Energy production forecasts for distributed photovoltaic and wind energy sources
About the project
Learn how Globema helped to reduce Tradea’s balancing market participation costs thanks to reliable weather forecasts provided for 250 distributed solar and wind energy sources, using hybrid models utilizing artificial intelligence/ machine learning (AI/ML) and analytical computing.
Table of contents:
Trading in energy from Renewable Energy Sources (RES) involves the risk of deviation of the actual production from the forecast and, consequently, incurring additional costs of participation in the balancing market. With the growing volume of energy generated from renewable sources, it has become a challenge to increase the accuracy of forecasts and reduce errors every hour of the day.
Start of collaboration – initial stage
In late 2019, Globema and Tradea partnered to develop optimal methods for forecasting energy production in solar and wind farms. In 2020 we carried out work that included:
- completing the energy production measurement data and correcting this data, e.g. in terms of identification of measurement points (PPE) and values of installed capacity and technical data of the equipment – on Tradea’s side,
- verifying this data in terms of its correctness, completeness and possible anomalies – on Globema’s side,
- building preliminary predictive models and verifying their accuracy – on Globema’s side.
The cooperation was based on meetings, during which we presented research results to the customer and decided together on the further course of work. After developing a satisfactory prediction model, we started a six-month test phase of delivering and verifying the quality of forecasts.
The six-month trial period resulted in a positive verification of both the service itself and the production forecast error rate. Tradea’s evaluation of the results of the cooperation was positive and they decided to launch the service, based on 4RES, from the start of 2021.
We work incessantly to make our forecast delivery mechanism even more reliable. The delivery channel redundancy mentioned earlier (FTP and email) increases reliability in the event that one of them fails. We also prepare backup forecasts more in advance in case the weather forecast service itself fails – then, even in the absence of the latest weather forecast, our service does not fail, although the output forecast sent is statistically slightly less accurate.
Did you know that…
As a part of Globema R&D Center’s activity, we also conduct continuous research on improving the quality of our forecasts, in cooperation with our customers and weather forecast provider – the Interdisciplinary Centre for Mathematical and Computational Modelling UW
An interesting example of such cooperation is the issue of the impact of snowfall and snow accumulation on photovoltaic panels on the forecasting error, reported by Tradea. Although, with the warming climate, heavy snowfall, especially in the lowlands, is slowly becoming a thing of the past, it had a noticeable impact on forecasting errors in the 2020/21 season.
Based on this experience, we have developed a snow coverage model for the panels, which has been tested in the snowiest period in January and February of 2021, and is ready to be implemented in the upcoming winter season.
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