Weather Data for Wind and Solar Energy
The Planet OS Weather Data Service provides the most valuable data products for wind and solar farm operators via a simple API.
Renewables operators face complex efficiency challenges, increasing price pressure, and dynamic weather patterns. With access to curated data, operators can increase availability, improve maintenance planning, and decrease operational costs.
Planet OS combines historical records and forecasts into a single package that:
  • eliminates complex scientific data formats
  • simplifies dimensionality and spatial grids
  • enables numerous use-cases including...
    • real-time monitoring
    • statistical performance analysis
    • and complex machine learning solutions.
GWYNT Y MÔR WINDFARM, UNITED KINGDOM
Some of the world's largest offshore wind farms use the Planet OS API to access and monitor the weather in their immediate surrounding.
Variables
HISTORY
FORECAST
  • wind vector speed @ 80m
  • pressure @ 80m
  • air temperature @ 80m
  • wind vectors @ surface
  • wind gust @ surface
  • air temperature @ surface
  • precipitation
  • humidity
  • solar irradiance (W/m2 forecast, J/m2 history)
  • snow cover
SAMPLE API QUERY
curl --request GET \ 
--url 'http://api.planetos.com/v1/search/text?q=/v1/datasets/noaa_gfs_pgrb2_global_analysis_0.25degree/point?lat=49.5&lon=-50.5&var=vgrd_m,ugrd_m&z=1&apikey={apikey}'
SAMPLE API RESPONSE
{
  "context": "time_height_above_ground3_lat_lon",
  "axes": {
    "time": "2018-11-22T00:00:00",
    "z": 80,
    "latitude": 49.5,
    "longitude": -50.49999999999997
  },
  "data": {
    "ugrd_m": 14.199614524841309,
    "vgrd_m": 9.763354301452637
  }
}
Improvements in renewables efficiency are largely dependent on data-driven insights. Data Science today is only scratching the surface of potential performance improvements. Access to relevant, quality data is what turns Data Science into business success.
Every
Data Scientist
knows how much effort goes into cleaning and preparing their data. Planet OS saves hours of work that is spent on fetching and crunching data before it’s digestible by statistical models or machine learning tools.
There is a growing variety of data science applications in the renewable energy sector, some examples include:
  • increasing precision of energy production forecasts
  • building stronger financial models (energy pricing)
  • optimizing operations and maintenance schedules
  • fault detection and preventive maintenance
  • risk models for harsh weather conditions
Planet OS code examples and helper functions make quick data assessments and prototyping possible. All of our code projects are openly shared on GitHub where Planet OS users, as a community, can collaborate on extending the toolset and solving domain-specific problems.
Wind and solar farm production analysis requires a diverse set of input parameters, and high-quality historical weather records are a significant part of it.
A key function of a
Performance Analyst
is the analysis of production trends, patterns, and outliers. Using only IoT data generated by turbines or solar panels is often not enough to derive meaningful insights. Having external weather data to validate assumptions and analyze historical correlations is essential.
The Planet OS API facilitates weather data integration with standard BI tools, allowing analysts to blend weather conditions with operational machine data from solar panels and wind turbines, providing new context to analyses and reports.
Example use-cases:
  • Replace unreliable weather data ingestion pipelines with analysis-ready data piped directly into your BI tools via a standard REST API.
  • Investigate the impact of seasonal and multi-year weather trends, giving new context to your key performance indicators and reports.
Carefully crafted apps are making data accessible and actionable. The Planet OS API lets developers without domain expertise easily build solutions for the renewable energy industry.
Data isn't just reports and pretty charts though, in the hands of developers it can be transformed into valuable new opportunities. Our goal is to empower those looking to leverage weather data in creative ways, and provide a developer experience that makes working with data simple and straightforward.
We provide a number of resources to help developers succeed, including detailed documentation, code samples on GitHub, and a Slack community where they can chat with our support staff.
Operating and maintaining renewable energy assets is highly dependent on timely data acquisition, processing and distribution.
Maximizing the performance of your renewable energy assets requires operational awareness that can only be achieved with a robust data architecture that allows you to acquire, store, and securly distribute a diverse set of data. This means providing stakeholders with timely access to all your organization's data assets, including real-time machine data, condition monitoring metrics, operational weather observations, and data products from third-party providers.
The Planet OS Enterprise Plan provides a powerful data processing and governance solution that enables organizations to exercise full control over their data.
Looking for a solution to manage and govern your operational data? Click here to request a demo.
  • Clean and blend data from multiple operational systems and IoT sensors into a single data stream using entity resolution (for instance using turbine/sensor id). Distribute processed data via WebSocket (real-time) and REST (historical) APIs.
  • Track assets using near-real-time sensor data and high-resolution regional weather forecast to analyze how weather parameters are affecting business operations. After data processing and normalization, clean data becomes available via APIs for visualization on the client-side (mobile or web app, for instance).
Variables
HISTORY
FORECAST
  • wind vector speed @ 80m
  • pressure @ 80m
  • air temperature @ 80m
  • wind vectors @ surface
  • wind gust @ surface
  • air temperature @ surface
  • precipitation
  • humidity
  • solar irradiance (W/m2 forecast, J/m2 history)
  • snow cover
SAMPLE API QUERY
curl --request GET \ 
--url 'http://api.planetos.com/v1/search/text?q=/v1/datasets/noaa_gfs_pgrb2_global_analysis_0.25degree/point?lat=49.5&lon=-50.5&var=vgrd_m,ugrd_m&z=1&apikey={apikey}'
SAMPLE API RESPONSE
{
  "context": "time_height_above_ground3_lat_lon",
  "axes": {
    "time": "2018-11-22T00:00:00",
    "z": 80,
    "latitude": 49.5,
    "longitude": -50.49999999999997
  },
  "data": {
    "ugrd_m": 14.199614524841309,
    "vgrd_m": 9.763354301452637
  }
}