{"product_id":"forecast-electricity-demand-with-lstm-on-pi-5-compoden-kit","title":"Forecast Electricity Demand with LSTM on Pi 5 - Compoden Kit","description":"\u003ch1\u003ePredict Tomorrow's Power Demand - Build a Pi 5 Edge AI Forecaster with LSTM and Prophet\u003c\/h1\u003e\n\u003cp class=\"value-summary\"\u003eEvery part needed, pre-tested for compatibility, with an AI build companion trained on this exact project. Shipped from Bengaluru in 3-5 days.\u003c\/p\u003e\n\u003cdiv class=\"specs-strip\"\u003e\n  \u003cspan\u003e\u003cstrong\u003eDifficulty:\u003c\/strong\u003e Intermediate\u003c\/span\u003e\n  \u003cspan\u003e\u003cstrong\u003eBuild Time:\u003c\/strong\u003e 5-6 hrs\u003c\/span\u003e\n  \u003cspan\u003e\u003cstrong\u003eAge:\u003c\/strong\u003e 15-21\u003c\/span\u003e\n  \u003cspan\u003e\u003cstrong\u003eSkill:\u003c\/strong\u003e Deploying LSTM networks for time series forecasting on edge\u003c\/span\u003e\n\u003c\/div\u003e\n\u003cp\u003eWhat 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.\u003c\/p\u003e\n\u003ch2\u003eWhat You'll Build\u003c\/h2\u003e\n\u003cp\u003eYou'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.\u003c\/p\u003e\n\u003ch2\u003eWhat You'll Learn\u003c\/h2\u003e\n\u003cul\u003e\n  \u003cli\u003eCollect real-time electricity consumption data using SCT-013 CT sensors and a 16-bit ADS1115 ADC over I�C\u003c\/li\u003e\n  \u003cli\u003ePreprocess noisy time series, handle missing readings, and create sliding windows for sequence prediction\u003c\/li\u003e\n  \u003cli\u003eArchitect and train an LSTM neural network directly on Raspberry Pi 5 using TensorFlow Lite with GPU acceleration\u003c\/li\u003e\n  \u003cli\u003eEvaluate two forecasting philosophies - statistical (Prophet) vs deep learning (LSTM) - using RMSE and MAE on live data\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003ch2\u003eKit Contents\u003c\/h2\u003e\n\u003ctable\u003e\n  \u003cthead\u003e\u003ctr\u003e\n\u003cth\u003eComponent\u003c\/th\u003e\n\u003cth\u003eQuantity\u003c\/th\u003e\n\u003c\/tr\u003e\u003c\/thead\u003e\n  \u003ctbody\u003e\n    \u003ctr\u003e\n\u003ctd\u003eRaspberry Pi 5 4GB\u003c\/td\u003e\n\u003ctd\u003e1\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eSCT-013 CT Sensor\u003c\/td\u003e\n\u003ctd\u003e3\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eADS1115 ADC\u003c\/td\u003e\n\u003ctd\u003e1\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eNVMe SSD 256GB\u003c\/td\u003e\n\u003ctd\u003e1\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003ePi 5 M.2 HAT+\u003c\/td\u003e\n\u003ctd\u003e1\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eUSB-C PSU\u003c\/td\u003e\n\u003ctd\u003e1\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eM-M Wires\u003c\/td\u003e\n\u003ctd\u003e20\u003c\/td\u003e\n\u003c\/tr\u003e\n  \u003c\/tbody\u003e\n\u003c\/table\u003e\n\u003ch2\u003eWhy Buy This Kit Instead of Sourcing Parts Separately\u003c\/h2\u003e\n\u003ctable\u003e\n  \u003cthead\u003e\u003ctr\u003e\n\u003cth\u003eFactor\u003c\/th\u003e\n\u003cth\u003eSourcing Separately\u003c\/th\u003e\n\u003cth\u003eCompoden Kit\u003c\/th\u003e\n\u003c\/tr\u003e\u003c\/thead\u003e\n  \u003ctbody\u003e\n    \u003ctr\u003e\n\u003ctd\u003eCompatibility checks\u003c\/td\u003e\n\u003ctd\u003eYou verify every part\u003c\/td\u003e\n\u003ctd\u003ePre-tested as a system\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eBuild support\u003c\/td\u003e\n\u003ctd\u003eForums and scattered tutorials\u003c\/td\u003e\n\u003ctd\u003eAI companion trained on this exact project\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eTime to first working build\u003c\/td\u003e\n\u003ctd\u003eDays of debugging\u003c\/td\u003e\n\u003ctd\u003eHours, with step-by-step guidance\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eShipping coordination\u003c\/td\u003e\n\u003ctd\u003eMultiple sellers, multiple delays\u003c\/td\u003e\n\u003ctd\u003eOne shipment from Bengaluru in 3-5 days\u003c\/td\u003e\n\u003c\/tr\u003e\n  \u003c\/tbody\u003e\n\u003c\/table\u003e\n\u003ch2\u003eWho This Kit Is For\u003c\/h2\u003e\n\u003cp\u003eThis 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.\u003c\/p\u003e\n\u003ch2\u003eBuilt and Backed by Compoden\u003c\/h2\u003e\n\u003cp\u003eEvery 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.\u003c\/p\u003e\n\u003cdetails\u003e\u003csummary\u003eWhat if I get stuck during the build?\u003c\/summary\u003e\u003cp\u003eScan 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.\u003c\/p\u003e\u003c\/details\u003e\n\u003cdetails\u003e\u003csummary\u003eHow does the kit compare the forecasting accuracy of Prophet and LSTM?