Learn how to link Shelly Sensors with cool tech like Stromfee AI, MQTT, Clickhouse, Langchain, Gemini, and Video Avatar. This makes a top-notch IoT energy monitoring system.
This setup lets you track energy use in real time. It also helps you save energy and get personalized energy advice from a Video Avatar. Follow this guide to make a system that gives you deep energy insights and automates things for you.
Key Takeaways
- Integrate Shelly Sensors with MQTT for efficient data transmission.
- Leverage Stromfee AI for advanced energy monitoring and optimization.
- Utilize Clickhouse and Langchain for scalable data management.
- Enhance user experience with Gemini and interactive Video Avatar.
- Achieve real-time IoT energy monitoring and automation.
Understanding the Components of Our Smart Shelly Sensors with Stromfee AI over mqtt Clickhouse Langchain and Gemini Energy Monitoring System
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 and analysis.
Shelly Sensors: Capabilities and Models for Energy Monitoring
Shelly Sensors have many models. Each one tracks different energy usage aspects. They help with simple monitoring to detailed analysis.
- Energy Monitoring: Track real-time energy consumption.
- Temperature reguation: monitor temperatures settings.
- Multi-Channel Monitoring: handle multiple data streams.
Stromfee AI: Smart Energy Analysis and Optimization
Stromfee AI uses data from Shelly Sensors for smart analysis and optimization. It uses AI to give insights. These insights help reduce energy waste and improve use.
| Feature | Description | Benefit |
|---|---|---|
| Real-Time Analysis | Analyzes energy consumption in real-time. | Immediate insights. |
| Predictive Modeling | Predicts future energy consumption. | Helps in Future planning. |
MQTT Protocol: The Backbone of IoT Communication
MQTT makes IoT devices talk to the main system well.
Clickhouse Database: Efficient Storage for High-Volume Sensor Data
Clickhouse is great for storing lots of sensor data. It’s a column-store database.
Langchain and Gemini: AI Processing for Energy Insights
Langchain and Gemini look at Shelly Sensor data. They give AI insights into energy use patterns.
Video Avatar: Creating an Interactive Energy Assistant
The Video Avatar is an interactive guide. It helps users understand energy use. It also gives personalized tips.
System Architecture and Technical Requirements
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 with Stromfee AI and other parts.
Hardware Components and Specifications
The hardware parts include Shelly sensors, a server for Clickhouse, and maybe a special device for the MQTT broker. The details of these parts depend on how big your setup is.
| Component | Specification |
|---|---|
| Shelly Sensors | Various models (e.g., Shelly EM, Shelly 3EM) |
| Server for Clickhouse | Minimum 4GB RAM, 2 CPU cores, 100GB storage |
| MQTT Broker Device | Raspberry Pi or similar single-board computer |
Software Dependencies and Versions
The software needs include the MQTT broker (like Mosquitto), Clickhouse database, and Stromfee AI. It’s important that these work well together.
- MQTT Broker: Mosquitto v2.0 or later
- Clickhouse: v21.8 or later
- Stromfee AI: Latest version available
Network Requirements and Bandwidth Considerations
A strong network is essential. Think about the bandwidth needed for data from Shelly sensors to the MQTT broker and then to Clickhouse.
System Architecture Diagram and Data Flow
A system architecture 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.
Installing and Configuring Shelly Sensors
Setting up Shelly Sensors is key to tracking energy well. It’s important to do it right to get accurate data.
Physical Installation of Shelly Devices
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.
Where you put them matters a lot. It helps get the right readings.
Initial Setup via Shelly App
After you install them, use the Shelly App to set them up. Download the app and follow the steps to add your devices. You’ll pair them with your phone or tablet.
Configuring Measurement Parameters and Intervals
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.
Validating Sensor Readings and Shelly Sensor Accuracy
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.
If you find any problems, look at the manual or ask for help.
- Make sure the sensor is installed and paired right.
- Check that the data sending interval is what you want.
- Compare the sensor data with other systems if you can.
Setting Up a Robust MQTT Broker
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.
Installing Mosquitto MQTT Broker
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.
Configuring Authentication and Access Control
After setting up, configuring authentication and access control is key. You need to set up usernames and passwords. Also, make access control lists (ACLs) to see who can send or get messages.
