When choosing a smart plug for energy monitoring and data analysis, here’s a breakdown of key considerations based on the options you've outlined:

1. TP-Link Kasa Smart Plugs (e.g., KP115):

   • Pros: The community-developed solutions, like Kasa Collector and kasa2influxdb, can extract real-time data and integrate with tools like Grafana and InfluxDB for robust visualization and analysis.
   • Cons: TP-Link doesn’t officially support open API access, so you'll rely on community methods for data extraction, which may vary in reliability.

2. TP-Link Tapo Smart Plugs (e.g., P110):

   • Pros: The Energy Monitoring 2.0 Update supports local data export, which is advantageous for advanced analysis without continuous cloud dependence.
   • Cons: Limited third-party integration capabilities. Verify compatibility if you plan to feed data into external tools like InfluxDB or Grafana.

3. Shelly Smart Plugs:

   • Pros: Local REST API support allows for seamless, real-time data access without relying on cloud services, ideal for custom dashboards or systems that require continuous data feeds.
   • Cons: Requires more setup for analytics workflows if direct integrations with your existing systems aren’t readily available.

4. Govee Smart Plug Pro:

   • Pros: Offers energy monitoring features, though limited information is available on open API or data export support.
   • Cons: Lack of confirmed data export capabilities or API support means you may need to rely on third-party or manual data collection methods, which could affect data reliability and accessibility.

Key Selection Factors:

• API Access: Shelly Smart Plugs are most robust if you need reliable, open access via REST API.
• Data Export Capability: TP-Link Tapo P110 stands out for easy local data export, which may simplify analysis.
• Integration with Analytics Systems: TP-Link Kasa's community tools are beneficial for integration with platforms like Grafana and InfluxDB, though they are unofficial.

Based on your needs, prioritize Shelly for API flexibility or TP-Link Kasa if you plan to use community-supported analytics integration tools.

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I am looking for an affordable solution, alternatives to AWS which is really expensive, for data aggregation and data repository.

For affordable alternatives to AWS for data aggregation and data storage, here are a few reliable options that balance cost-effectiveness with performance:

1. DigitalOcean:

   • Offers straightforward cloud storage solutions with a DigitalOcean Spaces feature similar to AWS S3.
   • Includes managed databases (PostgreSQL, MySQL, Redis) and an App Platform that could serve well for data aggregation.
   • Pricing: Spaces starts at $5/month, making it affordable for smaller data requirements.

2. Backblaze B2:

   • Known for its low-cost storage, Backblaze B2 is a direct alternative to Amazon S3.
   • API compatibility with AWS S3 allows for easy migration if you're familiar with S3-based workflows.
   • Pricing: $0.005/GB/month for storage, making it very affordable for bulk data storage and retrieval.

3. Google Cloud Platform (GCP) - Firestore & BigQuery:

   • Firestore provides a flexible NoSQL database solution at a low cost for smaller datasets.
   • BigQuery, which can scale with usage, is an excellent tool for data aggregation and analytics with a pay-as-you-go model.
   • Pricing: Offers a generous free tier, which helps control costs, and you can also set up spending limits for budget control.

4. Linode:

   • Linode provides object storage similar to AWS S3, along with managed database options.
   • It’s highly affordable and straightforward, often with transparent pricing and no complex billing.
   • Pricing: Object Storage starts at $5/month, and Linode also has flexible scaling options.

5. Scaleway:

   • Offers Object Storage, managed databases, and other cloud solutions.
   • Known for aggressive pricing, especially in the European market, with simple data storage and transfer options.
   • Pricing: Starts at €0.01/GB per month for storage, with the first 75 GB free each month.

6. InfluxDB Cloud (for time-series data):

   • Specifically for time-series and IoT data, InfluxDB offers a fully managed solution.
   • It's particularly good for real-time data and metrics collection, with flexible usage tiers.
   • Pricing: Free tier for basic usage, making it cost-effective for modest data aggregation requirements.

For general use cases, DigitalOcean or Backblaze B2 may be your best bets for affordability. For time-series data, InfluxDB Cloud offers a strong balance between functionality and cost.

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I mean, relevant to the devices I mentioned earlier.

Given the devices you’re working with for energy monitoring, affordable data aggregation and storage alternatives that integrate well with IoT devices include:

1. InfluxDB Cloud:

   • Best for time-series data, InfluxDB Cloud is ideal for IoT data like that from smart plugs, as it allows efficient storage, querying, and real-time analysis.
   • Integration: Many IoT data collectors and community scripts (like kasa2influxdb for TP-Link devices) already support InfluxDB integration.
   • Pricing: It has a free tier and scalable pay-as-you-go pricing, which is economical for early-stage or medium-scale projects.

2. Azure IoT Hub and Azure Cosmos DB:

   • IoT Hub provides device-to-cloud communication, and Cosmos DB is flexible, supporting both NoSQL and time-series data.
   • Integration: Supports direct ingestion from IoT devices, and TP-Link community tools often have Azure-compatible options.
   • Pricing: Cosmos DB offers a free tier and competitive pricing for smaller data requirements, making it more budget-friendly than AWS.

