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AI based model used to simulate possible dependencies and to forecast the market changes and outcomes for the next days in order to optimize usage of batteries and other energy market assets
The dependencies in the energy sector are complex and a human cannot make reliable forecasts without efficient tools. Therefore, more automatisation and self-learning IoT tools are needed in order to increase the accuracy and overall system efficiency.
Automatised forecasting tools were developed. The solution is based on AI which collects data automatically via open interfaces from public data sources. The goal is to improve the prediction accuracy for energy and power market participation. For the end user, the data is collected nearly in real-time and stored historical data can be used to optimise the Virtual Power Plant (VPP) operations.
Ultimately, the solution was successfully implemented across several locations in Finland, with multiple asset buildings participating in the VPP, stretching from Espoo to Lapland. The prediction models operate in a virtual environment, using cloud-based solutions and data centres.
