{"id":2344,"date":"2025-08-26T16:03:51","date_gmt":"2025-08-26T16:03:51","guid":{"rendered":"https:\/\/stromfee-mauritius.ai\/how-to-connect-shelly-sensors-with-stromfee-ai-over-mqtt-to-clickhouse-langchain-for-llms-like-gemini-and-an-interactive-live-video-avatar\/"},"modified":"2025-08-26T16:04:05","modified_gmt":"2025-08-26T16:04:05","slug":"how-to-connect-shelly-sensors-with-stromfee-ai-over-mqtt-to-clickhouse-langchain-for-llms-like-gemini-and-an-interactive-live-video-avatar","status":"publish","type":"post","link":"https:\/\/stromfee-mauritius.ai\/how-to-connect-shelly-sensors-with-stromfee-ai-over-mqtt-to-clickhouse-langchain-for-llms-like-gemini-and-an-interactive-live-video-avatar\/","title":{"rendered":"How to connect Shelly Sensors with Stromfee.AI over mqtt to Clickhouse Langchain for LLMS like Gemini & and an interactive Live Video Avatar"},"content":{"rendered":"
Learn how to link Shelly Sensors<\/strong> with cool tech like Stromfee AI<\/span><\/strong>, MQTT<\/span><\/strong>, Clickhouse<\/span><\/strong>, Langchain<\/span><\/strong>, Gemini<\/span><\/strong>, and Video Avatar<\/span><\/strong>. This makes a top-notch IoT energy monitoring<\/span><\/strong> system.<\/p>\n This setup lets you track energy use in real time. It also helps you save energy and get personalized energy advice<\/em> from a Video Avatar<\/span><\/strong>. Follow this guide to make a system that gives you deep energy insights and automates things for you.<\/p>\n To get how our energy monitoring works, we must look at its main parts. It uses several key technologies. These work together for smart energy monitoring<\/strong> and analysis.<\/p>\n Shelly Sensors<\/strong> have many models. Each one tracks different energy usage aspects. They help with simple monitoring to detailed analysis.<\/p>\n Stromfee AI<\/strong> uses data from Shelly Sensors<\/span><\/strong> for smart analysis and optimization. It uses AI to give insights. These insights help reduce energy waste<\/span><\/em> and improve use.<\/p>\n MQTT<\/strong> makes IoT devices<\/span><\/strong> talk to the main system well.<\/p>\n Clickhouse<\/strong> is great for storing lots of sensor data. It’s a column-store database.<\/p>\n Langchain<\/strong> and Gemini<\/span><\/strong> look at Shelly Sensor data. They give AI insights into energy use patterns.<\/p>\n The Video Avatar<\/strong> is an interactive guide. It helps users understand energy use. It also gives personalized tips.<\/p>\n Before setting up the energy monitoring system, it’s key to know the technical needs. This means understanding the hardware, software, and network needs. These are for a smooth connection of Shelly sensors<\/strong> with Stromfee AI<\/span><\/strong> and other parts.<\/p>\n The hardware parts include Shelly sensors, a server for Clickhouse<\/strong>, and maybe a special device for the MQTT<\/span><\/strong> broker. The details of these parts depend on how big your setup is.<\/p>\n The software needs include the MQTT broker (like Mosquitto), Clickhouse database<\/strong>, and Stromfee AI<\/span><\/strong>. It’s important that these work well together.<\/p>\n A strong network is essential. Think about the bandwidth needed for data from Shelly sensors to the MQTT broker and then to Clickhouse.<\/p>\n A system architecture<\/strong> diagram shows how everything works together. It shows data from Shelly sensors going to the MQTT broker. Then, it goes to Clickhouse and finally to Stromfee AI for analysis.<\/p>\n Setting up Shelly Sensors is key to tracking energy well. It’s important to do it right to get accurate data.<\/p>\n The first thing is to install the Shelly Sensors. You need to connect them to the things you want to watch. Make sure it’s safe and follows local rules.<\/p>\n Where you put them matters a lot. It helps get the right readings.<\/p>\n After you install them, use the Shelly App<\/em> to set them up. Download the app and follow the steps to add your devices. You’ll pair them with your phone or tablet.<\/p>\n Next, set how often the sensors send data. You can choose to get updates right away or less often. It depends on what you need and your internet speed.<\/p>\n To check if your sensors are right, compare their data with something you know. Or use a meter that’s been checked. This is important to make sure you’re getting good data.