aimpera PV Power Forecasting
Control storage, self-consumption, and marketing more efficiently.
Fluctuating PV generation presents producers, utilities, and energy consumers with a key challenge: without precise forecasting, it remains unclear how much solar power will be available in the next minutes or hours — for example for optimized storage schedules, self-consumption control, or marketing. This is exactly where our fine-grained PV power forecasting comes in: it provides site-specific forecasts updated every 15 minutes of system output, complemented by uncertainty bands and confidence estimates. This enables intelligent storage operation, optimized schedules, and stable management of balancing groups.
Individual & portfolio forecasting
Detailed forecasts at individual asset level, with the option to flexibly aggregate entire portfolios.
Quality monitoring
Continuous, transparent verification of forecast accuracy using standardized metrics.
Flexible connectivity
Easy integration into existing virtual power plants (VPP), EMS systems, and storage controllers.
Rolling forecasts
Continuous updates every 15 minutes (or more frequently) with a standard horizon of 48 hours (optionally longer).
Cold-start capability
Full functionality from day one — our algorithms do not require years of historical data.
Europe-wide applicability
Broad deployment across Europe through integration of modular, high-resolution weather data models.
Confidence & uncertainty bands
Integrated confidence estimates and uncertainty bands as a precise basis for decision-making.
Seamless integration
Fast access via REST API, web UI, or export (CSV/SFTP). Go-live possible within a few days.
Maximum data security
Hosting in Germany, full GDPR compliance, and optional on-premises installation.
Developed and optimized for
IPPs & PV operators
Municipal utilities & utilities
VPP operators
Industrial customers with self-consumption
Direct marketers & traders
Battery marketers
How it works
Our forecasting continuously processes weather data, site parameters, and real-time power values from the plant, generating an updated forecast every 15 minutes. Upstream preprocessing cleans faulty or noisy input data, detects gaps, and harmonizes all sources for model processing.
The forecasting core is based on a hybrid approach: a physics-based digital twin represents the PV system’s structure, while a deep learning model learns site-specific patterns from historical power data and weather forecasts.
All models are continuously monitored and automatically retrained when needed — for example when plant conditions change or forecast quality drops. This allows the AI to adapt automatically to changing conditions and deliver consistently best-possible forecast results. Output is provided via a standardized REST API or alternatively through a web interface, each complemented by uncertainty bands and confidence estimates.
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aimpera
aimpera is a spin-off of the German Research Center for Artificial Intelligence (DFKI) and develops practical AI systems for the energy world of tomorrow. Whether forecasting, control, or operations management — our platform solutions enable intelligent planning and optimization of energy systems, from individual sites to virtual power plants.
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