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Over the past decades, energy forecasting has attracted many researchers. The electrification of the modern world influences the necessity of electricity load, wind energy, and solar energy forecasting in power sectors. Energy demand increases with the increase in population. The energy has inherent characteristics like volatility and uncertainty. So, the design of accurate energy forecasting is a critical task. The electricity load, wind, and solar energy are important for maintaining the energy supply-demand equilibrium non-conventionally. Energy demand can be handled effectively using accurate load, wind, and solar energy forecasting. It helps to maintain a sustainable environment by meeting the energy requirements accurately. The limitation in the availability of sufficient data becomes a hindrance to achieving accurate energy forecasting. The transfer learning strategy supports overcoming the hindrance by transferring the knowledge from the models of similar domains where sufficient data is available for training. The present study focuses on the importance of energy forecasting, discusses the basics of transfer learning, and describes the significance of transfer learning in load forecasting, wind energy forecasting, and solar energy forecasting. It also explores the reviews of work done by various researchers in electricity load forecasting, wind energy forecasting, and solar energy forecasting. It explores how the researchers utilized the transfer learning concepts and overcame the limitations of designing accurate electricity load, wind energy, and solar energy forecasting models.

Reliable probabilistic wind speed forecasts are essential for integrating renewable energy into power grids and managing operational uncertainty. This study compares Quantile Regression Forests (QRF), Bayesian Additive Regression Trees (BART), and Gaussian Process Regression (GPR) under at-site and regional pooled frameworks using 21 years (2000–2020) of daily wind data from eleven stations in New South Wales and Queensland, Australia. Models are evaluated via strict year-based holdout validation across seven metrics: RMSE, MAE, R2, bias, correlation, coverage, and Continuous Ranked Probability Score (CRPS). Regional QRF achieves exceptional point forecast stability with minimal RMSE increase but suffers persistent under-coverage, rendering probabilistic bounds unreliable. BART attains near-nominal coverage at individual sites but experiences catastrophic calibration collapse under regional pooling, driven by fixed noise priors inadequate for spatially heterogeneous data. In contrast, GPR maintains robust probabilistic skill regionally despite larger point forecast RMSE penalties, achieving the lowest overall CRPS and near-nominal coverage through kernel-based variance inflation. Variable importance analysis identifies surface pressure and minimum temperature as dominant predictors (60–80%), with spatial covariates critical for regional differentiation. Operationally, regional QRF is prioritised for point accuracy, regional GPR for calibrated probabilistic forecasts in risk-sensitive applications, and at-site BART when local data suffice. These findings show that Bayesian machine learning methods can effectively navigate the trade-off between local specificity and regional pooling, a challenge common to wind forecasting in diverse terrain globally. The methodology and insights are transferable to other heterogeneous regions, providing guidance for probabilistic wind forecasting and renewable energy grid integration.

This review examines advancements and methodologies in long-term wind-speed and -power forecasting. It emphasizes the importance of these techniques in integrating wind energy into power systems. Covering a range of forecasting timeframes from monthly to multiyear projections, this paper highlights the diversity of applications and approaches. These applications and approaches are essential for managing the inherent variability and unpredictability of wind energy. Various forecasting methods, including statistical models, machine-learning techniques, and hybrid models, are discussed in detail. The review demonstrates how these methods improve forecast accuracy and reliability across different temporal and geographical scales. It also identifies significant challenges such as model complexity, data limitations, and the need to accommodate regional variations. Future improvements in wind forecasting include enhancing model integration, employing higher resolution data, and fostering collaborative research to further refine forecasting methodologies. This comprehensive analysis aims to advance knowledge on wind forecasting, facilitate the efficient integration of wind power into global energy systems, and contribute to sustainable energy development goals.

