ECopti

Optimize PV Energy with AI-Driven Forecasting

Image 1
Image 2
Image 3
Image 4

ECopti: Intelligent Optimization for PV Systems

The new ECopti is an advances optimization algorithm designed for Self-Consumption and Arbitrage Optimization. It dynamically manages energy costs using:

  • A fixed daily tariff profile
  • Daily TSO spot prices for European markets
  • A combination of the above

Powered by sophisticated ML algorithms, ECopti makes load consumption predictions based on site-specific historical data, while PV energy forecasts incorporate past production data, local weather patterns, and site conditions.

Optimization 1

Seamless Integration with ENcombi EMS

Integrated within the ENcombi EMS, ECpvX, ECopti runs real-time optimizations directly on-site, leveraging live predictions and energy cost data. The ECcloud portal provides a digital twin, offering an interactive view forecasts and expected outcomes.

Advanced Grid Integration with ECgrid

For even greater flexibility, adding ECgrid alongside ECpvX enables participation in frequency ancillary services, including:

  • aFRR, mFRR, FRR, FCR-N and FCR-D in the Nordic synchronous area
  • FCR in the Central Europe synchronous area

While Encombi does not handle market bidding directly, ECgrud integrates seamlessly with third-party aggregators via the Encombi API, allowing users to interact with multiple aggregators without being locked into a single provider.

AI

Optimize PV Energy with AI-Driven Forecasting

AI and Machine Learning (ML) are revolutionizing the way we predict and optimize photovoltic (PV) energy production and load consumption. By analyzing historical data and enviromental factors, AI models provide highly accurate PV output forecasts, considering weather patterns, seasonal changes, and site-specific conditions. Similarly, ML algorithms predict load consumption based on past usage, external influences, and user behavior.

Optimize PV Energy with AI-Driven Forecasting

With precise forecasting, energy self-consumption strategies can be optimized - maximizing on-site PV usage and reducing grid dependency. These insights also power arbitrage algorithms, identifying the best times to store energy or sell excess power based on market prices, leading to both financial savings and a more sustainable energy grid.