JoshMusira / -household-electricity-consumption

Time Series Prediction of Household Electricity Consumption

Project Overview

This internship project focuses on leveraging Python for time series prediction of household electricity consumption. Using a dataset with various features related to electricity usage, the goal is to build robust forecasting models that can predict future trends in electricity consumption. Insights derived from this project aim to help households optimize energy usage, plan efficiently, and contribute to sustainable energy practices.

Dataset Description

The dataset consists of the following features:

1. Date: Date of the electricity consumption recording.
2. Time: Time of the electricity consumption recording.
3. Global_active_power: Total active power consumed by the household (in kilowatts).
4. Global_reactive_power: Total reactive power consumed by the household (in kilowatts).
5. Voltage: Voltage level during the electricity consumption period (in volts).
6. Global_intensity: Total current intensity consumed by the household (in amperes).
7. Sub_metering_1: Electricity consumption in sub-metering 1 (e.g., kitchen).
8. Sub_metering_2: Electricity consumption in sub-metering 2 (e.g., laundry).
9. Sub_metering_3: Electricity consumption in sub-metering 3 (e.g., water heater).

Project Objectives

1. Data Preprocessing

• Clean and preprocess the dataset, handling any missing values or outliers.
• Combine the date and time columns into a datetime format for effective time series analysis.

2. Exploratory Data Analysis (EDA)

• Conduct EDA to uncover patterns, trends, and seasonality in electricity consumption.
• Visualize the relationships between different features to gain insights.

3. Time Series Forecasting Models

• Implement time series forecasting models such as ARIMA, SARIMA, or LSTM.
• Evaluate the performance of the models using appropriate metrics.

4. Feature Engineering

• Investigate the impact of various features on electricity consumption.
• Explore the creation of new features that might enhance prediction accuracy.

5. Model Evaluation and Tuning

• Fine-tune model hyperparameters for optimal performance.
• Validate and optimize the model using a separate test dataset.

6. Future Consumption Prediction

• Generate forecasts for future electricity consumption based on the trained models.
• Visualize and interpret the predictions to identify potential consumption patterns.

Deliverables

• Python scripts for data preprocessing, EDA, and time series prediction models.
• Visualizations illustrating consumption patterns, model evaluation metrics, and predicted future trends.
• A comprehensive report summarizing the findings, challenges encountered, and recommendations for optimizing household electricity consumption.

Conclusion

This project equips interns with hands-on experience in time series analysis, forecasting, and feature engineering, contributing to the broader goal of promoting energy-efficient practices in households.