How My Parents’ Energy Bills Inspired an AI-Powered Smart Home Energy Optimiser

As a kid, one of my vivid memories was watching my parents’ faces twist in frustration when the energy bill arrived. Every month, they’d remind us about turning off lights, unplugging devices, and not letting the AC run all day. But let's face it—no matter how much we complained, those energy bills weren’t going anywhere.

Fast forward to today, and I find myself in a similar boat. While I’m the one grumbling about the electricity bills now, I decided to break the cycle. With my skills in AI and the power of AWS, I built a project to optimize home energy consumption while automating smarter decisions. And today, I’m sharing how you can build it too.

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What Does the Smart Home Energy Optimizer Do?

This project leverages AWS Bedrock and generative AI models to:

1. Analyze appliance usage patterns in real-time.
2. Predict peak energy usage and recommend actions (e.g., scheduling high-consumption tasks like laundry during low-demand periods).
3. Provide personalized energy-saving tips based on household behavior.

With this, homeowners can better manage their energy consumption, save money, and reduce their environmental footprint—all while automating tedious monitoring tasks.

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How It Works

This is how the project is structured:

Step 1: Setting Up Your Environment

We use AWS Bedrock to tap into foundation models for analyzing energy usage data and generating recommendations. Here’s the tech stack:

• Amazon Bedrock for generative AI insights.
• Amazon S3 for storing synthetic energy usage data.
• Amazon Lambda to automate AI-powered recommendations.
• Streamlit for the user interface.

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Step 2: Generating Synthetic Data

Since real household energy data might not be available, let’s simulate some. Run this script to create synthetic data for appliance energy consumption:

import pandas as pd
import random

# Generate synthetic data
def generate_data():
    hours = list(range(24))
    appliances = ["Fridge", "AC", "Lights", "Washing Machine", "Oven"]
    data = []

    for day in range(1, 31):  # Generate for 30 days
        for hour in hours:
            for appliance in appliances:
                usage = round(random.uniform(0.1, 2.0), 2)  # Random kWh usage
                data.append({"Day": day, "Hour": hour, "Appliance": appliance, "Usage (kWh)": usage})

    return pd.DataFrame(data)

# Save to CSV
df = generate_data()
df.to_csv("synthetic_energy_data.csv", index=False)
print("Synthetic data generated!")

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Step 3: Building the AI Model with Bedrock

AWS Bedrock helps us generate personalized recommendations based on this synthetic data. Here’s how we connect and use it:

import boto3

# Initialize Bedrock client
client = boto3.client('bedrock', region_name='us-east-1')

# Example input to a foundation model
input_text = (
    "Based on this usage data: Fridge: 1.2kWh, AC: 3.5kWh, "
    "recommend energy-saving actions for the household."
)

response = client.invoke_model(
    modelId="foundation-model-id",  # Replace with actual model ID
    body={"inputText": input_text},
)

print("Recommendations:", response["body"]["outputText"])

The AI processes patterns and suggests actions like “Run the washing machine after 9 PM to reduce costs” or “Consider switching to LED lights.”

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Step 4: Creating the Dashboard with Streamlit

Streamlit is perfect for creating a user-friendly interface. Here's how you can integrate the data and recommendations:

import streamlit as st
import pandas as pd

# Load synthetic data
data = pd.read_csv("synthetic_energy_data.csv")

st.title("Smart Home Energy Optimizer")

# Display raw data
st.subheader("Energy Usage Data")
st.dataframe(data)

# Upload your AI recommendations (replace with actual AI output)
st.subheader("AI-Powered Recommendations")
st.text_area("Here are your energy-saving suggestions:", "Run washing machine after 9 PM.")

This simple interface allows users to view their energy usage patterns and actionable tips generated by the AI model.

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Why AWS Bedrock?

AWS Bedrock simplifies the integration of foundation models into your projects. Instead of building an AI model from scratch, you can directly use powerful pre-trained models for tasks like:

• Summarization.
• Pattern recognition.
• Personalized recommendations.

In this project, it saved time while providing highly accurate insights.

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Key Takeaways

This project bridges personal frustration with technology-driven solutions. By using AWS Bedrock, I was able to turn a long-standing problem into an innovative, automated tool that not only saves time but also helps save money and energy.

If you’re looking to experiment with generative AI in real-world applications, this project is a great starting point. From smart homes to personalized assistants, the possibilities are endless.

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What do you think of the Smart Home Energy Optimizer? Let me know in the comments, and feel free to connect with me if you want to explore similar AWS AI-driven projects!

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