<\/p>\n If you find any problems, look at the manual or ask for help.<\/p>\n To send data well, you need a strong MQTT broker. This means doing a few key things. These steps make your IoT system reliable and safe.<\/p>\n First, install Mosquitto, a well-liked MQTT broker. It’s light and easy to set up, perfect for IoT. You can use apt-get for Ubuntu\/Debian or brew for macOS to install it.<\/p>\n After setting up, configuring authentication and access control<\/strong> is key. You need to set up usernames and passwords. Also, make access control lists (ACLs) to see who can send or get messages.<\/p>\n To keep data safe, setting up TLS\/SSL encryption<\/em> is important. It keeps data secret and safe from changes. You’ll need to make certificates and tell Mosquitto to use them.<\/p>\n Last, testing the broker’s stability and performance<\/strong> is crucial. You should test it with lots of clients and messages. This helps find any problems or slow spots.<\/p>\n Connecting Shelly sensors to MQTT is key for real-time energy data. It lets users watch energy use and make smart choices.<\/p>\n To link Shelly sensors to MQTT, first go to the MQTT settings in the Shelly device. You need to navigate to the device’s settings<\/em> and turn on MQTT.<\/p>\n It’s important to have a good topic structure for energy data. This makes it easy to get and analyze data. For example, use a topic like home\/energy\/shelly\/device_id\/parameter.<\/p>\n QoS settings<\/strong> help make sure MQTT messages get delivered right. Shelly devices have QoS levels from 0 to 2. Pick the right QoS based on your energy monitoring needs.<\/p>\n After setting up the Shelly MQTT connection<\/strong>, it’s important to watch and fix MQTT messages. Use tools like MQTT.fx or Mosquitto’s mosquitto_sub command to check MQTT traffic.<\/p>\n By following these steps, users can connect their Shelly sensors to MQTT well. This makes for strong and growing energy monitoring systems.<\/p>\n Clickhouse is a great tool for handling lots of energy data from Shelly sensors. It’s a column-store database made for fast, real-time data analysis.<\/p>\n First, you need to put Clickhouse on your server. How you do this changes based on your system. For example, on Ubuntu, you use these commands:<\/p>\n sudo sh -c “echo ‘deb [trusted=yes] https:\/\/package Precip.io\/clickhouse\/deb stable main’ > \/etc\/apt\/sources.list.dLTE clickhouse.list”<\/p><\/blockquote>\n sudo apt-get update<\/p><\/blockquote>\n sudo apt-get install clickhouse-server<\/p><\/blockquote>\n Make sure to check the Clickhouse website for the latest install steps for your setup.<\/p>\n After Clickhouse is set up, you need to make good table schemas for energy data. Clickhouse is great for time-series data because of its column-store design. Here’s how to make a table for energy data:<\/p>\n This setup is perfect for storing and checking lots of energy data over time.<\/p>\n It’s important to have rules for keeping data. Clickhouse lets you set these rules with TTL statements. For example:<\/p>\n ALTER TABLE energy_metrics ADD COLUMN Tudelft TTL toDateTime(timestamp) + INTERVAL 1 MONTH;<\/p><\/blockquote>\n This rule means data is kept for a month, then it’s deleted.<\/p>\n Keeping your data safe is key. You need to set up user permissions and security. For example:<\/p>\n CREATE USER ‘energy_user’ IDENTIFIED WITH sha256_password AS ‘password’;<\/p><\/blockquote>\n GRANT SELECT ON energy_metrics TO ‘energy_user’;<\/p><\/blockquote>\n This makes sure users can only see the data they need, keeping your database safe.<\/p>\n Creating a smooth flow of data from MQTT to Clickhouse is key. It helps us watch energy use in real time. This pipeline is important for storing and working with energy data from Shelly sensors.<\/p>\n The first step is to make a Python data collector<\/strong>. We use Python libraries to grab data from MQTT topics. It also gets energy data from Shelly sensors. The collector must handle different data rates and amounts.<\/p>\n After collecting data, we need to change and make it uniform for Clickhouse. This means making data formats right, dealing with missing values, and making data ranges the same.