In aviation, accurate wind prediction is crucial, especially during takeoff and landing at complex sites like Gran Canaria Airport. This study evaluated five Deep Learning models: Long Short-Term Memory (LSTM), Vanilla Recurrent Neural Network (vRNN), One-Dimensional Convolutional Neural Network (1dCNN), Convolutional Neural Network Long Short-Term Memory (CNN-LSTM), and Gated Recurrent Unit (GRU) for forecasting wind speed and direction. The LSTM model demonstrated the highest precision, particularly for extended forecasting periods, achieving a mean absolute error (MAE) of 1.23 m/s and a circular MAE (cMAE) of 15.80° for wind speed and direction, respectively, aligning with World Meteorological Organization standards for Terminal Aerodrome Forecasts (TAF). While the GRU and CNN-LSTM also showed promising results, and the 1dCNN excelled in wind direction forecasting over shorter intervals, the vRNN lagged in performance. Additionally, the autoregressive integrated moving average model underperformed relative to the DL models, underscoring the potential of DL, particularly LSTM, in enhancing TAF accuracy at airports with intricate wind patterns. This study not only confirms the superiority of DL over traditional methods but also highlights the promise of integrating artificial intelligence into TAF automation.

Accurate wind speed forecasts are essential for optimizing the efficiency of wind energy operations. Currently, there is limited research on nationwide assessment of predictive performance in multiple numerical weather prediction (NWP) models for wind speed at turbine hub height over China, especially concerning wind ramp events. Utilizing observed measurements from 262 wind farms, this study evaluated the performance of five NWP models in forecasting the mean state and spatiotemporal variations of wind speed as well as wind ramps. The results indicated that the European Center for Medium‐Range Weather Forecast Integrated Forecasting System (ECMWF–IFS) performed the best in forecasting climatological wind speed with a temporal correlation coefficient (TCC) of 0.74 and root mean square error (RMSE) of 2.34 m s−1. Although not widely utilized in China, the model from Meteo‐France (MF–ARPEGE) showed promising potential for wind energy forecasting with a TCC of 0.72 and RMSE of 2.45 m s−1. In terms of temporal variations of wind speed, all the models could reasonably predict the seasonal variations of wind speed, whereas only three NWP models were able to capture the characteristics of the observed diurnal variation. An error decomposition analysis further revealed that the primary source of predicted error for wind speed was the sequence error component (SEQU), indicating the model errors were mainly attributed from the temporal inconsistency between forecasts and observations. Furthermore, the occurrences of wind ramps were generally underestimated by NWP models, while this shortcoming can be partly overcome by improving the time resolution of NWP models. European Center for Medium‐Range Weather Forecast Integrated Forecasting System (ECMWF–IFS) and Meteo‐France (MF–ARPEGE) performed the best in forecasting the hub‐height wind speed, accounting for 80.4% and 89.9% of total 262 wind farms with the highest temporal correlation coefficient (TCC) and the lowest root mean square error (RMSE), respectively.

Reliable ultra-short-term and short-term wind speed forecasting is pivotal for clean energy development and grid operation planning. During the wind forecasting process, decomposing the measured wind speed into data with different frequencies is a solution for overcoming the nonlinearity and the randomness of the natural wind. Existing forecasting methods, a hybrid method based on empirical mode decomposition and the back propagation neural network optimized by genetic algorithm (EMD-GA-BPNN), rely on partial decomposing the measured wind speed into data with different frequencies and subsequently achieving forecasting results from machine learning algorithms. However, such method can roughly divide IMF signals in different frequency domains, but each frequency domain contains signals with multiple frequencies. The condition reflects that the method cannot fully distinguish wind speed into data with different frequencies and thus it compromises the forecasting accuracy. A complete decomposition of measured wind speed can reduce the complexity of machine learning algorithm, and has become a useful approach for precise simulations of wind speed. Here, we propose a novel hybrid method (CEEMDAN-GA-BPNN) based on the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) by completely decomposing the measured wind speed. The decomposition results are put into the back propagation neural network optimized by genetic algorithm (GA-BPNN), and the final forecasting results are achieved by combining all the output values by GA-BPNN for each decomposition result from CEEMDAN. We benchmark the forecasting accuracy of the proposed hybrid method against EMD-GA-BPNN integrated by EMD and GA-BPNN. From a wind farm case in Yunnan Province, China, both for ultra-short-term forecasting (15 minutes) and short-term forecasting (1 hour), the performance of the proposed method exceeds EMD-GA-BPNN in several criteria, including root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and coefficient of determination (R 2 ). The forecasting accuracy in decomposed components of low frequencies outperform components of high and middle frequencies. Fine improvement of the error metric (in percentage) in ultra-short-term/short-term forecasting is found by the complete decomposition method CEEMDAN-GA-BPNN: RMSE (7.0% and 8.6%), MAE (7.41% and 7.9%), MAPE (11.0% and 8.7%), and R 2 (2.2% and 11.0%), compared with the incomplete decomposing method EMD-GA-BPNN. Our result suggests that CEEMDAN-GA-BPNN could be an accurate wind speed forecasting tool for wind farms development and intelligent grid operations.

