Intelligent Algorithms
Our Solutions
The need of algorithms
Renewable Energy
We develop specialized algorithms for PV plants, wind farms and renewable-energy portfolios. By combining real-time production data, weather forecasts, asset specifications and physical models, we help operators understand how much energy their assets can produce now and in the future.
Available Active Power
Available Active Power (AAP) estimates how much power a renewable asset could generate under current conditions if it were not curtailed or limited. We build plant-specific AAP models for PV and wind assets, supporting:
balancing and reserve services
curtailment management
generation baselines
performance monitoring
energy trading and market operations
Renewable Generation Forecasting
We create short-term, intraday and day-ahead forecasts for individual assets and renewable-energy portfolios. Our models combine weather predictions, live measurements, historical production and asset-specific characteristics to support:
PV and wind generation forecasting
portfolio forecasting
trading and balancing
production planning
imbalance-cost reduction
uncertainty estimation
Intelligent Control
We use predictive models and advanced optimization algorithms to control complex systems, maintain a stable climate, and improve energy efficiency.
Control
Algoneed is a non-invasive optimization layer for your existing industrial process. We install an industrial-grade RevPi Connect in your control cabinet to securely bridge your local controllers with our cloud. Our servers handle complex MPC, forecasting, and operational optimization, returning optimized setpoints directly to your machines, actuators, or control systems. For modern installations, we also offer direct cloud-to-cloud API integration.
Monitoring
Algoneed avoids the black-box AI approach by providing full visibility into the system’s logic. Our dashboards display both real-time measurements and the resulting control signals, visualizing how local sensor data integrates with continuous external inputs like weather forecasts and energy prices — so operators can always verify the exact parameters driving the optimization.
Predictive optimization at the core
At the core of Algoneed is an advanced Model Predictive Control (MPC) engine that manages complex industrial processes with multiple interacting variables and constraints. Instead of reactive, rule-based logic, the system continuously simulates future process behavior, external conditions, and energy market signals to calculate the most cost-efficient operating trajectory.
Optimization use cases
Dynamic tariffs and heat buffering
The MPC algorithm correlates projected overnight thermal demand with hourly energy prices, pre-heating the water buffer tank or greenhouse air volume during the cheapest energy windows instead of maintaining a flat heating curve.
Predictive shading screen deployment
Intense solar radiation is anticipated using high-resolution weather forecasts. The system preemptively adjusts shading screens to modulate incoming thermal load, while ensuring crops still receive the optimal amount of solar radiation for growth.
Ventilation and air exchange control
The system balances air exchange and humidity control against energy cost, dynamically adjusting ventilation setpoints to maintain optimal climate conditions at the lowest possible energy cost.
Daily light integral (DLI) optimization
Natural solar radiation is forecasted for the full 24-hour cycle and adjusted with local measurements. The system calculates the exact light deficit and schedules supplementary LED/HPS lighting during the lowest-cost energy windows, maximizing photosynthetic efficiency while minimizing electricity cost.
Ready to improve your process?
Get in touch to discuss how our solutions can support your operations.