Forecast Electricity Demand with LSTM on Pi 5 - Compoden Kit
Predict Tomorrow's Power Demand - Build a Pi 5 Edge AI Forecaster with LSTM and Prophet
Every part needed, pre-tested for compatibility, with an AI build companion trained on this exact project. Shipped from Bengaluru in 3-5 days.
What if you could build a fully self-contained appliance that clips onto your meter box, streams real consumption readings, and forecasts the next 24 hours of demand - comparing two completely different AI approaches? This kit puts a complete smart grid forecasting lab on your desk. Using three CT sensors, a high-speed ADC, and the Raspberry Pi 5's NPU-friendly architecture, you collect real power data and train an LSTM directly on the edge. The included AI companion then guides you through swapping in Facebook's Prophet model, so you can measure RMSE and MAE and decide which forecasting strategy wins for Indian load profiles.
What You'll Build
You'll assemble a current-monitoring station that logs three circuits (or all three phases of a small distribution board) in real time, stores the time series on the onboard NVMe SSD, and exposes a dashboard with 24-hour demand forecasts. The system runs a Jupyter environment on the Pi 5 where you train the LSTM, feed the same data into Prophet, and compare accuracy side by side. By the end, you'll have a portable edge AI forecaster you can demonstrate at college tech fests or hackathons like the Smart India Hackathon energy track.
What You'll Learn
- Collect real-time electricity consumption data using SCT-013 CT sensors and a 16-bit ADS1115 ADC over I�C
- Preprocess noisy time series, handle missing readings, and create sliding windows for sequence prediction
- Architect and train an LSTM neural network directly on Raspberry Pi 5 using TensorFlow Lite with GPU acceleration
- Evaluate two forecasting philosophies - statistical (Prophet) vs deep learning (LSTM) - using RMSE and MAE on live data
Kit Contents
| Component | Quantity |
|---|---|
| Raspberry Pi 5 4GB | 1 |
| SCT-013 CT Sensor | 3 |
| ADS1115 ADC | 1 |
| NVMe SSD 256GB | 1 |
| Pi 5 M.2 HAT+ | 1 |
| USB-C PSU | 1 |
| M-M Wires | 20 |
Why Buy This Kit Instead of Sourcing Parts Separately
| Factor | Sourcing Separately | Compoden Kit |
|---|---|---|
| Compatibility checks | You verify every part | Pre-tested as a system |
| Build support | Forums and scattered tutorials | AI companion trained on this exact project |
| Time to first working build | Days of debugging | Hours, with step-by-step guidance |
| Shipping coordination | Multiple sellers, multiple delays | One shipment from Bengaluru in 3-5 days |
Who This Kit Is For
This kit targets CBSE Class 11-12 students who already tinker with Python and want to apply machine learning to real hardware. B.Tech ECE or EEE sophomores working on smart grid capstone projects will find the ready-to-train forecasting pipeline a significant time saver. Participants of the Smart India Hackathon (energy or smart automation tracks), ATL Tinkering Lab mentors, and IIT/NIT/VIT students prototyping for campus energy audits will appreciate the pre-configured SSD environment that escapes the fragility of SD cards during long logging runs.
Built and Backed by Compoden
Every Compoden kit ships with an AI build companion trained on this exact project - accessible via a QR code on the box, with WhatsApp and email backup. We've spent 10 years building projects for makers, schools, and institutions across India. If a part fails because of a manufacturing defect, replace it free within 7 days.
What if I get stuck during the build?
Scan the QR code on the kit box to start a conversation with the AI companion, which has been trained on every step, library, and common error for this exact project. If you still need help, drop a WhatsApp message - we'll reply with the same 24-hour project knowledge base.
How does the kit compare the forecasting accuracy of Prophet and LSTM?
The included Jupyter notebook splits your recorded load data into training and test sets, runs both models, and prints the root mean square error (RMSE) and mean absolute error (MAE) for the next 24-hour predictions. You see directly which algorithm adapts better to your household or lab consumption pattern.
Can I use this kit with a single-phase supply, or is it only for 3-phase?
You can clamp one SCT-013 onto a single-phase line or any individual circuit. The remaining two sensors can be attached to other appliances or left disconnected; the software automatically detects how many channels are active. This makes the kit equally useful for home energy audits and small-scale load profiling.
Is prior machine learning experience required to complete the build?
Basic Python comfort is assumed, but the AI companion walks you through TensorFlow Lite installation, the LSTM architecture, and the Prophet library without requiring ML theory. The step-by-step notebooks are designed to let you learn forecasting concepts while you build, so you'll finish with a working model and a solid understanding.
LSTM on Pi 5 forecasts next 24-hour electricity demand from historical sensor data - compares Prophet vs LSTM accuracy.
What's in this kit
- Raspberry Pi 5 4GB
- SCT-013 CT Sensor x3
- ADS1115 ADC
- NVMe SSD 256GB
- Pi 5 M.2 HAT+
- USB-C PSU
- M-M Wires x20
Shipping Information
- Prepaid Orders: ₹75 for orders up to ₹999, FREE shipping above ₹999
- COD Orders: ₹125 shipping + ₹50 COD fee = ₹175 total
- Delivery Timeline: Dispatch in 1-2 days, delivery in 2-7 days depending on location
Returns & Warranty
- 7-Day Return: Manufacturing defects only (approval required)
- Warranty: 7 days from delivery
- Non-Returnable: Batteries, consumables, cut wires, clearance items