Raspberry Pi 5 LSTM-Powered Smart Library Occupancy Predictor — Forecast Peak Hours with AI

Every part needed, pre-tested for compatibility, with an AI build companion trained on this exact project. Shipped from Bengaluru in 3-5 days.

Imagine a library where seat availability and entry points automatically feed a dashboard, and an LSTM neural network on the Raspberry Pi 5 forecasts precisely when the rush hours will hit — so administrators can plan staff rosters, open extra reading rooms, or guide students to quieter times. This kit brings that entire data‑driven resource planning system into your hands, from force‑sensing resistors embedded under chairs to dual IR people counters at the door.

What You'll Build

You’ll assemble eight FSR 402 pressure pads to detect which seats are occupied, position two IR break‑beam sensors to count people entering and leaving, and stream all sensor readings over GPIO to the Raspberry Pi 5. The Pi stores a rolling dataset on the included NVMe SSD, and a pre‑trained LSTM model predicts occupancy levels 30 and 60 minutes ahead. The final output is a live web dashboard showing current occupancy and predicted spikes.

What You'll Learn

• Calibrating thin‑film FSR sensors for real‑world weight thresholds and de‑bouncing false triggers
• Writing a multi‑sensor data acquisition loop in Python with threading on Raspberry Pi 5
• Training an LSTM model on time‑series occupancy data to forecast future load
• Deploying the model on the Pi and serving predictions through a lightweight Flask dashboard

Kit Contents

Why Buy This Kit Instead of Sourcing Parts Separately

Who This Kit Is For

This kit speaks directly to B.Tech ECE, EEE, and CSE students building final‑year projects around IoT‑enabled smart campuses or Smart India Hackathon prototypes. CBSE Class 11‑12 students in ATL Tinkering Labs can tackle the sensor integration with mentorship, while IIT, NIT, VIT, and BITS teams will value the ready‑to‑use LSTM foundation for data‑driven facility management. The kit bridges hardware tinkering and applied machine learning without assuming prior experience in either.

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.