This study focuses on the importance of reliable surface wind forecasts for various sectors, particularly energy production. Traditional numerical weather prediction models are facing limitations and increasing complexity, leading to the development of machine learning models as alternatives or supplements. The research consists of two stages. In the first stage, the ERA5 database is used to evaluate the long-term performance of different combinations of features and two tree-based algorithms for predicting surface wind characteristics (speed and direction) in Cairo. The XGBoost algorithm slightly outperforms the Random Forest algorithm, especially when combined with appropriate feature selection. Even three years after the training period, the results remain very good, with an RMSE of 0.59 m/s, rRMSE of 17%, and R2 of 0.84. The second stage assesses the multivariate approach's ability to forecast wind speed evolution at different time horizons (1–12 h) during a week characterized by significant wind dynamics. The forecasts demonstrate excellent agreement with observations at a 1-h time horizon, with an RMSE of 0.35 m/s, rRMSE of 7.6%, and R2 of 0.98, surpassing or comparable to other literature results. However, as the time lag increases, the RMSE (0.86, 1.14, and 1.51 m/s for 3, 6, and 12 h, respectively) and rRMSE (18.7%, 24.8%, and 32.9% for 3, 6, and 12 h, respectively) also increase, while R2 decreases (0.86, 0.79, and 0.60). Furthermore, the wind variations' amplitude is underestimated. To address this bias, a simple correction method is proposed.

Accurate wind speed forecasting is crucial for the efficient operation of renewable energy platforms, such as wind turbines, as it facilitates more effective management of power output and maintains grid reliability and stability. However, the inherent variability and intermittency of wind speed present significant challenges for achieving precise forecasts. To address these challenges, this study proposes a novel method based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and a deep learning-based Long Short-Term Memory (LSTM) network for wind speed forecasting. In the proposed method, CEEMDAN is utilized to decompose the original wind speed signal into different modes to capture the multiscale temporal properties and patterns of wind speeds. Subsequently, LSTM is employed to predict each subseries derived from the CEEMDAN process. These individual subseries predictions are then combined to generate the overall final forecast. The proposed method is validated using real-world wind speed data from Austria and Almeria. Experimental results indicate that the proposed method achieves minimal mean absolute percentage errors of 0.3285 and 0.1455, outperforming other popular models across multiple performance criteria.

Precise wind power forecasting is essential for the successful integration of wind power into the power grid and for mitigating the potential effects of wind power on the power system. To enhance the precision of predictions, a hybrid VMD-BiTCN-Psformer model was devised. Firstly, VMD divided the original sequence into several data components with varying time scales. Furthermore, the BiTCN network was utilized to extract the sequence features. These features, along with the climate features, were then input into the positional encoding and ProbSparse self-attention improved Transformer model. The outputs of these models were combined to obtain the ultimate wind power prediction results. For the prediction of the wind power in Fujian Province on April 26, four additional models were developed for comparison with the VMD-BiTCN-Psformer model. The VMD-BiTCN-Psformer model demonstrated the greatest level of forecast accuracy among all the models. The R2 increased by 22.27%, 12.38%, 8.93%, and 2.59%, respectively.