3. DigitalOcean + TimescaleDB:

   • TimescaleDB (a time-series database) on DigitalOcean is ideal for structured IoT data collection and storage.
   • Integration: With PostgreSQL compatibility, TimescaleDB works well with community IoT data scripts and can support Grafana for visualization.
   • Pricing: DigitalOcean offers flexible, lower-cost managed database services, ideal for time-series data, and starts at $5/month.

4. Scaleway IoT Hub + Object Storage:

   • IoT Hub collects data from IoT devices, and Object Storage is useful for storing historical datasets.
   • Integration: Scaleway offers an S3-compatible API for Object Storage, so tools like kasa2influxdb that support S3 can be adapted.
   • Pricing: Scaleway has a lower entry cost, with Object Storage starting at €0.01/GB and free data egress for the first 75 GB monthly.

5. Google Cloud IoT Core + BigQuery:

   • IoT Core manages device data ingestion, and BigQuery handles storage and real-time analysis.
   • Integration: BigQuery works well with Grafana for visualization, and Google’s IoT Core is adaptable with Python-based data collectors.
   • Pricing: Google’s flexible billing and the free tier in BigQuery can be cost-effective, especially for smaller data volumes.

For easy setup and affordability with smart plug data, InfluxDB Cloud paired with Grafana for visualization is one of the best fits. Alternatively, DigitalOcean with TimescaleDB provides a similar time-series database experience at a manageable price point.

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What is Kasa Collector and kasa2influxdb

Here’s an overview of Kasa Collector and kasa2influxdb, two community-driven tools designed for working with TP-Link Kasa devices:

1. Kasa Collector

• Description: Kasa Collector is a Python-based tool that collects real-time energy usage data from TP-Link Kasa smart plugs (like the KP115 model, which supports energy monitoring). This tool was developed by the community as TP-Link does not officially offer an API for data collection.
• Functionality: It continuously polls the smart plugs for real-time data, capturing metrics such as power consumption and usage patterns.
• Integration with Grafana: It can push the data to a Grafana dashboard, allowing users to visualize energy usage trends over time. Grafana supports real-time graphing, which is useful for monitoring and analyzing the data visually.
• Setup: Requires installation of the Python script and configuration to connect to your local network where the Kasa devices are active. It may require basic adjustments to work with different Grafana and InfluxDB setups.

2. kasa2influxdb

• Description: This is another Python script developed to integrate TP-Link KP115 smart plugs with InfluxDB, a time-series database optimized for high-speed data processing.
• Functionality: It regularly polls the smart plugs to gather energy data (like wattage and energy consumption), and then logs that information directly into an InfluxDB database.
• Data Storage: InfluxDB is a great choice for IoT data storage because it’s designed for handling large volumes of timestamped data efficiently. This makes it easy to keep historical data and perform time-based queries.
• Usage: Once the data is in InfluxDB, you can use Grafana or other data visualization tools to analyze it. The script is customizable and can be scheduled to run at specific intervals to pull data from the smart plugs.

Benefits of Using These Tools:

• Cost-effective: No need for cloud subscriptions or additional services, as both tools work with locally stored data.
• Real-time data monitoring: They provide instant insights into energy usage, which is useful for tracking consumption trends, identifying peak usage, and potentially optimizing power management.
• Open-source and customizable: Being community-driven, these tools are free and can be adapted to specific use cases or expanded for additional device compatibility.

Considerations:

These tools rely on the TP-Link Kasa local control capabilities and may require a bit of technical setup with Python and databases (InfluxDB, Grafana). However, they provide a robust way to analyze IoT device data without needing an official API.

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So I can store the data on cloud?

Yes, you can store the data from TP-Link Kasa devices on the cloud by combining Kasa Collector or kasa2influxdb with cloud-hosted databases like InfluxDB Cloud, AWS Timestream, Google BigQuery, or even Google Sheets for lighter usage. Here’s how it can work:

Cloud Storage Options for Kasa Data

1. InfluxDB Cloud:

   • Description: InfluxDB offers a managed cloud version, which is ideal for IoT data because it’s optimized for time-series data.
   • Integration: Since kasa2influxdb is designed to log data to InfluxDB, you can configure it to point to your InfluxDB Cloud instance instead of a local database.
   • Steps:
     • Set up an account with InfluxDB Cloud and create a bucket for storing your data.
     • Modify the kasa2influxdb script to send data to your cloud database URL and use the authentication tokens provided by InfluxDB Cloud.
   • Visualization: Use InfluxDB’s own dashboard tools or connect it to Grafana for more advanced visualizations.

2. Google BigQuery:

   • Description: Google BigQuery is a cloud data warehouse suited for large datasets and analytics.
   • Integration: While BigQuery isn’t time-series specific, it works well for long-term storage and analysis.
   • Steps:
     • Write a modified version of kasa2influxdb or use a data connector to upload data to BigQuery in batches.
     • For smaller projects, consider using a Google Cloud Function to periodically pull data from your local InfluxDB instance and store it in BigQuery.
   • Visualization: Use Data Studio (free from Google) or integrate BigQuery with Grafana for visualization.