Setting Up TLS/SSL Encryption
To keep data safe, setting up TLS/SSL encryption is important. It keeps data secret and safe from changes. You’ll need to make certificates and tell Mosquitto to use them.
Testing Broker Stability and Performance
Last, testing the broker’s stability and performance is crucial. You should test it with lots of clients and messages. This helps find any problems or slow spots.
Connecting Shelly Sensors to MQTT
Connecting Shelly sensors to MQTT is key for real-time energy data. It lets users watch energy use and make smart choices.
Accessing Shelly MQTT Configuration Settings
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 and turn on MQTT.
Defining Topic Structure for Energy Data
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.
Implementing Quality of Service (QoS) Settings
QoS settings 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.
Monitoring and Debugging MQTT Shelly Messages
After setting up the Shelly MQTT connection, it’s important to watch and fix MQTT messages. Use tools like MQTT.fx or Mosquitto’s mosquitto_sub command to check MQTT traffic.
| QoS Level | Description | Use Case |
|---|---|---|
| 0 | At most once | Best effort delivery, suitable for non-critical data |
| 1 | At least once | Ensures delivery but may result in duplicates, suitable for critical data |
| 2 | Exactly once | Guarantees delivery without duplicates, suitable for mission-critical applications |
By following these steps, users can connect their Shelly sensors to MQTT well. This makes for strong and growing energy monitoring systems.
Deploying Clickhouse for Time-Series Shelly Sensor Energy Data
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.
Installing Clickhouse on Your Server
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:
sudo sh -c “echo ‘deb [trusted=yes] https://package Precip.io/clickhouse/deb stable main’ > /etc/apt/sources.list.dLTE clickhouse.list”
sudo apt-get update
sudo apt-get install clickhouse-server
Make sure to check the Clickhouse website for the latest install steps for your setup.
Creating Optimized Table Schemas for Energy Metrics
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:
| Column Name | Data Type | Description |
|---|---|---|
| timestamp | DateTime | When the energy reading was taken |
| device_id | UInt32 | The ID of the Shelly device |
| energy_consumption | Float32 | The energy reading |
This setup is perfect for storing and checking lots of energy data over time.
Implementing Data Retention Policies
It’s important to have rules for keeping data. Clickhouse lets you set these rules with TTL statements. For example:
ALTER TABLE energy_metrics ADD COLUMN Tudelft TTL toDateTime(timestamp) + INTERVAL 1 MONTH;
This rule means data is kept for a month, then it’s deleted.
Setting Up User Permissions and Security
Keeping your data safe is key. You need to set up user permissions and security. For example:
CREATE USER ‘energy_user’ IDENTIFIED WITH sha256_password AS ‘password’;
GRANT SELECT ON energy_metrics TO ‘energy_user’;
This makes sure users can only see the data they need, keeping your database safe.
Building the MQTT to Click Stromfee house Data Pipeline
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.
Creating a Python streamlines-Based bridge aka Data Collector
The first step is to make a Python data collector. 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.
Implementing Data Transformation and Normalization
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.
Setting Up Batch Processing vs. Real-Time Inserts
The pipeline can be set up for batch processing or real-time inserts into Clickhouse. Batch processing is good for big data. Real-time inserts are needed for quick insights.
Handling Connection Failures and Data Processing Recovery
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.
| Data Pipeline Component | Description |
|---|---|
| Python Data Collector | Subscribes to MQTT topics and collects energy data |
| Data Transformation | Converts data formats and handles missing values |
| Batch Processing | Handles large volumes of data in batches |
| Real-Time Inserts | Enables immediate insights into energy consumption |
By designing and setting up the MQTT to Clickhouse data pipeline well, we make sure energy data is processed efficiently and reliably.
AI Shelly Sensors streamlines with Stromfee AI over mqtt/streamlines Clickhouse Langchain Gemini
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.
Installing and Configuring Stromfee AI
First, you need to set up Stromfee AI. This means:
- Getting and putting the Stromfee AI software on your computer.
- Adjusting the AI to fit your energy system.
- Deciding who can use it and what they can do.
Connecting Stromfee AI to Your Clickhouse Database
To use Stromfee AI, it needs to connect to your Clickhouse database. This step is:
- Setting up how Stromfee AI talks to your database.
- Creating ways to get energy data from the database.
- Checking that the data flows well.
