ATMOSPHERE - weather data processor
City: Kyiv
Project manager: Victor Dolgopyatov
Degree of readiness: Prototypes
"Atmosphere" is a weather data processor, a system that collects many parameters of weather conditions and air quality from global sources and local-placed own meteorological stations to make refined predictions of the weather.
The offer is an on-site weather prediction which has a variety of applications. Currently, the “Atmosphere” team is focused on solar stations. It is important for them to forecast their generation levels.
Our customers pay the subscriber fee.
Video presentation of the project
Project presentation in PDF format >>>
Additional materials:
Startup site - http://atmosphere.city
Links to publications in online publications >>>
It would be helpful to see more information on how this solution is better than existing solutions and how you will gain new customers.
ReplyDeleteGreetings from the Atmosphere team!
Delete1. Speaking of our solution for solar stations, the main distinguishing features are sources of data and approach to the weather forecasts.
We aim to install a cluster of our weather stations. For weather and energy generation forecasts we combine local data, data from external meteorological resources, telemetry, an archive of historical weather data. While existent solutions focus on basic weather and satellite data. It results in errors between predicted and factual generation.
2. How will we gain new customers? In our brief marketing strategy, we mentioned that we target conferences, discussions, seminars as the topic of energy generation forecast for SPP is in the center of attention. Moreover, we are preparing for our first pilot, which will prove the system's efficiency. Also, we will work with specialized media resources.
You may find more information in our presentation above. Don't hesitate to ask us here or via email! Thanks.
The Atmosphere weather prediction system demonstrates how weather data, air quality measurements, and information from meteorological stations can be combined to generate more refined forecasts. Its focus on predicting solar power generation highlights the importance of accurate forecasting in renewable energy management, where weather conditions directly influence energy output.
DeleteMachine learning techniques can help analyze historical weather observations and identify patterns in time-dependent data. Exploring a Machine Learning Time Series Course can help learners understand forecasting methods for applications such as weather prediction and solar energy generation.
Regression techniques are also useful for estimating continuous values, including expected solar power output based on relevant weather parameters. A Machine Learning Regression Course can help learners explore predictive modeling techniques and understand how they can be applied to data-driven forecasting systems.
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ReplyDeleteI think on-site weather prediction is a very useful application.
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