Smart Home Energy Management System (SHEMS)

This repository contains the implementation of SHEMS (Smart Home Energy Management System), developed as part of the Principles of Database Systems (CS-GY 6083) course project. The system is designed to manage and analyze smart home energy consumption using a relational database model and a web-based interface.

Project Overview

SHEMS is a database-driven application that enables users to monitor, manage, and analyze their household energy consumption. The system is built using PostgreSQL for database management and Django for backend services, with a frontend designed using HTML, Bootstrap CSS, and JavaScript.

Features

🔹 Database Design & Management

• Relational Schema & ER Diagram: A structured schema to efficiently store addresses, customers, service locations, devices, and energy consumption data.
• Normalization: Database design follows BCNF (Boyce-Codd Normal Form) to reduce redundancy and improve integrity.
• Sample SQL Queries: Includes optimized queries for data retrieval and visualization.

🔹 Web-Based Interface

• 🔑 Authentication System: User login with password hashing using Blowfish encryption.
• 🛠 CRUD Operations: Manage service locations and enrolled smart devices.
• 📊 Data Visualization: Graphs and charts powered by Plotly to track energy consumption trends.
• 🔒 Security Measures: Protection against SQL Injection, Cross-Site Scripting (XSS), and CSRF Attacks.
• 🔄 Transaction & Concurrency Handling: Atomic transactions for customer and address insertion.

🔹 Data Processing & Analytics

• 📡 Smart Device Monitoring: Real-time tracking of energy consumption from enrolled devices.
• 💰 Energy Price Analysis: Stores and analyzes hourly energy prices by zip code.
• ⏳ Time-Series Data Processing: Handles high-frequency device data efficiently.

Visualization & Reports

SHEMS provides interactive graphs to help users understand their energy consumption:

1. 📈 Device Type vs. Energy Consumption
2. ⏳ Time-based Energy Consumption per Service Location
3. ⚡ Actual vs. Average Energy Usage Comparison
4. 🌍 Energy Consumption Trends Across Devices & Locations
5. 🏷 Average Energy Prices by Zip Code

Tech Stack

• Backend: Django (Python)
• Database: PostgreSQL
• Frontend: HTML, Bootstrap CSS, JavaScript
• Libraries: Plotly (Data Visualization), Crypt (Password Hashing)

Authors

• Sumedh Parvatikar (sp7479)
• Jaya Sabarish Reddy Remala (jr6421)

Please look up into the Final_Report for additional information.