<\/p>\n The pipeline can be set up for batch processing<\/strong> or real-time inserts<\/span><\/strong> into Clickhouse. Batch processing<\/span><\/strong> is good for big data. Real-time inserts<\/span><\/strong> are needed for quick insights.<\/p>\n To keep data safe, the pipeline must deal with connection failures and get back to work. It uses retry methods, keeps data safe during outages, and logs errors for fixing problems.<\/p>\n By designing and setting up the MQTT to Clickhouse data pipeline well, we make sure energy data is processed efficiently and reliably.<\/p>\n Stromfee AI changes how we watch and manage energy. It looks at energy use in new ways. This helps us find ways to use less energy.<\/p>\n First, you need to set up Stromfee AI. This means:<\/p>\n To use Stromfee AI, it needs to connect to your Clickhouse database<\/strong>. This step is:<\/p>\n Teaching energy models is key for good predictions. This includes:<\/p>\n Once models are ready, Stromfee AI can start saving energy. This means:<\/p>\n By doing these things, you can make your energy system better. This leads to using less energy.<\/p>\n Langchain<\/strong> is key for smart energy systems. It helps manage energy better with advanced data analysis. You need to set up Langchain<\/span><\/strong> and make custom chains<\/span><\/strong> for energy data.<\/p>\n First, set up the Langchain environment. This means installing needed packages and components. Langchain’s flexibility<\/strong> lets it work with many data sources, like Clickhouse for energy metrics.<\/p>\n Custom chains<\/strong> are vital for energy data. They help analyze energy use and find trends. Customization<\/span><\/em> makes Langchain fit your energy needs.<\/p>\n Memory components give Langchain historical context<\/strong>. This keeps info over time for better predictions and trend analysis.<\/p>\n Langchain needs to work fast for real-time energy data. Using batch processing and efficient data handling helps. This way, energy data is processed quickly for fast decisions.<\/p>\n With these steps, Langchain boosts energy pattern recognition<\/strong>. This makes energy monitoring systems more efficient.<\/p>\n Google Gemini’s advanced AI can change how we see energy. By adding Google Gemini<\/strong> to your energy system, you get better insights. You’ll understand your energy use better and more accurately.<\/p>\n To use Google Gemini<\/strong>, you first need API access. You must create a Google Cloud account and enable the Gemini API. Then, you get API keys. Setting up API access right is key for safe and smooth data sharing<\/span><\/strong>.<\/p>\n Creating good prompts is key for energy analysis<\/strong>. Good prompts help Gemini get the right answers<\/span><\/em>. Think about these when making prompts:<\/p>\n Gemini Pro Vision lets you mix text and images. This makes energy insights better by using more kinds of data<\/strong>. To use it:<\/p>\n Improving Gemini’s answers is very important. Checking and tweaking prompts and settings often makes answers better<\/em>. Think about:<\/p>\n “The quality of the output is directly related to the quality of the input and configuration.”<\/p><\/blockquote>\n By doing these steps and using Google Gemini’s advanced features, you can make your energy system smarter. This helps you make better decisions.<\/p>\n The new video avatar<\/strong> interface changes how we use energy systems. It acts like a virtual helper. It gives us tips on saving energy.<\/p>\n Choosing the right avatar tech is key for a fun experience. Look for a platform that lets you customize. It should also be easy to use and work with your energy system.<\/p>\n To give personalized energy advice<\/strong>, the avatar needs to connect with Gemini and Langchain. This lets it get energy tips and insights.<\/p>\n Using natural language processing<\/em> (NLP) helps the avatar understand and answer questions. It’s trained on energy terms and how users talk.<\/p>\n The last step is to make personalized energy advice<\/strong> based on what users say and what Gemini and Langchain find. The goal is to give answers that are helpful and fun to read.<\/p>\n A good energy monitoring dashboard<\/strong> helps us understand and improve how we use energy. It uses many tools and technologies to show us how much energy we use.