aimpera Load Forecasting
AI-powered load forecasts
…for individual consumers, sites, and portfolios — optimize operations, trading, and flexibilities.
Fluctuating energy consumption and volatile energy prices pose new challenges for utilities and large energy consumers: Without precise, AI-powered load forecasts, it remains unclear how energy consumption will develop over the coming hours and days and how it can be economically aligned with generation, market prices, and grid constraints. This leaves operations, procurement, and trading without a reliable basis for decision-making.
This is exactly where aimpera’s load forecasting comes in. It delivers quarter-hourly, continuously updated forecasts for individual consumers, sites, or entire portfolios.
Single & Portfolio Forecasting
Load forecasting at the level of individual consumers or aggregates, as well as entire sites and portfolios.
Quality Monitoring
Continuous, transparent verification of forecast accuracy using standardized metrics.
Automatic Adaptation
Automatic model adjustment in response to load changes or structural drift.
Rolling Forecasts
Continuous updates at 15-minute intervals, with a forecast horizon of up to 7 days (optionally beyond).
Cold-Start Capability
Fully functional from day one—our algorithms do not require years of historical data.
Proven in Practice
Successfully used, among others, in charging hubs, heating networks, and industrial processes.
Confidence & Uncertainty Band
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), and integration into existing system landscapes.
Short Deployment Time
Go-live in just a few days thanks to rapid implementation.
Developed and optimized for
Industrial & commercial customers
Energy utilities, municipal utilities & IPPs
Virtual power plants & aggregators
Operators of energy infrastructure (mobility, heat, etc.)
How it works
Our load forecasting continuously processes historical consumption data, current measurements, and exogenous factors such as weather and calendar information, as well as endogenous factors derived from asset and operational behavior, generating an updated forecast every 15 minutes. Upstream preprocessing cleans faulty data, detects outliers, and harmonizes different data sources.
The forecasting core is based on self-learning machine learning methods with neural networks that learn load- and site-specific patterns from the data. The models are continuously monitored and retrained when needed to ensure consistently high forecast quality. Results are delivered flexibly via standardized interfaces or a web dashboard — including transparent indications of forecast uncertainty.
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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