Applications of High-Resolution Wind Data for Engineers

Pleased to share our latest research published in the Journal of Wind Engineering & Industrial Aerodynamics: "Wind profile nowcasting and forecasting using machine learning" Jingyu Wei, Narazaki Yasutaka, Giuseppe Quaranta, 杨庆山, Christos T. Georgakis, and Cristoforo Demartino 🔍 What’s it about? We present a robust ML-based framework to predict vertical wind profiles — crucial for wind energy optimization and structural engineering applications. Nowcasting: real-time estimation from ground-level meteorological data using XGBoost. Forecasting: short-term predictions with LSTM networks using combined meteorological and lidar/tower data. 📊 Applied to over 114,000 wind profiles from the Cabauw experimental site (Netherlands), the models achieved high predictive accuracy, supporting: Digital twin optimization of wind turbines Proactive control strategies for structures under wind loading Enhanced operational planning in renewable energy 🔗 Read the paper (Open Access): https://lnkd.in/d7KD3Yys #MachineLearning #WindEngineering #DigitalTwins #RenewableEnergy #Lidar #WindTurbines #StructuralEngineering #DataScience #Nowcasting #Forecasting

#𝐋𝐎𝐆_𝐍𝐎_𝟏𝟖𝟏 𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐢𝐧𝐠 𝐑𝐮𝐧𝐰𝐚𝐲 𝐎𝐫𝐢𝐞𝐧𝐭𝐚𝐭𝐢𝐨𝐧 𝐓𝐡𝐫𝐨𝐮𝐠𝐡 𝐖𝐢𝐧𝐝 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬 Runway Orientation, a vital component of airport layout design. This work blends engineering design principles with meteorological data analysis to determine the safest and most efficient runway alignment for aircraft operations. 📌 𝐊𝐞𝐲 𝐓𝐞𝐜𝐡𝐧𝐢𝐜𝐚𝐥 𝐏𝐨𝐢𝐧𝐭𝐬 𝐂𝐨𝐯𝐞𝐫𝐞𝐝: 🌬️ 𝟏. 𝐈𝐦𝐩𝐨𝐫𝐭𝐚𝐧𝐜𝐞 𝐨𝐟 𝐖𝐢𝐧𝐝 𝐢𝐧 𝐑𝐮𝐧𝐰𝐚𝐲 𝐃𝐞𝐬𝐢𝐠𝐧 ● Aircraft performance during takeoff and landing is highly sensitive to wind direction ● Runways are aligned to maximize headwind and minimize crosswind, ensuring safety and operational efficiency 🛰️𝟐. 𝐃𝐚𝐭𝐚 𝐂𝐨𝐥𝐥𝐞𝐜𝐭𝐢𝐨𝐧 & 𝐖𝐢𝐧𝐝 𝐑𝐨𝐬𝐞 𝐌𝐞𝐭𝐡𝐨𝐝 ● Wind data collected for at least 5 years (velocity, frequency, and direction) ● In absence of official data, field measurement devices (anemometers, wind vanes) are used ● Wind Rose Diagram used for graphical representation: ● Circular plot showing wind frequency vs. direction ● Transparent template with runway centerline and crosswind limit boundaries is overlaid ● Rotated to determine orientation that satisfies minimum 95% usability (per ICAO/FAA standards) 📐 𝟑. 𝐃𝐞𝐬𝐢𝐠𝐧 𝐏𝐚𝐫𝐚𝐦𝐞𝐭𝐞𝐫𝐬: ● Usability Factor: ▪ Runway must be usable at least 95% of the time with acceptable crosswind ▪ If not, additional (crosswind) runway may be required ● Crosswind Component Limit (per FAA): ▪ Light aircraft (<13,000 lb): max crosswind = 10.5 knots ▪ Medium aircraft (13,000–200,000 lb): max = 13 knots ▪ Heavy aircraft (>200,000 lb): max = 16 knots ● Runway Bearing Determination: ▪ True bearing (based on wind rose result) ▪ Converted to magnetic bearing for real-world navigation and alignment ▪ Final alignment written as 2-digit runway number (e.g., 15/33) ✏️ 𝟒. 𝐏𝐫𝐚𝐜𝐭𝐢𝐜𝐚𝐥 𝐎𝐮𝐭𝐩𝐮𝐭 & 𝐀𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧 ● Helps define airport layout plan ● Ensures aircraft safety, fuel efficiency, and operational continuity ● Integrates with broader geotechnical planning and land use requirements This study reinforced my ability to integrate aerodynamic needs, wind analysis, and civil engineering design in airfield development especially under FAA and ICAO guidelines. #𝐑𝐮𝐧𝐰𝐚𝐲𝐎𝐫𝐢𝐞𝐧𝐭𝐚𝐭𝐢𝐨𝐧 #𝐀𝐢𝐫𝐩𝐨𝐫𝐭𝐃𝐞𝐬𝐢𝐠𝐧 #𝐖𝐢𝐧𝐝𝐑𝐨𝐬𝐞𝐌𝐞𝐭𝐡𝐨𝐝 #𝐈𝐂𝐀𝐎 #𝐅𝐀𝐀 #𝐀𝐯𝐢𝐚𝐭𝐢𝐨𝐧𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠 #𝐆𝐞𝐨𝐭𝐞𝐜𝐡𝐧𝐢𝐜𝐚𝐥𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠 #𝐀𝐢𝐫𝐟𝐢𝐞𝐥𝐝𝐏𝐥𝐚𝐧𝐧𝐢𝐧𝐠 #𝐂𝐫𝐨𝐬𝐬𝐰𝐢𝐧𝐝𝐂𝐨𝐦𝐩𝐨𝐧𝐞𝐧𝐭 #𝐑𝐮𝐧𝐰𝐚𝐲𝐁𝐞𝐚𝐫𝐢𝐧𝐠 #𝐂𝐢𝐯𝐢𝐥𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠 #𝐓𝐫𝐚𝐧𝐬𝐩𝐨𝐫𝐭𝐈𝐧𝐟𝐫𝐚𝐬𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞 #𝐒𝐦𝐚𝐫𝐭𝐌𝐨𝐛𝐢𝐥𝐢𝐭𝐲

