How to manage the forecast error if there is suddenly a thick layer of snow on the solar panels

Numerical weather prediction (NWP) models are the most important time-variant input when forecasting weather-dependent processes, as they cover the upcoming days which are of particular interest in many applications. Selecting the right models, parameters and time periods helps to achieve high forecast accuracy when using machine learning methods with NWP model data.

Solar power forecasts from enercast for optimized use of E-cars as buffer storage and suppliers of primary control thru Local energy management and regenerative charging infrastructure

Within the research project “Prophesy”, a tool was developed that allows to simulate feed-in, forecasts and forecast error on different temporal and spatial scales.

Researching new methods for automated knowledge transfer between individual system components in the energy sector in order to be able to generate robust status determinations and forecasts for new and changing system components.

On December 9 at 03:30 PM IST (10:00 UTC) Emre Uraz, Head of South Asia Business at enercast, will give an online talk on “Solar Power Forecasting Methods and Applications”.

On October 1, 2021, far-reaching changes for the grid integration of renewable energy sources in Germany will come into force through the new “Redispatch 2.0. As a result, grid operators will be faced with new tasks in which forecasts play an important role.

The Indian Wind Power Association recently published an article by Jonathan Simon, Product Manager at enercast, on the cover story of their eMagazine Windpro “Accurate forecasts for successful renewable energy businesses”.

Each NWP model has certain strengths and weaknesses depending on conditions such as region, terrain type, and forecast horizon. Knowing these strengths and weaknesses allows making well-informed decisions that can assist in producing forecasts with higher accuracy.

Dr. Henning Schulze-Lauen, Managing Director of enercast GmbH, speaks about artificial intelligence and how enercast applies this technology for making renewable energy successful.

Company outing at enercast! After many months working from home, the enercast team finally saw each other in person again. Under the motto “Around the Hirzstein”, the program was a three-hour walk followed by a barbecue.

Prof. Dr. Kristina Sinemus, Hessian Minister for Digital Strategy and Development, visited enercast GmbH in Kassel today as part of her summer trip.

Our white paper provides an overview of forecasting methods and how we apply enercast e³ technology in order to deliver optimal power forecasts.

It is critical to understand the various techniques of measuring accuracy and to carefully identify from the start the ones most suitable for a user’s needs. 

The quality of the input data provided to a state-of-the-art forecasting method for renewable energy is a key factor for forecast accuracy. This input data can be grouped into static data and time-variant data.

Thanks to complete automation, it is now possible to set up high-precision power prediction with machine learning algorithms within a few minutes. The enercast ensemble engine e³ optimizes forecast quality for the specific business case by means of interactively specified horizons, update times and KPIs.

From 11 through 13 February 2020, meet enercast at E-world in Essen, Germany. You can find us at our usual location in hall 5, booth 5-300.

A case study on how enercast improves accuracy of our renewable energy forecasts through feedback loop technology is featured in a report recently published by SolarPower Europe, titled ‘Digitalisation & Solar in Emerging Markets’.