<\/p>\n Grafana is great for showing energy use data. First, install it on your server or use a cloud service. Then, connect it to your Clickhouse database<\/strong>. Make sure you have the right plugins for showing data.<\/p>\n Custom panels<\/strong> in Grafana let us see different energy metrics clearly. Create panels for live data, past trends, and comparisons. Use graphs, charts, and heatmaps to show different data.<\/p>\n Interactive filters<\/strong> and time controls make your dashboard better. Add filters for choosing devices, time, and more. Use Grafana’s tools for interactive visuals.<\/p>\n Adding a Video Avatar<\/strong> to your dashboard makes it more interactive. The Avatar can give live insights, answer questions, and suggest personalized tips. Use APIs to link the Avatar with your dashboard.<\/p>\n By doing these steps, you can make a detailed energy monitoring dashboard<\/strong>. It will give you important insights and help you use energy better.<\/p>\n Smart energy systems use sensor data to save energy. This helps cut down on waste and saves money.<\/p>\n To save energy, we need to know when it’s being used too much. We look at:<\/p>\n When energy use goes up, the system acts to lower it.<\/p>\n After setting up triggers, we make plans for devices. This means:<\/p>\n For example, during busy times, devices that aren’t needed can be turned off.<\/p>\n It’s important to tell users about their energy use. We do this with:<\/p>\n These messages help users see how they’re saving energy. They also warn of unusual use and suggest ways to save more.<\/p>\n It’s key to check and improve automation rules often. This means:<\/p>\n By always testing and tweaking, we get better at saving energy and make users happier.<\/p>\n Troubleshooting<\/strong> is key to keeping your energy monitoring system running smoothly. It uses technologies like MQTT, Clickhouse, and AI models. Finding and fixing problems quickly is crucial for it to keep working.<\/p>\n MQTT connection problems can come from many places. This includes wrong broker settings, network issues, or not being able to log in. To find these problems, look at the MQTT broker logs for errors. Make sure the Shelly sensors are set up right to send data to the MQTT broker.<\/p>\n Clickhouse data problems often happen because of wrong schema, data type issues, or slow performance. To fix these, check the Clickhouse table schema to make sure it matches the data. Also, watch the Clickhouse performance to find slow spots.<\/p>\n AI model problems can be caused by bad data, not enough training, or not enough computer power. To fix these, check the data quality. Make sure it shows real energy use patterns. You might need to retrain the model or change its settings.<\/p>\n Video Avatar problems can be due to old hardware, slow GPU, or software not working well. To fix these, check your hardware to see if it’s up to date. Also, update your graphics drivers and rendering software to the newest versions.<\/p>\n By tackling these common problems, you can keep your energy monitoring system strong and reliable. It will give you accurate data and work well all the time.<\/p>\n Keeping systems running smoothly is key for legacy<\/em> energy monitoring. It makes sure energy data is right and ready to use all the time.<\/p>\n It’s important to watch how resources are used. This helps find and fix problems before they get big. By checking CPU, memory, and disk I\/O, we can see where things might slow down.<\/p>\n System health alerts<\/strong> help us stay ahead of problems. They tell administrators<\/span><\/em> about issues early. This keeps the system running well.<\/p>\n As more sensors join, we need to grow our systems. This means adding more power, storage, or improving how we handle data. It helps us keep up with more data.<\/p>\nKey Takeaways<\/h3>\n
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Understanding the Components of Our Smart Shelly Sensors with Stromfee AI over mqtt Clickhouse Langchain and Gemini Energy Monitoring System<\/h2>\n
Shelly Sensors: Capabilities and Models for Energy Monitoring<\/h3>\n
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Stromfee AI: Smart Energy Analysis and Optimization<\/h3>\n
