Project: Smart Energy Consumption Analysis and Alert System Objective: Build a system using the Emporia Gen 3 Smart Home Energy Monitor to track, analyze, and optimize home energy usage. The system will notify users when energy usage exceeds specified thresholds, providing real-time insights via a dashboard and alerts via email/SMS.

Components and Tools:

1. Hardware:

   • Emporia Gen 3 Smart Home Energy Monitor
   • Wi-Fi-enabled router
   • Raspberry Pi (optional, for local data processing)
   • Smart Plugs (optional, for appliance-level monitoring)

2. Software/Platforms:

   • Python for data collection and processing
   • Flask/Django for the dashboard (or Node.js if preferred)
   • Emporia API (for retrieving energy monitor data)
   • Twilio API (for SMS notifications)
   • SMTP (for email notifications)
   • SQLite or MySQL for data storage

3. Key APIs and Libraries:

   • requests

     (for interacting with the Emporia API)

   • pandas

     (for data analysis)

   • matplotlib

     /

     plotly

     (for data visualization)

   • Flask

     /

     Django

     (for building the web interface)

Steps to Build the Project:

1. Setup Emporia Gen 3 Energy Monitor:

   • Install and configure the Emporia device according to the manufacturer’s instructions.
   • Register the device via the Emporia app and link it to your account.

2. Access Emporia API:

   • Generate API credentials from the Emporia web portal or documentation.
   • Use these credentials to fetch real-time and historical energy usage data.

3. Data Collection Script (Python):

   • Write a script to fetch data from the Emporia API at regular intervals.
   • Save the data to a database for analysis.

import requests
import sqlite3
import time

# Emporia API configuration
API_URL = "https://api.emporiaenergy.com/v1"
API_KEY = "your_emporia_api_key"

# Database setup
conn = sqlite3.connect("energy_data.db")
cursor = conn.cursor()
cursor.execute("""
CREATE TABLE IF NOT EXISTS energy_usage (
 timestamp TEXT,
 usage REAL
)
""")
conn.commit()

# Fetch data
def fetch_energy_data():
 headers = {"Authorization": f"Bearer {API_KEY}"}
 response = requests.get(f"{API_URL}/devices/usage", headers=headers)
 if response.status_code == 200:
 data = response.json()
 usage = data["currentUsage"] # Adjust based on the API response
 timestamp = time.strftime("%Y-%m-%d %H:%M:%S")
 cursor.execute("INSERT INTO energy_usage VALUES (?, ?)", (timestamp, usage))
 conn.commit()
 else:
 print("Failed to fetch data:", response.status_code, response.text)

# Schedule periodic data fetching
while True:
 fetch_energy_data()
 time.sleep(60) # Fetch data every minute

4. Analyze and Visualize Data:
   • Create daily, weekly, and monthly usage summaries.
   • Visualize trends using

     matplotlib

     or

     plotly

     .

import pandas as pd
import matplotlib.pyplot as plt

# Load data
data = pd.read_sql_query("SELECT * FROM energy_usage", conn)

# Plot
data['timestamp'] = pd.to_datetime(data['timestamp'])
data.set_index('timestamp', inplace=True)
data['usage'].plot(kind='line')
plt.title("Energy Usage Over Time")
plt.xlabel("Time")
plt.ylabel("Energy Usage (kWh)")
plt.show()

5. Build a Web Dashboard:
   • Use Flask to create a web interface for visualizing data and setting thresholds.

from flask import Flask, render_template, jsonify

app = Flask(__name__)

@app.route("/")
def index():
 return render_template("index.html")

@app.route("/data")
def get_data():
 data = pd.read_sql_query("SELECT * FROM energy_usage", conn)
 return jsonify(data.to_dict(orient="records"))

if __name__ == "__main__":
 app.run(debug=True)

6. Set Alerts:
   • Use Twilio or SMTP to send notifications if energy usage exceeds a threshold.

import smtplib

def send_alert(usage):
 if usage > 100: # Replace with your threshold
 with smtplib.SMTP("smtp.gmail.com", 587) as server:
 server.starttls()
 server.login("your_email@gmail.com", "your_password")
 server.sendmail(
 "your_email@gmail.com",
 "recipient_email@gmail.com",
 f"Subject: High Energy Usage AlertnnYour energy usage is too high: {usage} kWh!"
 )

Features:

1. Real-Time Monitoring: View live energy usage.
2. Historical Data Analysis: Analyze past energy trends.
3. Custom Alerts: Set thresholds for notifications.
4. Dashboard: Access usage stats and trends via a user-friendly web interface.

Next Steps:

• Add machine learning to predict future energy consumption.
• Integrate with smart home devices to automate energy-saving measures.
• Expand the project to support multiple energy monitors.