Energy Forecast Optimization With the Power of Machine Learning
As renewable assets increase their share in the energy mix and gradually integrate into the energy markets, the role of accurate power forecast for day ahead and intraday market participation becomes crucial. The forecasts that relevant specialized companies provide often do not adapt appropriately to the peculiarities of each site and therefore end up with rather high forecasting errors.
Our leading platform, Unity, deploys Machine Learning (ML) algorithms that combine real-time SCADA measurements with third-party power forecasts to provide high-accuracy forecasts and decrease imbalance costs.
Inaccess combines and trains different ML models for each site using constantly growing datasets. The ML models are periodically retrained within a specific duration for optimum results. The achieved forecast optimization can save thousands of otherwise applicable imbalance costs, as already proven across several sites and energy markets in Europe.