SolarMind provides state-of-the-art accuracy solar power production forecasts for asset owners, power traders and system operators. SolarMind combines weather data (such as cloud coverage, temperature, pressure, etc.) from numerical weather prediction (NWP) models, satellites, ground measurements and sky imagery with advanced machine learning methods that learn both local and global weather patterns. Processing of spatio-temporal information allows SolarMind to produce forecast for individual PV panels, utility scale PV or CSP and distributed (possibly behind the meter) region or country aggregation level generation with state-of-the-art accuracy. Compared to traditional forecasting methods, SolarMind achieves a significant reduction in forecast errors, which lowers the imbalance costs from trading operations and helps improve system stability. The specific solar farm characteristics are modeled implicitly by the machine learning method. Therefore, SolarMind does not rely on static data, such as solar panel manufacturer, shadowing, topology and vegetation, but learns the influence of these parameters from observed data. This results in higher accuracy than compared to physical models where uncertainty regarding static variables is translated to the forecast.
SolarMind continuously incorporates the latest weather and power information to provide frequent forecast updates. In addition, performance on the first few hours ahead can be further reduced by adding live production data, although this is not a requirement. Through probabilistic power forecasts, the forecast uncertainty assessed. This gives higher degree of preparation for power market operations and allows for optimized intraday balancing of solar power. SolarMind is provided as a cloud service and thus requires no installation, it can be easily integrated with your current system setup through API or SFTP-server.