One access point for renewable asset data — so teams see performance and data quality clearly, and know where energy availability can improve. The Algorithm Ecosystem is how we measure it; the work is tailored to your portfolio.
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.
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.
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 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 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.
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.
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.
We connect the data energy teams already use — weather, SCADA, forecasts, markets — and keep expanding the network so day-to-day questions have answers in one place.
GFS, ICON, ECMWF IFS/AIFS, Météo-France, WeatherNext/GraphCast/GenCast — we connect the weather models teams already use, and we can generate 10+ years of historical data so long-run analysis stays consistent.
Vestas, SGRE, GE Vernova, Nordex, Enercon, Goldwind, Huawei, Sungrow, SMA, Fronius, GoodWe, SolarEdge, Enphase — we plug into the OEM data you already run.
Power Factors, BaxEnergy, Nuo, GPM, Green Eagle, Hitachi Energy, Siemens Spectrum — APM / Level-2 SCADA stays synced for day-to-day questions.
Meteologica, enercast, energy & meteo systems, UL Solutions — and real-time satellite data such as Solcast, SolarGIS, among others — forecast and irradiance feeds you already trust, connected into one place.
Galp, Ignis, Axpo, Simples, Gesnova, among others — market counterparties and trading tools kept in sync with the rest of the stack.
ENTSO-E, REN, OMIE, OMIP, e·sios, Elexon BMRS, Nord Pool — market and grid operators energy teams already rely on. We connect prices, generation, load and settlement data into the stack.
Morning view of key metrics, asset status, and energy signals. Spot portfolio performance, top losses, and price context — with alerts that help prioritise what to fix first.
Real-time performance, capacity factors, estimated losses, and AI cues in one view. Health indicators and map context help spot issues early and focus maintenance where it counts.
Production forecasts from multiple models, grounded in weather and price context. Fewer market deviations, tighter planning, and decisions tuned to each asset.
Dashboards for asset performance, forecast accuracy, and energy metrics. Compare models, track trends, and see efficiency across the portfolio — so decisions rest on evidence, not guesswork.
Every metric carries a data quality index so you know how much to trust the number — scored from the same algorithm inputs that power the result.
Daily, weekly and monthly performance in one index — so asset behaviour is clear before you decide where to act.
Temperatures, vibrations, lightning, dust and storm context rolled into one health index — deviations from normal show up early.
Runs on the data you already have — no physical install. Host on-cloud or on-prem; you choose.
Built with industry software and cybersecurity practices so asset data stays protected.
Create, monitor and suspend API keys by role — secure access to platform data and services when you need it.
Every figure is traceable: you see the inputs, the quality behind it, and why the recommendation is there.
Measure and forecast renewable portfolio performance with the metrics and algorithms the work actually uses — so optimisation is specific, not generic.
The Algorithm Ecosystem tracks assets and data quality together — so you know when a number is solid, and when it still needs work.
Talk to our experts and learn more about how Enlitia's AI Platform can optimize your renewable energy assets and decision-making process.
Enlitia is an innovative provider of asset performance management & monitoring solutions, empowering the energy industry to thrive in the digital era. With our pioneering AI Platform, we deliver transformative results, driving growth, performance, and value for our clients. Unlock the power of data with Enlitia today.
