Green energy is the best way to save the environment and satisfy humanity needs. Having a solar or wind power station gives an opportunity to earn money using inexhaustible resources that cost nothing. But it also implies many technical and economical issues. To sell the power you need to know how much energy is going to be generated tomorrow or in three days. Ability to predict power generation and consumption as accurate as possible is an essential thing to optimize profitability. Here, modern , , and work for our interest.
Our technology uses weather forecast, hardware properties, historical data like generating history per hour, weather conditions, solar activity, to predict how much energy the station will produce over the next 24 hours or any alternative time slice in the future. We use modern data science and machine learning based methods. They guarantee that the difference between predicted and real power output will be as small as possible.
Depending on the type of consumption we can develop a custom predictive model. It will show how much energy will be spent/consumed for a certain period in the future.
Interaction with energy consumer - from a global power grid to a car charging point - implies having a transparent way to monitor the power generating/consuming balance. This results in a detailed report or through the convenient API.
Solar power generating is unstable and just partially predictable, and it is clear why. Weather, panels stability, dust, and other parameters can be really unpredictable sometimes. In addition to annual cycles, the task gets much more complex. One hundred percent accuracy is not reachable. There is a number of factors that may greatly influence the outcome:
The better the light intensity the more electrical power can be generated. The geographical position of the power station along with changing weather and climate conditions make their amendments towards its productivity and prediction accuracy.
The installation type and temperature properties, the shading extent, orientation and inclination of solar panels may impact the calculation output along with the maintenance conditions and proper exploitation.
The power generation changes within the time of year and day: it’s increased in summer during the light day although the nominal performance may drop due to abnormal heat.
Our solution here is to use the most modern math and data science to reduce the deviation between predicted and real value.
We’ve created a new predictive model that has improved the forecast accuracy by 7% compared to the old one and helped our client to optimize business efficiency.
The designed ecosystem takes into account a myriad of parameters about meteorological, equipment and seasonality conditions to tailor the most accurate energy forecast for each unique case.
We’ve implemented the reports system and API integration that significantly optimizes business processes to ensure further profitability growth.