Forecasting solar irradiance, particularly Global Horizontal Irradiance (GHI), has drawn much interest recently due to the rising demand for renewable energy sources. Many works have been proposed in the literature to forecast GHI by incorporating weather or environmental variables. Nevertheless, the expensive cost of the weather station hinders obtaining meteorological data, posing challenges in generating accurate forecasting models. Therefore, this work addresses this issue by developing a framework to reliably forecast the values of GHI even if meteorological data are unavailable or unreliable. It achieves this by leveraging lag observations of GHI values and applying feature extraction capabilities of the deep learning models. An ultra-short-term GHI forecast model is proposed using the Convolution Neural Network (CNN) algorithm, considering optimal heuristic configurations. In addition, to assess the efficacy of the proposed model, sensitivity analysis of different input variables of historical GHI observations is examined, and its performance is compared with other commonly used forecasting algorithm models over different forecasting horizons of 5, 15, and 30 minutes. A case study is carried out, and the model is trained and tested utilizing real GHI data from solar data located in Riyadh, Saudi Arabia. Results reveal the importance of employing historical GHI data in providing precise forecasting outcomes. The developed CNN-based model outperformed in ultra-short-term forecasting, showcasing average root mean square error results across different forecasting horizons: 2.262 W/m² (5min), 30.569 W/m² (15min), and 54.244 W/m² (30min) across varied day types. Finally, the findings of this research can permit GHI to be integrated into the power grid and encourage the development of sustainable energy systems.

Accurate ultra-short-term wind speed forecasting is essential for the reliable integration of wind energy into power grids; nevertheless, it is challenging due to the non-linearity and non-stationarity of wind signals. Therefore, this research introduces a novel multi-phase hybrid framework—VMD-PermEn-DBSCAN-ICEEMDAN—aimed at improving prediction accuracy through the systematic refinement of complex signal components. The process initiates with variational mode decomposition (VMD) to decompose raw wind speed data into intrinsic mode functions (IMFs). Permutation entropy (PermEn) is employed for feature extraction to address the complexity of these components, followed by density-based spatial clustering of applications with noise (DBSCAN) clustering to categorize IMFs exhibiting analogous dynamic patterns. A secondary decomposition phase employing enhanced complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) is aimed primarily at high-frequency clusters to reveal hidden fluctuations. These refined features serve as inputs for advanced deep learning models, such as long short-term memory (LSTM) networks, gated recurrent units (GRU), and their hybrid configurations. The framework was assessed utilizing wind speed data from Riyadh, Saudi Arabia, gathered at 5-minute intervals. Experimental findings indicated that the LSTM-GRU hybrid model consistently surpasses independent architectures and conventional machine learning methods, including artificial neural networks, support vector machines, and decision trees. The proposed framework attained a remarkable mean squared error of 0.00081 m²/s² and a coefficient of determination of 0.99954 for 5-minute forecasts. In addition, the study examined the effects of input lag lengths and forecasting resolutions of up to one hour, validating the model's durability and exceptional performance in ultra-short-term scenarios. The results underscore the effectiveness of integrating adaptive signal decomposition, intelligent clustering, and deep learning for accurate wind speed prediction, offering a dependable resource for energy management and grid stability.

Harvesting the power coming from the wind provides a green and environmentally friendly approach to producing electricity. To facilitate the ongoing advancement in wind energy applications, deep knowledge about wind regime behavior is essential. Wind speed is typically characterized by a statistical distribution, and the two-parameters Weibull distribution has shown its ability to represent wind speeds worldwide. Estimation of Weibull parameters, namely scale $\left(c\right)$ and shape $\left(k\right)$ parameters, is vital to describe the observed wind speeds data accurately. Yet, it is still a challenging task. Several numerical estimation approaches have been used by researchers to obtain c and k. However, utilizing such methods to characterize wind speeds may lead to unsatisfactory accuracy. Therefore, this study aims to investigate the performance of the metaheuristic optimization algorithm, Neural Network Algorithm (NNA), in obtaining Weibull parameters and comparing its performance with five numerical estimation approaches. In carrying out the study, the wind characteristics of three sites in Saudi Arabia, namely Hafer Al Batin, Riyadh, and Sharurah, are analyzed. Results exhibit that NNA has high accuracy fitting results compared to the numerical estimation methods. The NNA demonstrates its efficiency in optimizing Weibull parameters at all the considered sites with correlations exceeding 98.54.