In the heart of Türkiye’s Western Black Sea Region, a groundbreaking study is reshaping how we think about wind energy. Ziya Demirkol, a researcher from Kastamonu University’s Department of Electrical, Vocational of School, has been crunching numbers and running complex models to predict the future of wind power in this strategically important area. His work, published in Energies, is not just about understanding wind patterns; it’s about unlocking the economic potential of wind farms and steering investment towards the most profitable ventures.
Demirkol’s research focuses on hourly wind speed data from 23 meteorological stations, collected over five years from 2020 to 2024. He used the Weibull distribution to estimate annual energy production and then took it a step further with the Long Short-Term Memory (LSTM) model, a type of deep learning algorithm, to forecast future wind speeds. “The LSTM model allows us to capture the temporal dynamics of wind speed data, providing more accurate predictions,” Demirkol explains. This isn’t just about academic curiosity; it’s about making wind energy a viable and profitable option for investors.
The study’s findings are a roadmap for the energy sector. By comparing the measured and forecasted data, Demirkol identified the most promising locations for wind farms. Stations like Zonguldak South, Sinop İnceburun, and Bartın South emerged as hotspots for high revenues and unit electricity profits. But the real game-changer is the LSTM-based forecast for 2025, which highlights seven stations—Sinop İnceburun, Bartın South, Zonguldak South, İnebolu, Cide North, Gebze Köşkburnu, and Amasra—as prime candidates for investment.
This research isn’t just about pinpointing the best spots for wind farms; it’s about optimizing the entire process. By using advanced machine learning techniques, Demirkol’s work provides a blueprint for minimizing errors in wind power forecasting, a critical factor in maximizing efficiency and profitability. “Accurate forecasting is key to optimizing the efficiency and profitability of wind power plants,” Demirkol notes. This precision could revolutionize how energy companies approach wind power, making it a more attractive and reliable option in the renewable energy mix.
The implications for the energy sector are vast. As the world shifts towards renewable energy sources, accurate forecasting and cost-benefit analysis become crucial. Demirkol’s study offers a template for other regions, demonstrating how data-driven insights can guide investment and policy decisions. It’s a step towards a future where wind energy is not just sustainable but also economically viable.
As we look ahead, Demirkol’s work could shape the future of wind energy in Türkiye and beyond. By leveraging deep learning and advanced statistical models, we can unlock the full potential of wind power, making it a cornerstone of the global energy transition. The winds of change are blowing, and with studies like this, we’re better equipped to harness their power.