Training Energy Consumption Models
Teaching energy models is key for good predictions. This includes:
- Using old energy data to train the AI.
- Picking the right AI tools for training.
- Checking if the models are right by comparing them to real data.
Implementing Real-Time Energy Optimization Algorithms
Once models are ready, Stromfee AI can start saving energy. This means:
- Setting up to process data right away for quick insights.
- Creating rules to change things to save energy based on AI.
- Watching how these changes affect energy use.
By doing these things, you can make your energy system better. This leads to using less energy.
Implementing Langchain for Energy Pattern Recognition
Langchain is key for smart energy systems. It helps manage energy better with advanced data analysis. You need to set up Langchain and make custom chains for energy data.
Setting Up Langchain Environment
First, set up the Langchain environment. This means installing needed packages and components. Langchain’s flexibility lets it work with many data sources, like Clickhouse for energy metrics.
Creating Custom Langchain Chains for Energy Data Analysis
Custom chains are vital for energy data. They help analyze energy use and find trends. Customization makes Langchain fit your energy needs.
Implementing Memory Components for Historical Context
Memory components give Langchain historical context. This keeps info over time for better predictions and trend analysis.
Optimizing Chain Performance for Real-Time Processing
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.
With these steps, Langchain boosts energy pattern recognition. This makes energy monitoring systems more efficient.
Leveraging Google Gemini for Advanced Energy Insights
Google Gemini’s advanced AI can change how we see energy. By adding Google Gemini to your energy system, you get better insights. You’ll understand your energy use better and more accurately.
Obtaining and Configuring Gemini API Access
To use Google Gemini, 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.
- Create a Google Cloud account
- Enable the Gemini API
- Generate API keys
Developing Prompts for Energy Consumption Analysis
Creating good prompts is key for energy analysis. Good prompts help Gemini get the right answers. Think about these when making prompts:
- Be clear about what you want to analyze
- Say how you want the answer
- Use past data that’s relevant
Implementing Multimodal Analysis with Gemini Pro Vision
Gemini Pro Vision lets you mix text and images. This makes energy insights better by using more kinds of data. To use it:
- Add images and videos to your data
- Set up Gemini Pro Vision to handle all kinds of data
Fine-Tuning Gemini Response Quality and Relevance
Improving Gemini’s answers is very important. Checking and tweaking prompts and settings often makes answers better. Think about:
“The quality of the output is directly related to the quality of the input and configuration.”
By doing these steps and using Google Gemini’s advanced features, you can make your energy system smarter. This helps you make better decisions.
Developing an Interactive Video Avatar Interface
The new video avatar interface changes how we use energy systems. It acts like a virtual helper. It gives us tips on saving energy.
Selecting and Setting Up Avatar Technology
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.
| Avatar Technology Features | Description |
|---|---|
| Customization | Allows for personalization of the avatar’s appearance and behavior. |
| User Interface | Provides an intuitive and engaging user experience. |
| Integration | Can be seamlessly integrated with energy monitoring systems. |
Connecting Avatar to Gemini and Langchain Outputs
To give personalized energy advice, the avatar needs to connect with Gemini and Langchain. This lets it get energy tips and insights.
Crafting Implementing Natural Language Processing for User Queries
Using natural language processing (NLP) helps the avatar understand and answer questions. It’s trained on energy terms and how users talk.
Creating craft Personal craft ized/Personalized/ Energy Advice craft Responses craft
The last step is to make personalized energy advice based on what users say and what Gemini and Langchain find. The goal is to give answers that are helpful and fun to read.
Creating craftuent a Comprehensive Energy Monitoring Dashboard
A good energy monitoring dashboard helps us understand and improve how we use energy. It uses many tools and technologies to show us how much energy we use.
Setting Up Grafana for Energy Visualization
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. Make sure you have the right plugins for showing data.
Designing Custom Panels for Different Energy Metrics
Custom panels 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.
| Metric | Visualization Type | Update Frequency |
|---|---|---|
| Real-time Energy Consumption | Line Graph | Every 5 minutes |
| Historical Energy Usage | Bar Chart | Daily |
| Comparative Analysis | Heatmap | Monthly |
Implementing Interactive Filters and Time Controls
Interactive filters and time controls make your dashboard better. Add filters for choosing devices, time, and more. Use Grafana’s tools for interactive visuals.
