Enlitia - Renewable Energy Asset Data / AI Software
Providing a single, unified access point of data for wind and solar power, we help businesses operate more efficiently to increase their energy production and ROI. Our Algorithm Ecosystem measures portfolio performance and data quality, improving energy availability.
CurtailmentSight
Curtailment Detection
Detects and categorises power limitations due to grid curtailments and other external factors. CurtailmentSight analyses both historical and real-time data to determine when energy output is limited, delivering actionable insights to operators on the impact of curtailments on overall energy production.
Use Case: A wind farm experiences frequent grid curtailment due to regional transmission constraints. CurtailmentSight identifies peak curtailment periods and quantifies energy loss, allowing the operator to engage with grid operators or regulators to explore solutions that reduce curtailment frequency and improve profitability.
DustGuard
Soiling Detection
Monitors solar panel cleanliness by assessing soiling accumulation from dust, grime, and other contaminants that temporarily reduce efficiency. DustGuard alerts operators when soiling thresholds are reached, optimizing cleaning schedules for maximum efficiency without unnecessary costs.
Use Case: A solar farm in a dusty region has experienced consistent efficiency drops between scheduled cleanings. DustGuard tracks dirt buildup in real-time and alert the operator when energy production is significantly impacted. Operators use this data to plan additional cleanings during dust-heavy periods, improving energy capture and minimizing power loss.
SolarLife
Solar Degradation Detection
Monitors the long-term degradation of solar panels and inverters by comparing real-world performance data to expected degradation rates. SolarLife identifies abnormal degradation rates that may require maintenance or replacement. This algorithm helps extend the lifespan of solar assets and prevent unexpected drops in efficiency.
Use Case: Operators of a solar farm notice a gradual decrease in panel and inverter efficiency. SolarLife confirms whether the observed decline is within expected limits or suggests that certain panels or inverters are degrading faster than anticipated. By pinpointing these issues early, the operator can schedule inspections, maintain optimal performance, and avoid larger, costly repairs.
PowerFit
Power Curve Digital-Twin
PowerFit leverages Artificial Intelligence models and historical data from wind turbines or solar inverters to create a precise digital twin. This digital twin enables real-time detection of underperformance or potential faults. By providing actionable insights, PowerFit empowers operators to identify deviations that may indicate inefficiencies or health issues, ensuring optimal asset performance and reliability.
Use Case: At a wind farm, operators observe that a few turbines are producing less energy than others under similar wind conditions. PowerFit analyzes each turbine’s power curve, identifying those deviating from expected performance. This prompts maintenance teams to inspect affected turbines, optimising power generation and reducing revenue loss.
HealthWatch
Condition Monitoring
HealthWatch offers an efficient solution for enhancing the condition monitoring of wind turbines by analysing temperature data from all components in real time using advanced artificial intelligence models. It provides actionable insights, enabling operators to identify and anticipate potential issues up to 21 days in advance. This proactive approach facilitates timely maintenance, reduces downtime, and minimizes costly repairs, ensuring optimal turbine performance and reliability.
Use Case: A wind turbine experiences a consistent increase in the temperature of the generator. HealthWatch detects these variations early and predicts the possible evolution of that temperature for the next days, allowing the operator to engage with maintenance teams to evaluate the possible issue and this way avoiding failures that could lead to significant production losses.
DataTrust
Data Quality Index
Ensures the reliability and accuracy of wind and solar data through comprehensive profiling. It verifies data quantity, detects outliers, frozen values, and inconsistencies, and replaces invalid points using correlated variables or external sources. By generating a validated dataset and tracking bad data timestamps, DataQuality ensures robust data for decision-making and analytics.
Use Case: A wind turbine shows low-performance rates, but the data quality index is very low, so the operator knows that there are considerable issues in the data and will take that into account when making decisions about that performance issue.
PowerForecast
Advanced Power Forecast
Combines SCADA data, weather forecasts, and external power forecast providers to deliver short-term, high-accuracy power production estimates, enabling energy producers to anticipate production fluctuations with precision. The result is a reliable, probabilistic forecast that supports better trading strategies and O&M decisions.
Use Case: Operators managing a portfolio of wind and solar assets need to submit accurate short-term production forecasts for day-ahead and intraday markets. Enlitia's Advanced Power Forecast processes SCADA signals and combines them with weather and forecast data to generate a refined forecast with up to 30% higher accuracy. This enables the operator to reduce trading deviations and optimise the scheduling of maintenance during periods of low production, boosting both revenue and reliability.