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This study introduces an optimized hybrid deep learning approach that leverages meteorological data to improve short-term wind energy forecasting in desert regions. Over a year, various machine learning and deep learning models have been tested across different wind speed categories, with multiple performance metrics used for evaluation. Hyperparameter optimization for the LSTM and Conv-Dual Attention Long Short-Term Memory (Conv-DA-LSTM) architectures was performed. A comparison of the techniques indicates that the deep learning methods consistently outperform the classical techniques, with Conv-DA-LSTM yielding the best overall performance with a clear margin. This method obtained the lowest error rates (RMSE: 71.866) and the highest level of accuracy (R2: 0.93). The optimization clearly works for higher wind speeds, achieving a remarkable improvement of 22.9%. When we look at the monthly performance, all the months presented at least some level of consistent enhancement (RRMSE reductions from 1.6 to 10.2%). These findings highlight the potential of advanced deep learning techniques in enhancing wind energy forecasting accuracy, particularly in challenging desert environments. The hybrid method developed in this study presents a promising direction for improving renewable energy management. This allows for more efficient resource allocation and improves wind resource predictability. [ABSTRACT FROM AUTHOR]

Precise solar radiation forecasting can provide great benefits and solutions for smart grid distribution and electricity management. However, its non-stationary behavior and randomness render its estimation very difficult. In this respect, a new hybrid learning approach is proposed for multi-hour global solar radiation forecasting, relying on Convolutional Neural Network (CNN), Nonparametric Gaussian Process Regression (GPR), Least Support Vector Machine (LS-SVM), and Extreme Learning Machine (ELM) as essence predictors. Then compressive sensing technique is applied to perform a hybridization scheme of the model's output. Hourly global solar radiation data from two sites in Algeria with different climate conditions are used to evaluate the full potential of the integrated model, with stationarity checks with an advanced clear sky model (MecClear model). Different comparative simulations show the superiority of the proposed pipeline in forecasting hourly global solar radiation data for multi-hour ahead compared to the stand-alone model. Experimental results show that the proposed hybridization methodology can effectively improve the prediction accuracy and outperforms benchmarking models during all the forecasting horizons. [ABSTRACT FROM AUTHOR]

• •Development of an enhanced hybrid model for multi-hour ahead DNI forecasting. • •Comprehensive performance analysis using four different DNI databases. • •Implementation of the GA-GOA Algorithm for CSP Optimization. • •Assessment of electricity generation from CSP plants for multi-hour ahead forecasting. This study addresses the critical need for precise Direct Normal Irradiation forecasting in concentrating solar power systems to enhance performance and manage power generation intermittency. We propose a novel hybrid model that combines Variation Mode Decomposition, Swarm Decomposition Algorithm, Random Forest for feature selection, and Deep Convolutional Neural Networks, aiming to improve the forecasting accuracy. This model covers the entire process from Direct Normal Irradiation forecasting to heliostat field optimization and electricity generation. We validated the model across four globally diverse regions, taking into account their distinct climates and meteorological conditions. The results show that our model aligns closely with actual measurements and outperforms existing forecasting methods in terms of precision and stability. The forecasting performance was assessed using normalized Root Mean Square Error, with results ranging from 0.75% to 3.4% across different regions. This demonstrates the model's potential for real-world application in concentrating solar power systems, optimizing heliostat field effectiveness, and reliably forecasting electricity production for grid management. [ABSTRACT FROM AUTHOR]