Słownik AI
Kompletny słownik sztucznej inteligencji
Wind power production forecasting
Using machine learning models to anticipate wind turbine energy production in the short, medium, and long term, based on meteorological and historical data.
Dispatching optimization
Application of AI algorithms to optimally allocate energy resources from renewable sources to the grid, minimizing losses and costs.
Recurrent Neural Networks (RNN) for time series
AI architecture specialized in processing sequential data, used to model and forecast renewable energy production and consumption profiles.
Support Vector Machine (SVM) for resource classification
Supervised algorithm used to classify the energy potential of sites or to identify operational anomalies in renewable facilities.
Genetic algorithms for wind turbine placement
Optimization methods inspired by natural evolution, used to determine the optimal spatial configuration of a wind farm to maximize production and minimize wake effects.
Reinforcement learning for battery management
AI technique where an agent learns an optimal charging and discharging policy for an energy storage system, to smooth the intermittent production of renewables.
ARIMA-Neural Network hybrid model
Combination of statistical models (ARIMA) and neural networks to capture both linear trends and complex non-linear patterns in solar energy time series.
Anomaly detection with Isolation Forest
Efficient unsupervised algorithm for quickly identifying failures or underperformance in solar panels or wind turbines by isolating anomalous observations.
AI-based Energy Management System (EMS)
Intelligent software platform that optimizes in real-time the energy flow within microgrids or buildings, integrating renewable production forecasts and consumption patterns.
Federated Learning Data Aggregation
AI technique that enables training a global model on decentralized data (e.g., different solar farms) without centralizing raw information, thus preserving privacy.
CNN-based Solar Irradiance Forecasting
Use of convolutional neural networks to analyze satellite or meteorological images and accurately predict future solar irradiance, key for photovoltaic production.
Optimized Power Curtailment
AI-driven strategy to intentionally and optimally reduce renewable energy production to prevent grid overload when supply exceeds demand.
GARCH Volatility Modeling
Application of GARCH econometric models to quantify and forecast the variability (volatility) of renewable energy production, essential for risk management in energy markets.
Digital Twin of a Solar Farm
Dynamic virtual replica of a solar installation, powered by real-time data and AI models, to simulate, predict, and optimize its behavior and maintenance.
AI-based Tilt Angle Optimization
Algorithm that continuously calculates the optimal tilt angle of solar panels (for tracking systems) to maximize solar energy capture throughout the day and year.
Generative Adversarial Networks (GAN) for Data Synthesis
Use of GANs to create synthetic but realistic weather or production data, enabling training of more robust forecasting models when real data is scarce.