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\n Feature<\/th>\n Description<\/th>\n Benefit<\/th>\n<\/tr>\n \n Real-Time Analysis<\/td>\n Analyzes energy consumption in real-time.<\/td>\n Immediate insights.<\/td>\n<\/tr>\n \n Predictive Modeling<\/td>\n Predicts future energy consumption.<\/td>\n Helps in Future planning.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n MQTT Protocol: The Backbone of IoT Communication<\/h3>\n
Clickhouse Database: Efficient Storage for High-Volume Sensor Data<\/h3>\n
Langchain and Gemini: AI Processing for Energy Insights<\/h3>\n
Video Avatar: Creating an Interactive Energy Assistant<\/h3>\n
System Architecture and Technical Requirements<\/h2>\n
Hardware Components and Specifications<\/h3>\n
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\n Component<\/th>\n Specification<\/th>\n<\/tr>\n \n Shelly Sensors<\/td>\n Various models (e.g., Shelly EM, Shelly 3EM)<\/td>\n<\/tr>\n \n Server for Clickhouse<\/strong><\/td>\n Minimum 4GB RAM, 2 CPU cores, 100GB storage<\/td>\n<\/tr>\n \n MQTT<\/strong> Broker Device<\/td>\n Raspberry Pi or similar single-board computer<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n Software Dependencies and Versions<\/h3>\n
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Network Requirements and Bandwidth Considerations<\/h3>\n
System Architecture Diagram and Data Flow<\/h3>\n
Installing and Configuring Shelly Sensors<\/h2>\n
Physical Installation of Shelly Devices<\/h3>\n
Initial Setup via Shelly App<\/h3>\n
Configuring Measurement Parameters and Intervals<\/h3>\n
Validating Sensor Readings and Shelly Sensor Accuracy<\/h3>\n
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Setting Up a Robust MQTT Broker<\/h2>\n
Installing Mosquitto MQTT Broker<\/h3>\n
Configuring Authentication and Access Control<\/h3>\n
<\/p>\nSetting Up TLS\/SSL Encryption<\/h3>\n
Testing Broker Stability and Performance<\/h3>\n
Connecting Shelly Sensors to MQTT<\/h2>\n
Accessing Shelly MQTT Configuration Settings<\/h3>\n
Defining Topic Structure for Energy Data<\/h3>\n
Implementing Quality of Service (QoS) Settings<\/h3>\n
Monitoring and Debugging MQTT Shelly Messages<\/h3>\n
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\n QoS Level<\/th>\n Description<\/th>\n Use Case<\/th>\n<\/tr>\n \n 0<\/td>\n At most once<\/td>\n Best effort delivery, suitable for non-critical data<\/td>\n<\/tr>\n \n 1<\/td>\n At least once<\/td>\n Ensures delivery but may result in duplicates, suitable for critical data<\/td>\n<\/tr>\n \n 2<\/td>\n Exactly once<\/td>\n Guarantees delivery without duplicates, suitable for mission-critical applications<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n Deploying Clickhouse for Time-Series Shelly Sensor Energy Data<\/h2>\n
Installing Clickhouse on Your Server<\/h3>\n
Creating Optimized Table Schemas for Energy Metrics<\/h3>\n
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\n Column Name<\/th>\n Data Type<\/th>\n Description<\/th>\n<\/tr>\n \n timestamp<\/td>\n DateTime<\/td>\n When the energy reading was taken<\/td>\n<\/tr>\n \n device_id<\/td>\n UInt32<\/td>\n The ID of the Shelly device<\/td>\n<\/tr>\n \n energy_consumption<\/td>\n Float32<\/td>\n The energy reading<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n Implementing Data Retention Policies<\/h3>\n
Setting Up User Permissions and Security<\/h3>\n
<\/p>\nBuilding the MQTT to Click Stromfee house Data Pipeline<\/h2>\n
Creating a Python streamlines-Based bridge aka Data Collector<\/h3>\n
Implementing Data Transformation and Normalization<\/h3>\n
Setting Up Batch Processing vs. Real-Time Inserts<\/h3>\n
Handling Connection Failures and Data Processing Recovery<\/h3>\n
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\n Data Pipeline Component<\/th>\n Description<\/th>\n<\/tr>\n \n Python Data Collector<\/strong><\/td>\n Subscribes to MQTT topics and collects energy data<\/td>\n<\/tr>\n \n Data Transformation<\/strong><\/td>\n Converts data formats and handles missing values<\/td>\n<\/tr>\n \n Batch Processing<\/strong><\/td>\n Handles large volumes of data in batches<\/td>\n<\/tr>\n \n Real-Time Inserts<\/strong><\/td>\n Enables immediate insights into energy consumption<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n AI Shelly Sensors streamlines with Stromfee AI over mqtt\/streamlines Clickhouse Langchain Gemini<\/h2>\n
