consumption).
• Time series forecasting.
• Feature importance analysis (e.g., how much weather impacts indoor appliance usage).