HOUSEHOLD ELECTRICITY CONSUMPTION PATTERN ANALYSIS

Abstract

The Household Electricity Consumption Pattern Analysis project focuses on understanding and analyzing the growing demand for electricity in modern households using a data-driven approach. With the increasing use of electrical appliances, air conditioning, and electronic devices, electricity consumption has risen significantly, leading to higher energy costs, frequent peak load conditions, and increased stress on power grids. This project utilizes the publicly available Individual Household Electric Power Consumption dataset and employs Python programming language for data processing and analysis. The dataset includes minute-level measurements of global active power, voltage, current, and appliance-level consumption over several years. Data preprocessing steps such as cleaning, handling missing values, and converting timestamps into a unified datetime format are performed. The data is then resampled into hourly and daily intervals for better analysis. Exploratory Data Analysis (EDA) and visualization techniques are used to identify consumption patterns across different times of the day, days of the week, and seasons. Peak load periods are detected using statistical threshold-based methods applied to global active power. These peaks are further analyzed to determine critical hours of high energy demand.