Data Science
We explore advanced machine learning (ML) and artificial intelligence (AI) methods to enhance analytical understanding of energy systems across wind, solar, hydro, and battery storage applications. Our work focuses on research-oriented modeling using supervised learning, time-series forecasting, clustering, and anomaly-pattern exploration to study variability, identify data irregularities, and improve interpretability of complex energy datasets.
We also investigate AI-based approaches for conceptual system evaluation, including analyses related to turbine behavior, solar inverter performance characteristics, battery charge–discharge patterns, and broader system-level interactions. These studies aim to provide deeper insight into the factors that influence efficiency and operational behavior, rather than deliver real-time optimization or predictive maintenance services.
By integrating SCADA, meteorological, and operational datasets, our analytical frameworks help build a clearer technical understanding of renewable energy system dynamics and support informed planning and early-stage decision studies for developers, researchers, and stakeholders.
AI-based Forecasting
We develop advanced forecasting models using state-of-the-art AI methods—including Transformers, LSTMs, GRUs, ANNs, and hybrid approaches—to study the temporal dynamics and variability of wind and solar power generation. These models are designed to capture nonlinear relationships in historical production data, meteorological inputs, and system behavior, providing deeper insight into the factors that influence renewable energy variability.
Our work also evaluates ensemble approaches that combine deep learning with traditional machine learning techniques such as Random Forests, Gradient Boosting, and Support Vector Regression. These ensembles help improve modeling robustness across diverse weather conditions and geographical contexts.
The forecasting studies aim to support conceptual analyses, resource variability assessments, and exploratory research in grid-informed planning. By enhancing understanding of short-term and long-term generation patterns, these models contribute to more informed evaluation of renewable energy performance and uncertainty.
Anomaly Detection & Diagnostics
We investigate data-driven approaches for identifying irregular patterns and unexpected behaviors in renewable energy datasets. Using techniques such as clustering, unsupervised learning, statistical change-point detection, and time-series analysis, we study variability and deviations in wind, solar, and storage system measurements.
Rather than providing operational asset diagnostics, our work focuses on understanding the underlying data characteristics, exploring potential indicators of performance shifts, and improving interpretability of complex signals. These analytical methods support early-stage assessments, research studies, and conceptual evaluations by revealing patterns that may influence long-term performance modeling and resource analysis.
Data Denoising & Reconstruction
We explore advanced techniques for addressing gaps, noise, and inconsistencies in wind-measurement datasets. Our work focuses on developing analytical and machine-learning methods to study how missing or faulty data can be reconstructed in systems such as SODAR, LiDAR, and met masts—particularly in complex terrain and challenging atmospheric conditions.
These methods include spatio-temporal interpolation, AI-based regression models, physics-informed neural networks (PINNs), and ensemble approaches such as Random Forests and Gradient Boosting. We also investigate the use of external reference sources, including ERA5 reanalysis data, to better understand correlations and improve reconstruction robustness.
Additional steps such as outlier detection, bias exploration, and uncertainty analysis help enhance interpretability and provide clearer insight into the reliability of reconstructed datasets. The resulting analyses support early-stage evaluations, wind-profile studies, and conceptual assessments where more complete data coverage is needed for technical understanding.
Market Analysis & Intelligence
We explore how AI and data-driven methods can improve the understanding of energy-market behavior and broader industry trends. Using techniques such as machine learning, time-series modeling, and natural language processing, we study patterns in complex datasets to generate clearer analytical perspectives.
Our services cover:
-
Market forecasting – examining demand, pricing patterns, and long-term trend behavior using statistical and machine-learning approaches.
-
Customer analytics – applying clustering and segmentation methods to investigate behavioral patterns and variability within user or stakeholder groups.
-
Competitive intelligence – studying publicly available data and broad market signals to understand how sector dynamics evolve over time.
-
Scenario testing – exploring how different assumptions or external conditions may influence market trajectories through sensitivity and uncertainty analyses.
Rather than providing operational decision-support tools, our work centers on conceptual studies and research-driven insights that help clarify market dynamics and support early-stage evaluations.