Intelligent climate control
We use Model Predictive Control (MPC), weather forecasts, and dynamic energy prices to ensure a perfectly stable climate while cutting energy costs.
Control
Algoneed is a non-invasive optimization layer for your existing greenhouse. We install an industrial-grade RevPi Connect in your cabinet to securely bridge your local controllers with our cloud. Our servers handle the complex MPC, weather, and energy modeling, returning optimized setpoints directly to your actuators. For modern systems, 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. The interface visualizes how local on-site sensor data integrates with continuous external inputs, including weather forecasts and energy prices. That allows operators to continuously monitor and verify the exact parameters driving the optimization.
The need of algorithms
At the core of Algoneed is an advanced Model Predictive Control (MPC) engine designed to manage the complex thermodynamics and multi-variable climate dynamics of commercial greenhouses. Instead of relying on reactive, rule-based logic, the system continuously simulates future weather patterns and dynamic energy market conditions to calculate the most cost-efficient climate trajectory. This advanced predictive modeling ensures optimal climate parameters and maximum energy savings.
Optimization scenarios
Dynamic tariffs and heat buffering
Process: Operating heat pumps under fluctuating day-ahead energy market prices.Control strategy: The MPC algorithm correlates the projected overnight thermal demand (derived from localized weather forecasts) with hourly energy prices. Instead of maintaining a flat heating curve, the system pre-heats the water buffer tank or the greenhouse air volume during the cheapest energy windows.
Predictive shading screen deployment
Process: Managing excessive solar radiation and preventing indoor temperature spikes during weather shifts.Control Strategy: Intense solar radiation is anticipated using high-resolution weather forecasts. The system preemptively adjusts the shading screens to modulate incoming thermal load before the greenhouse begins to overheat, while continuously ensuring the crops receive the exact, optimal amount of solar radiation required for growth.
Ventilation and air exchange control
Process: Operating heat pumps under fluctuating day-ahead energy market prices.Control strategy: The MPC algorithm correlates the projected overnight thermal demand (derived from localized weather forecasts) with hourly energy prices. Instead of maintaining a flat heating curve, the system pre-heats the water buffer tank or the greenhouse air volume during the cheapest energy windows.
Daily light integral (DLI) optimization
Process: Fulfilling the precise daily light integral (DLI) requirement for crops using supplementary LED/HPS lighting while minimizing exposure to peak electricity prices.Control Strategy: Natural solar radiation is forecasted for the entire 24-hour cycle using high-resolution weather forecasts. The forecasts are adjusted by local measurements. It calculates the exact light deficit and schedules the supplementary lighting strictly during the lowest-cost energy windows (based on dynamic day-ahead market tariffs). The exact target DLI is achieved, maximizing photosynthetic efficiency while minimizing the operational electricity cost
Algoneed sp. z o.o.
Świeradowska 47, 02-662 Warszawa
NIP: 5214056437+48 604 202 870
contact@algoneed.com