article · Scientific Reports
Climate change creates operational challenges for wind energy generation by shifting key weather variables such as wind speed, patterns, and temperature. Because traditional methods struggle to capture complex non-linear relationships in climate data, a hybrid deep learning model combining a Convolutional Neural Network, ResNet50, and Long Short-Term Memory units was developed. This architecture forecasts both temperature and wind power output. Tested on three publicly available datasets covering turbine operational data, regional weather history, and multi-location power generation, the model outperformed five standard regression baselines. It achieved coefficient of determination scores exceeding 98 percent for both target variables across all test benchmarks. Furthermore, the architecture was applied to project climate trends up to 2030, showing capability for extended forecasting and power system planning.
Accurate forecasting of wind power and temperature is vital for the stable operation of wind energy installations. As climate change alters weather dynamics, highly accurate predictive models allow energy authorities and grid managers to anticipate power fluctuations, improve system planning, and maintain energy security.
This framework could be applied in forecasting software for wind energy operators, grid managers, and power system planners requiring accurate climate and yield projections. Based on validation against historical and public datasets rather than live deployment, the technology is currently at an applied experimental stage and requires field testing within active control systems before reaching commercial deployment.
AI-generated from the published abstract. Always read the original work before citing.
Climate change poses a significant challenge to wind energy production. It involves long-term, noticeable changes in key climatic factors such as wind power, temperature, wind speed, and wind patterns. Addressing climate change is essential to safeguarding our environment, societies, and economies. In this context, accurately forecasting temperature and wind power becomes crucial for ensuring the stable operation of wind energy systems and for effective power system planning and management. Numerous approaches to wind change forecasting have been proposed including both traditional forecasting models and deep learning models. Traditional forecasting models have limitations since they cannot describe the complex nonlinear relationship in climatic data, resulting in low forecasting accuracy. Deep learning techniques have promising non-linear processing capabilities in weather forecasting. To further advance the integration of deep learning in climate change forecasting, we have developed a hybrid model called CNN-ResNet50-LSTM, comprising a Convolutional Neural Network (CNN), a Deep Convolutional Network (ResNet50), and a Long Short-Term Memory (LSTM) model to predict two climate change factors: temperature and wind power. The experiment was conducted using three publicly available datasets: Wind Turbine Scada (Scada) Dataset, Saudi Arabia Weather history (SA) dataset, and Wind Power Generation Data for 4 locations (WPG) dataset. The forecasting accuracy is evaluated using several evaluation metrics, including the coefficient of determination ([Formula: see text]), Mean Squared Error (MSE), Mean Absolute Error (MAE), Median Absolute Error (MedAE) and Root Mean Squared Error (RMSE). The proposed CNN-ResNet50-LSTM model was also compared to five regression models: Dummy Regressor (DR), Kernel Ridge Regressor (KRR), Decision Tree Regressor (DTR), Extra Trees Regressor (ETR), and Stochastic Gradient Descent Regressor (SGDR). Findings revealed that CNN-ResNet50-LSTM model achieved the best performance, with [Formula: see text] scores of 98.84% for wind power forecasting in the Scada dataset, 99.01% for temperature forecasting in the SA dataset, 98.58% for temperature forecasting and 98.35% for wind power forecasting in the WPG dataset. The CNN-ResNet50-LSTM model demonstrated promising potential in forecasting both temperature and wind power. Additionally, we applied the CNN-ResNet50-LSTM model to predict climate changes up to 2030 using historical data, providing insights that highlight its potential for future forecasting and decision-making.
This page summarises published work. The authoritative version sits with the publisher.
DOI: 10.1038/s41598-025-97401-9
Is something wrong with this record? Report it or request removal.
Discussion
Have you built on this work, tried to replicate it, or seen it applied in practice? Share what you know. Verified researchers and MARATTO™ domain experts can open a discussion, and any member can reply. Contributions are reviewed before they appear.
No discussion yet. Open the first thread.
New to MARATTO™? Create a free account.