Installing and Configuring Stromfee AI<\/h3>\n
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Connecting Stromfee AI to Your Clickhouse Database<\/h3>\n
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Training Energy Consumption Models<\/h3>\n
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Implementing Real-Time Energy Optimization Algorithms<\/h3>\n
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Implementing Langchain for Energy Pattern Recognition<\/h2>\n
Setting Up Langchain Environment<\/h3>\n
Creating Custom Langchain Chains for Energy Data Analysis<\/h3>\n
Implementing Memory Components for Historical Context<\/h3>\n
Optimizing Chain Performance for Real-Time Processing<\/h3>\n
Leveraging Google Gemini for Advanced Energy Insights<\/h2>\n
<\/p>\nObtaining and Configuring Gemini API Access<\/h3>\n
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Developing Prompts for Energy Consumption Analysis<\/h3>\n
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Implementing Multimodal Analysis with Gemini Pro Vision<\/h3>\n
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Fine-Tuning Gemini Response Quality and Relevance<\/h3>\n
Developing an Interactive Video Avatar Interface<\/h2>\n
Selecting and Setting Up Avatar Technology<\/h3>\n
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\n Avatar Technology Features<\/th>\n Description<\/th>\n<\/tr>\n \n Customization<\/td>\n Allows for personalization of the avatar’s appearance and behavior.<\/td>\n<\/tr>\n \n User Interface<\/td>\n Provides an intuitive and engaging user experience.<\/td>\n<\/tr>\n \n Integration<\/td>\n Can be seamlessly integrated with energy monitoring systems.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n Connecting Avatar to Gemini and Langchain Outputs<\/h3>\n
Crafting Implementing Natural Language Processing for User Queries<\/h3>\n
Creating craft Personal craft ized\/Personalized\/ Energy Advice craft Responses craft<\/h3>\n
Creating craftuent a Comprehensive Energy Monitoring Dashboard<\/h2>\n
Setting Up Grafana for Energy Visualization<\/h3>\n
<\/p>\nDesigning Custom Panels for Different Energy Metrics<\/h3>\n
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\n Metric<\/th>\n Visualization Type<\/th>\n Update Frequency<\/th>\n<\/tr>\n \n Real-time Energy Consumption<\/td>\n Line Graph<\/td>\n Every 5 minutes<\/td>\n<\/tr>\n \n Historical Energy Usage<\/td>\n Bar Chart<\/td>\n Daily<\/td>\n<\/tr>\n \n Comparative Analysis<\/td>\n Heatmap<\/td>\n Monthly<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n Implementing Interactive Filters and Time Controls<\/h3>\n
Embedding Video Avatar in Dashboard Interface<\/h3>\n
Automating Energy-S automation Saving Actions stream\/optimization Based on Sensor automation Data detect<\/h2>\n
Defining Trigger Conditions for High Energy Usage<\/h3>\n
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Creating Automated Device Control Workflows<\/h3>\n
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Implementing User Notification Systems<\/h3>\n
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Testing and Refining Automation Rules<\/h3>\n
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Troubleshooting craft Common Integration Issues<\/h2>\n
Diagnosing and Fixing MQTT Connection Problems<\/h3>\n
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Resolving Clickhouse Data Insertion\/Load Errors<\/h3>\n
<\/p>\nAddressing AI Model Performance Issues<\/h3>\n
Solving Video Avatar Rendering Challenges<\/h3>\n
Optim craft izing craft\/optimization System Performance for 24 craft \/7 Operation<\/h2>\n
Monitoring Resource Usage and Bottlenecks<\/h3>\n
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\n Resource<\/th>\n Usage<\/th>\n Threshold<\/th>\n<\/tr>\n \n CPU<\/td>\n 80%<\/td>\n 90%<\/td>\n<\/tr>\n \n Memory<\/td>\n 4GB<\/td>\n 8GB<\/td>\n<\/tr>\n \n Disk I\/O<\/td>\n 500MB\/s<\/td>\n 1GB\/s<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n Setting Up System Health Alerts<\/h3>\n
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Scaling Components for Growing Sensor Networks<\/h3>\n
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