3. AWS Timestream:

   • Description: AWS Timestream is a time-series database optimized for IoT data and analytics.
   • Integration: AWS Timestream can serve as a replacement for InfluxDB if you’re using other AWS services or need seamless integration within the AWS ecosystem.
   • Steps:
     • Modify kasa2influxdb to push data to AWS Timestream via its API.
     • You may also use AWS Lambda to act as a bridge between the smart plug data and Timestream if direct integration with kasa2influxdb is complex.
   • Visualization: AWS Timestream works well with Amazon QuickSight or Grafana (which offers Timestream integration) for visualization.

4. Google Sheets (Basic Storage):

   • Description: For smaller projects or simpler setups, you can store data in Google Sheets.
   • Integration: Use a Python library (like gspread) to write data from kasa2influxdb into a Google Sheet.
   • Steps:
     • Install gspread and configure Google Sheets API credentials.
     • Modify the script to log data into Google Sheets periodically.
   • Visualization: Google Sheets has basic charting capabilities, or you can link it to Data Studio for richer visualizations.

Steps for Cloud Data Storage

1. Modify the data logging script (kasa2influxdb or similar) to point to the chosen cloud database’s API endpoint.
2. Authenticate: Use API keys, tokens, or other credentials provided by the cloud service to authenticate data writes.
3. Automate: Schedule the script to run at regular intervals (via cron jobs or cloud functions) for continuous data capture.
4. Visualize: Connect the cloud database to a visualization tool like Grafana, Data Studio, or the platform’s native dashboard.

Using a cloud solution enables scalability, real-time access, and easy integration with analytics tools, all without maintaining local hardware.

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Which ones are the most affordable or even free?

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If affordability (or even free options) is a top priority, here’s a comparison of the most cost-effective cloud storage solutions for IoT data collection from tools like Kasa Collector or kasa2influxdb:

Free or Low-Cost Cloud Storage Options

1. InfluxDB Cloud (Free Tier Available):

   • Free Tier: InfluxDB Cloud offers a generous free tier, allowing up to 30 days of data retention with basic storage and querying capabilities, ideal for small-to-medium IoT data needs.
   • Paid Plans: If you exceed the free tier, paid plans start affordably based on data points and storage needs, scaling flexibly as your data grows.
   • Best For: Real-time data and time-series storage. It’s easy to visualize the data with InfluxDB’s native tools or with Grafana, which is also free to use.

2. Google Sheets (Free with Google Account):

   • Free: Google Sheets is entirely free if you have a Google account, making it a great choice for light data logging.
   • Limitations: While it’s not designed for high-frequency IoT data, it works well for small projects or low-frequency data (e.g., every few minutes).
   • Best For: Small projects, testing, or prototyping without complex data requirements. It also integrates well with Google Data Studio for free visualizations.

3. Scaleway Object Storage (First 75 GB Free per Month):

   • Free Tier: Scaleway offers the first 75 GB free every month, which covers basic storage without egress charges for moderate usage.
   • Paid Plans: After the free 75 GB, storage costs are only €0.01/GB per month, making it one of the most affordable options.
   • Best For: Cost-effective bulk storage, especially if you need to store larger data sets (e.g., hourly data logs over a long period) rather than highly granular time-series data.

4. DigitalOcean App Platform (Free Tier for Basic App Hosting):

   • Free Tier: DigitalOcean offers a free tier for small applications, which could include a Python script to collect data and store it temporarily.
   • Paid Plans: If you need more storage or functionality, DigitalOcean’s basic plans start at $5/month, and Object Storage also starts at $5/month.
   • Best For: Affordable storage with a simple setup and reasonable free data transfer limits. DigitalOcean is great for prototyping or smaller-scale data aggregation.

5. AWS Free Tier (Limited Usage):

   • Free Tier: AWS offers a 12-month free tier for many services, including Timestream and S3, with limited storage and usage capacity. After 12 months, it becomes a paid service.
   • Best For: Short-term projects or testing out the setup before committing to a paid plan. AWS Timestream is also well-suited for time-series IoT data if your project scales up in the future.

6. Google BigQuery (Free Monthly Limit):

   • Free Tier: Google BigQuery offers 1 TB of queries free per month and up to 10 GB of free storage.
   • Paid Plans: If you exceed this, BigQuery charges per query and storage usage, but with efficient queries, it can be very cost-effective.
   • Best For: Projects that don’t require constant querying, as BigQuery charges primarily by query rather than storage, which keeps costs low if you query only occasionally.

Summary of Most Affordable or Free Options:

• For time-series data: InfluxDB Cloud’s free tier is ideal if you need real-time monitoring and time-based data logging.
• For lightweight data logging: Google Sheets is completely free and integrates with Google Data Studio.
• For larger, simple storage: Scaleway’s 75 GB free Object Storage each month is perfect if you need cost-effective bulk data storage.

For a mix of affordability, ease of use, and IoT compatibility, InfluxDB Cloud’s free tier and Google Sheets stand out, with Scaleway Object Storage as a strong option for larger storage needs.

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