Forecasting solar energy is important for power trading. The forecasts inform the decisions made by power traders, which in turn impacts profit for asset operators and marketers. Additionally, the available amount of solar power influences larger trends in the market, making a good forecast relevant beyond the solar power trades themselves.
Power from most photovoltaic assets (PV assets) is currently traded on an exchange using the market premium model – often through a direct marketer such as Next Kraftwerke. Asset operators earn profits from the trade and the market premium. They also receive a management premium, which is added to the market premium for new assets. Usually, a marketer receives a portion of the management premium for marketing services. This is where the forecast is important: The better it is, the better traders can market the portfolio, leading to lower balancing costs and more residual marketing and management premiums for operators and marketers. A good forecast is therefore essential for a successful market.
More data leads to a better forecast
To create a forecast that is as accurate as possible, analysts look beyond the weather report. In fact, they use a wide range of data to predict a pattern of PV feed-in that is as precise as possible. The underlying idea is that if more data is available, the pattern becomes more precise.
Plant operation schedules, consumption and feed-in measurements, prices and ordered volumes on power exchanges, weather data, geographic data – all are examples of data that is incorporated into the forecast. The data comes from several different sources, is measured at different times, and utilizes different formats and data types. The large volume of data is processed by an algorithm and turned into a forecast of PV-power feed-in volume.
Forecast algorithms are mathematical processes, assisted by parametric models and machine learning, that analyze sets of data and report the results. Parametric models are based on statistics and use current data and a series of estimates to arrive at a conclusion about the future – daily PV feed-in, in this case. Machine learning, on the other hand, involves a data analyst using ‘lessons’ to teach a computer to recognize certain patterns within the data and to predict further developments. With PV forecasting, this includes the typical shape of the feed-in curve of PV power over the course of a day. As the volume of data provided to the computer grows and becomes more structured, the better the computer becomes at creating a corresponding pattern, which can include the ability to recognize deviations to the norm. This principle can apply to all forecasts that are being created today. The difference lies in the sources of data that are used and how the algorithm has been written, which varies from provider to provider.