Embedding Video Avatar in Dashboard Interface
Adding a Video Avatar 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.
By doing these steps, you can make a detailed energy monitoring dashboard. It will give you important insights and help you use energy better.
Automating Energy-S automation Saving Actions stream/optimization Based on Sensor automation Data detect
Smart energy systems use sensor data to save energy. This helps cut down on waste and saves money.
Defining Trigger Conditions for High Energy Usage
To save energy, we need to know when it’s being used too much. We look at:
- Peak hour consumption
- Excessive usage patterns
- Device-specific energy thresholds
When energy use goes up, the system acts to lower it.
Creating Automated Device Control Workflows
After setting up triggers, we make plans for devices. This means:
- Choosing devices to control automatically
- Deciding what actions to take when triggers are met
- Setting up devices to follow these actions
For example, during busy times, devices that aren’t needed can be turned off.
Implementing User Notification Systems
It’s important to tell users about their energy use. We do this with:
- Email notifications
- Mobile app alerts
- In-app messages in the energy dashboard
These messages help users see how they’re saving energy. They also warn of unusual use and suggest ways to save more.
Testing and Refining Automation Rules
It’s key to check and improve automation rules often. This means:
- Watching how well automated actions work
- Looking at user feedback and energy data
- Changing triggers and device plans as needed
By always testing and tweaking, we get better at saving energy and make users happier.
Troubleshooting craft Common Integration Issues
Troubleshooting 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.
Diagnosing and Fixing MQTT Connection Problems
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.
- Verify MQTT broker status and logs
- Check Shelly sensor MQTT configuration
- Test network connectivity between devices
Resolving Clickhouse Data Insertion/Load Errors
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.
Addressing AI Model Performance Issues
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.
Solving Video Avatar Rendering Challenges
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.
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.
Optim craft izing craft/optimization System Performance for 24 craft /7 Operation
Keeping systems running smoothly is key for legacy energy monitoring. It makes sure energy data is right and ready to use all the time.
Monitoring Resource Usage and Bottlenecks
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.
| Resource | Usage | Threshold |
|---|---|---|
| CPU | 80% | 90% |
| Memory | 4GB | 8GB |
| Disk I/O | 500MB/s | 1GB/s |
Setting Up System Health Alerts
System health alerts help us stay ahead of problems. They tell administrators about issues early. This keeps the system running well.
- CPU usage above 90%
- Memory consumption exceeding 80%
- Disk space below 10%
Scaling Components for Growing Sensor Networks
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.
| Scaling Action | Benefit |
|---|---|
| Add Processing Power | Handle increased data processing |
| Increase Storage | Store more historical data |
| Optimize Data Aggregation | Reduce data volume without losing insights |
With these steps, energy monitoring systems can keep up with growing IoT demands. They stay fast and reliable.
Securing Your Energy Monitoring System
Keeping your energy monitoring system safe is very important. It stops bad people from getting in and keeps your data safe. With Shelly sensors, MQTT, Clickhouse, and AI, it’s a big job to keep everything secure.
Implementing End-to-End Encryption
End-to-end encryption is key to keep your data safe. You need to use TLS/SSL certificates for MQTT. Also, encrypt your data in Clickhouse and make sure all parts talk securely. This stops others from listening in or messing with your data.
Setting Up Network Segmentation for IoT Devices
Network segmentation keeps IoT devices like Shelly sensors safe. It makes a special network just for them. This stops bad guys from moving around if they get in. You can do this with VLAN configuration on your router or switch.
Implementing Automated Security Updates
It’s important to keep your system updated with the latest security fixes. Automated security updates can help. They make sure your system is always protected against new threats. This way, you can fix problems fast and stay safe.
Also, making regular backup procedures is key. It helps you get your data back if something goes wrong. By doing all these things, you make your energy monitoring system much safer.
craft/advanced Conclusion
Shelly Sensors work with Stromfee AI over MQTT, Clickhouse, Langchain, and Gemini. This makes a strong IoT energy management system. It lets you track energy in real time and get tips to save energy from a Video Avatar.
These advanced tools help users understand their energy use. They find where energy is wasted and fix it. This makes homes and businesses use less energy and be more green.
Setting up this system needs careful planning and setup. It also needs watching over to keep it working well. Keeping up with new tech and tips helps make the most of this energy-saving system.
