How Enlitia, a Portuguese company, is revolutionizing the management of renewable energy assets with the use of Artificial Intelligence

Renewable energy asset management is rapidly changing with the support of Artificial Intelligence. Among the key players, the Portuguese company Enlitia stands out for transforming data into decisions that increase production, reduce costs, and prepare the sector for the next decade.

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How Enlitia is revolutionizing renewable asset management with AI: direct impact on operations

Enlitia was born from the data and AI field of Smartwatt, with expertise accumulated since 2008, and launched in 2023 a platform dedicated to the performance assessment of renewable assets. In just a few years, it has expanded its presence to 12 countries, collaborating with operators like EDP Renewables, Galp, Ventient Energy, and ERG. By 2025, the focus remains clear: to professionalize the extraction of value from data and ensure that each kilowatt-hour is maximized.

The big difference lies in connecting sensors, historical data, and meteorological context to a algorithm ecosystem. This allows for comparing what an asset should produce with what it actually produces, identifying invisible losses (dirt, misalignment, shading, yaw in wind) and recommending interventions with economic priority. Instead of monthly reports arriving late, teams receive actionable alerts in time to prevent revenue loss.

From origin to maturity: from “AI department” to complete platform

The path of Enlitia illustrates a trend: data teams cease to be support and become the engine of performance. Separated from the parent company to accelerate the focus on AI, the company has solidified its position with a results-oriented platform and a service model that aligns with operators’ routines, from O&M to finance.

For operators with distributed portfolios, the platform acts as a “control tower” between SCADA, CMMS, and financial reports. The goal is simple: less time searching for problems, more time generating energy.

• 🔎 Total visibility: clear dashboards by asset, park, and portfolio.
• 🧠 Early detection: algorithms anticipate failures before they become costly.
• ⚙️ Smart scheduling: maintenance done when it adds value, not by calendar.
• 📈 Internal benchmarks: fair comparison between similar assets.

Practical result: less uncertainty, more energy delivered, and a decision cycle disciplined by data, without unnecessary complications.

Algorithm platform for solar and wind: from anomaly detection to revenue optimization

The heart of Enlitia’s proposal is a set of models that cross high-resolution SCADA data, meteorological series, and asset context to isolate loss causes. The same infrastructure supports distinct use cases: solar performance (modules, strings, inverters), wind performance (pitch, yaw, power curves), and also the commercial side, where the energy selling strategy is played out.

Anomalies that count money: examples that repeat in the field

In a photovoltaic park operating under self-consumption, a 2% drop in production due to dirt can go unnoticed for weeks; in a wind park, a yaw misalignment persistently reduces wind capture. By detecting early and suggesting actions with the highest return, the platform reallocates resources where the ROI is highest.

• 🌬️ Sub-optimal yaw/pitch: correction guided by the expected power curve.
• 🔌 Intermittent inverters: identification by repeating failure patterns.
• 🧽 Dirt and degradation: relative loss per string compared to the baseline.
• 🌤️ Intraday forecasts: adjust dispatches and maintenance to real weather.

More than just “alerts”, the platform delivers prioritized recommendations: what to do, when, and why. It also provides an auditable trail that facilitates conversations with O&M, investors, and technical audits.

Integration into routine: from park to portfolio, with real examples and practical steps

When technology enters the routine, that’s where value is created. Consider a “Southwest Wind Farm” with 40 MW and an “Energy Community” in a neighborhood in Aveiro with 120 solar rooftops. In both cases, the logic is the same: link data, define key indicators, and establish a weekly and monthly decision rhythm.

Simple and straightforward adoption roadmap

Integration with existing systems (SCADA, CMMS, ERP) is done via APIs and connectors. Teams retain their tools but now have an “intelligent layer” on top. The goal is not to replace functioning processes but to make them more precise.

• 🗺️ Map data: inventory critical points (strings, turbines, inverters).
• 🎯 Choose KPIs: PR, availability, losses by category.
• ⏱️ Rythms: daily alarm check, weekly review, monthly deep dive.
• 🧩 Governance: who decides, when, and based on what evidence.

In this model, a manager knows where to act and why a particular intervention brings return. For those managing energy communities, this translates into greater self-consumption, less return to the grid, and more stable bills.

To deepen understanding, it’s worth looking at APM approaches applied to renewables and correlating them with the local reality of each park or energy neighborhood.

Data as a strategic asset: quality, privacy, and cybersecurity at the core

If data is the new oil, refining it is essential. The effectiveness of Enlitia’s algorithms depends on the quality of measurements, temporal synchronization, and the integrity of records. In parallel, protecting operational and personal information (in collective self-consumption) is crucial to comply with standards and maintain trust.

Easy-to-apply best practices now

Small gestures elevate overall quality: calibrating sensors, checking clocks, standardizing point names, documenting interventions. When this happens, algorithms “see” better, and decision-making speeds up without noise.

• 🧭 NTP synchronization: aligned clocks across all equipment.
• 🧪 Plausibility checks: filters for out-of-scale values.
• 🔐 Profile-based access: least privilege and MFA.
• 🧾 Auditable trail: who changed what and when.

By 2025, digital maturity will be measured by the ability to make technology invisible and secure. When the data foundation is solid, artificial intelligence delivers on its promises: predictability, efficiency, and trust.

By crossing these concerns with the right platform, exposure to incidents is reduced, and decision quality increases, both technical and economic.

Benefits for solar houses, buildings, and communities: what you can apply now

It’s not just large parks that benefit. In residential buildings with photovoltaics, heat pumps, and batteries, the same logic of data and algorithms helps optimize consumption and extend the lifespan of equipment. It’s the bridge between comfort and efficiency, with results felt in bills and daily life.

Practical applications in your building or neighborhood

By integrating smart meters, production data, and usage patterns, prioritization becomes clear: consume when there is sunlight, charge batteries at the right moments, and avoid peaks. All this with simple, objective alerts, without technical complexity for users.

• 🏠 Smart self-consumption: schedule loads (AQS, EV) during peak production windows.
• 🔋 Battery management: optimized cycles and greater longevity of accumulators.
• 🧊 Efficient HVAC: adjust setpoints to real climate and occupancy.
• 🤝 Energy communities: equitable and transparent sharing among neighbors.

To take the first step, simply start with the data that already exists: bidirectional meter, inverter record, and consumption schedule. Then, evolve to integrations and automations. Small victories accumulate and make the energy transition simpler and more effective.

If you manage assets, an energy neighborhood, or your home’s energy, the message is clear: well-managed data + useful algorithms = cheaper, cleaner, and more predictable energy. Start with a pilot asset, validate gains, and scale systematically — that’s how innovation becomes routine with Enlitia.

Source: expresso.pt