\u003c\/summary\u003e\u003cp\u003eThe 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.\u003c\/p\u003e\u003c\/details\u003e\n\u003cdetails\u003e\u003csummary\u003eCan I use this kit with a single-phase supply, or is it only for 3-phase?\u003c\/summary\u003e\u003cp\u003eYou 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.\u003c\/p\u003e\u003c\/details\u003e\n\u003cdetails\u003e\u003csummary\u003eIs prior machine learning experience required to complete the build?\u003c\/summary\u003e\u003cp\u003eBasic 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.\u003c\/p\u003e\u003c\/details\u003e\n\n\u003cdiv class=\"kit-description\"\u003e\n  \u003cp\u003eLSTM on Pi 5 forecasts next 24-hour electricity demand from historical sensor data - compares Prophet vs LSTM accuracy.\u003c\/p\u003e\n  \u003ch4\u003eWhat's in this kit\u003c\/h4\u003e\n  \u003cul\u003e\n    \u003cli\u003e\u003ca href=\"\/products\/raspberry-pi-5-model-b-4gb-technical-specs-projects\"\u003eRaspberry Pi 5 4GB\u003c\/a\u003e\u003c\/li\u003e\n    \u003cli\u003eSCT-013 CT Sensor x3\u003c\/li\u003e\n    \u003cli\u003e\u003ca href=\"\/products\/ads1115-16-bit-i2c-adc-module-for-arduino-raspberry-pi\"\u003eADS1115 ADC\u003c\/a\u003e\u003c\/li\u003e\n    \u003cli\u003e\u003ca href=\"\/products\/official-raspberry-pi-m2-hat-nvme-ssd-add-on-board-for-pi-5\"\u003eNVMe SSD 256GB\u003c\/a\u003e\u003c\/li\u003e\n    \u003cli\u003e\u003ca href=\"\/products\/raspberry-pi-5-pcie-to-m2-nvme-ssd-expansion-board-by-elecrow\"\u003ePi 5 M.2 HAT+\u003c\/a\u003e\u003c\/li\u003e\n    \u003cli\u003e\u003ca href=\"\/products\/raspberry-pi-4-official-power-supply-5v-3a-usb-c-compoden\"\u003eUSB-C PSU\u003c\/a\u003e\u003c\/li\u003e\n    \u003cli\u003eM-M Wires x20\u003c\/li\u003e\n  \u003c\/ul\u003e\n\u003c\/div\u003e\n\n\u003cscript type=\"application\/ld+json\"\u003e\n{\n  \"@context\": \"https:\/\/schema.org\",\n  \"@type\": \"FAQPage\",\n  \"mainEntity\": [\n    {\n      \"@type\": \"Question\",\n      \"name\": \"What is included in the Pi 5 Smart Grid Demand Forecasting?\",\n      \"acceptedAnswer\": {\n        \"@type\": \"Answer\",\n        \"text\": \"The Pi 5 Smart Grid Demand Forecasting includes all components needed: Raspberry Pi 5 4GB, SCT-013 CT Sensor, ADS1115 ADC, NVMe SSD 256GB, Pi 5 M.2 HAT+ and more. Everything is pre-tested for compatibility and shipped from Bengaluru, India.\"\n      }\n    },\n    {\n      \"@type\": \"Question\",\n      \"name\": \"What skill level is required for the Pi 5 Smart Grid Demand Forecasting?\",\n      \"acceptedAnswer\": {\n        \"@type\": \"Answer\",\n        \"text\": \"This kit is designed for Intermediate level makers, suitable for ages 15-21. LSTM on Pi 5 forecasts next 24-hour electricity demand from historical sensor data - compares Prophet vs LSTM accuracy. Estimated build time is 5-6 hrs.\"\n      }\n    },\n    {\n      \"@type\": \"Question\",\n      \"name\": \"Can I buy the Pi 5 Smart Grid Demand Forecasting online in India?\",\n      \"acceptedAnswer\": {\n        \"@type\": \"Answer\",\n        \"text\": \"Yes, the Pi 5 Smart Grid Demand Forecasting is available online at Compoden (compoden.in), India's AI-powered electronics and robotics store. Ships from Bengaluru in 1-5 business days across India.\"\n      }\n    }\n  ]\n}\n\u003c\/script\u003e\n\n\u003cscript type=\"application\/ld+json\"\u003e\n{\n  \"@context\": \"https:\/\/schema.org\",\n  \"@type\": \"Product\",\n  \"name\": \"Pi 5 Smart Grid Demand Forecasting\",\n  \"description\": \"LSTM on Pi 5 forecasts next 24-hour electricity demand from historical sensor data - compares Prophet vs LSTM accuracy.\",\n  \"sku\": \"CDN-KIT-2321\",\n  \"brand\": {\"@type\": \"Brand\", \"name\": \"Compoden\"},\n  \"offers\": {\n    \"@type\": \"Offer\",\n    \"url\": \"https:\/\/compoden.in\/products\/kit-pi-5-smart-grid-demand-forecasting\",\n    \"priceCurrency\": \"INR\",\n    \"price\": \"25180\",\n    \"availability\": \"https:\/\/schema.org\/InStock\",\n    \"seller\": {\"@type\": \"Organization\", \"name\": \"Compoden\"}\n  },\n  \"category\": \"AI IoT\"\n}\n\u003c\/script\u003e","brand":"Compoden","offers":[{"title":"Default Title","offer_id":53469354590573,"sku":"CDN-KIT-2321","price":29710.0,"currency_code":"INR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0999\/3997\/5533\/files\/kit-pi-5-smart-grid-demand-forecasting.png?v=1781948104","url":"https:\/\/compoden.com\/products\/forecast-electricity-demand-with-lstm-on-pi-5-compoden-kit","provider":"Compoden","version":"1.0","type":"link"}