You don’t often get second chances in project development.   But here is a challenge: our wind farms are designed for a lifetime over 25 years. However, we typically only have a few years of wind measurement data… are those years representative? So, we blend real measurement data with modelled data from historical weather models.   At RWE, we wanted to better understand the reliability of the modelled data. Thanks to a digitalisation and automation initiative from our Smart Data Pipeline team, colleagues Sam Williams and Gibson Kersting led one of the most thorough benchmarks of modelled wind data in our industry.   We tested 9 datasets, including reanalysis, mesoscale and large-eddy simulation (LES), against 370+ wind measurements across 190+ sites in every major wind market. Each dataset was standardised, cleaned through our Smart Data Pipeline, and assessed using robust statistical metrics.   The results provided valuable insight:   ERA5, the most widely used reanalysis dataset, performed more reliably than often assumed, particularly offshore and in simple terrain. Mesoscale models offer added resolution and typically improve significantly on reanalysis, but accuracy varies by provider and setup. LES (as shown in the animation, the generated winds which capture the complex atmospheric phenomenon that govern the weather), demonstrates clear benefits in modelling large offshore clusters, complex onshore sites where small-scale atmospheric effects become decisive, and high‑quality turbulence estimates. However, for simpler sites, the added value is limited.   This wasn’t an academic exercise. It was about understanding the tools we depend on, knowing when a model is good enough and when it isn’t.   Modelled wind data is incredibly powerful, but like any tool, its value depends on how and where it’s applied. With this benchmarking, we’ve taken a major step toward using it with greater precision and confidence across our global portfolio.   In a data-driven industry, precision isn’t a luxury. It’s a competitive edge. And that edge depends not just on having more data but on understanding it deeply.

🌬️ Did you know that before a single wind turbine is ever installed, there is an instrument that gathers the data that determines whether a site is viable for renewable energy? That instrument is called a Meteorological Mast (MET Mast). 🌍⚙️ A MET Mast is a tall, steel lattice or tubular tower erected on prospective wind farm sites to measure and record wind and atmospheric data over 12 to 24 months — the foundation of every Energy Analyst’s decision-making process. 💨 How Energy Analysts Use It Energy Analysts utilize the high-resolution data from the MET Mast to: Assess wind speed, direction, and turbulence intensity. Calculate capacity factors and energy yield for turbine layout design. Validate wind models and simulations used in financial feasibility studies. Optimize turbine positioning to maximize generation and minimize wake effects. 📊 The collected data helps transform mere wind potential into precise bankable energy forecasts that drive multi-million-rand investment decisions. ⚙️ Mechanical Components of a MET Mast — and Their Functions 1⃣ Anemometers – Measure wind speed at multiple heights. Cup anemometers or ultrasonic sensors ensure redundancy and accuracy. 2⃣ Wind Vanes – Indicate wind direction, crucial for understanding prevailing wind patterns. 3⃣ Temperature Sensors – Record air temperature gradients, vital for calculating air density and power output. 4⃣ Barometric Pressure Sensors – Track atmospheric pressure influencing air mass movement and turbine efficiency. 5⃣ Relative Humidity Sensors – Measure moisture levels, helping analysts understand weather stability and icing risks. 6⃣ Data Logger – The “brain” of the mast that collects, stores, and timestamps all meteorological readings. 7⃣ Solar Radiation Sensors (Pyranometers) – Sometimes installed to assess solar energy potential alongside wind data. 8⃣ Booms and Guy Wires – Ensure structural stability and hold sensors at specific calibrated distances from the mast body to prevent wind distortion. 9⃣ Lightning Protection and Grounding Systems – Protect sensitive equipment from electrical damage. 🔟 Telemetry Unit – Transmits real-time data remotely to Energy Analysts for ongoing performance monitoring. A fully instrumented MET Mast can stand 60–120 meters high, transforming invisible air currents into the data backbone of the renewable energy revolution. 🌱⚡