Best Energy consumption prediction Web Application Using Prophet and MERN Stack

Energy consumption prediction Web Application

Predicting energy usage is getting more important these days, especially with how much we rely on data and need to save energy. It helps power companies manage things better, save money, and make sure people get steady service. This project is a web app that predicts electricity use using a cool algorithm called Prophet by Facebook. It also uses info like temperature and holidays to make the predictions more accurate.The app has a clean and easy-to-use interface where users can check future energy usage, see trends, check the weather forecast, and even contact customer support—all in one place.

Project Overview

This system is built on the powerful MERN stack (MongoDB, Express.js, React, Node.js) and uses Facebook Prophet for forecasting energy consumption. The model is enhanced by adding external regressors such as temperature and holiday information, allowing for better real-world performance.

Users can interact with the app through a dashboard that provides:

• Accurate energy usage prediction
• Weather forecast integration
• Interactive graphs and plots
• Downloadable predicted data
• Email support functionality

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Technologies Used

Project Details Table

Available Features

This web application is developed with a clear focus on functionality and user experience. Below are the key features implemented based on the project content:

• Energy Consumption Prediction
  Uses Prophet algorithm to forecast energy consumption for upcoming periods based on historical data, weather conditions, and holiday information.
• Weather Forecast Integration
  The webapp fetches and displays 5-day weather forecasts using OpenWeather API to help visualize how weather changes impact energy use.
• Graphical Visualization
  Plots interactive energy usage graphs using the forecasted data to provide insightful analysis for better planning.
• Download Predicted Data
  Users can easily export forecasted results in downloadable formats for reporting or offline analysis.
• Customer Care Email Integration
  Users can directly email issues or feedback from within the platform for support or suggestions.

File Structure Overview

The project repository is well-structured for ease of understanding and scalability. Here’s a breakdown:

• src/model: Contains Python scripts to train and tune the machine learning model using Prophet and additional regressors.
• src/webapp: Complete MERN-based source code for the web interface and server logic.
• data/: Includes raw training data for the energy consumption model.
• diagrams/: UML diagrams showcasing system architecture and data flow.
• documents/: Contains formal documentation like SRS, Design Docs, Test Plans, and more.

Documentation Included

To ensure the project is professional-grade and ready for institutional or commercial use, it includes a variety of documentation:

• Software Requirements Specification (SRS)
• Project Plan
• Design Document
• Test Plan Document
• UML Diagrams

These documents make the project ideal for submission, demonstration, or deployment in real-world applications.

Why This Project Stands Out

• It combines cutting-edge machine learning with real-time APIs.
• Built entirely using modern full-stack development practices (MERN).
• Includes end-to-end forecasting, interactivity, and export features.
• Modular and well-documented codebase for easy customization or extension.

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