A novel cloud–edge collaborative intelligence framework which enables synergy between large and small models for STWPF and outperforms state-of-the-art baselines, highlighting the practical value of the framework.
Abstract
Accurate short-term wind power forecasting (STWPF) is critical to maintaining grid stability and improving renewable integration. Physical, statistical, and deep-learning methods are widely used and have produced promising results. However, the above methods often struggle to balance prediction accuracy with computational efficiency and to capture spatiotemporal dependencies while keeping hyperparameter tuning manageable. To address these limitations, we propose a novel cloud–edge collaborative intelligence framework which enables synergy between large and small models for STWPF. The framework consists of two components: a LightGBM module for efficient feature selection, and a cloud module with a temporal 1D Convolutional Neural Network (1D-CNN) for local temporal pattern extraction and cross-channel interactions, followed by a Bidirectional Long Short-Term Memory (BiLSTM) for bidirectional temporal modeling. Moreover, we use a resource aware tuning strategy that speeds up tuning without loss of accuracy. Through extensive experiments on real-world datasets, our method outperforms state-of-the-art baselines, highlighting the practical value of our framework.
Experimental results show that MSCNN-ResLSTM achieves higher forecasting accuracy and greater stability across the full 24-step prediction horizon, consistently outperforming all competing baselines while effectively suppressing recursive error propagation.
Yu-Hang Zhang, Yi-Ting Zhao, Yu-Jing Meng et al.· De Computis· 0 citations
Accurate solar radiation forecasting is essential for photovoltaic energy generation, smart grid stability, and renewable energy management. This study proposes a lightweight hybrid deep learning framework that combines a transformer encoder and Gated Rrecurrent Uunit (GRU) network for short-term solar radiation forecasting in Makkah and Madinah, Saudi Arabia. Hourly meteorological data from the NASA POWER dataset (2020–2025) were utilized, including solar radiation intensity, temperature, humidity, wind speed, cloud amount, rainfall, surface pressure, and dew point temperature. A preprocessing pipeline consisting of missing value treatment, outlier removal, normalization, timestamp alignment, and data cleaning was applied to improve data quality. Feature engineering techniques were incorporated to capture temporal dependency, meteorological interactions, weather dynamics, and solar variability patterns. The transformer encoder was used to learn long-range temporal dependencies through multi-head self-attention, while the GRU layer modeled sequential temporal dynamics efficiently. Hyperparameter optimization was performed using Bayesian optimization with Optuna. The experimental results demonstrate that the proposed transformer GRU framework achieved a Mean Absolute Error (MAE) of 0.014, Root Mean Square Error (RMSE) of 0.0219, and a coefficient of determination (R2) of 0.98. The proposed model outperformed ARIMA, LSTM, GRU, and XGBoost models while maintaining stable performance across varying weather conditions and forecasting horizons.
With the expansion of clean energy and momentum toward the United Nations' 2030 Sustainable Development Goals, wind power has emerged as a key element in the transition to renewable energy. However, wind energy’s unpredictable nature, driven by volatile weather conditions, makes accurate forecasting difficult. Methods like Long Short-Term Memory (LSTM) models have proven effective for temporal dependency tasks, while newer variants, such as Nested LSTM (NLSTM), offer enhanced capabilities for modeling complex time relationships. Temporal Convolutional Networks (TCNs), developed under the Convolutional Neural Network (CNN) architecture, have also gained attention as promising alternatives for sequence modeling. Therefore, this study proposes a hybrid model combining TCN and NLSTM to leverage the strengths of both architectures. It further integrates Variational Mode Decomposition (VMD) for handling nonstationary data, using both power and weather data for improved prediction performance. Additionally, a mutation-inspired modification of the Driving Training-Based Optimization (DTBO) algorithm dynamically tunes the model’s hyperparameters. The results demonstrate that the enhanced DTBO-aided TCN-NLSTM outperforms single-network architecture, achieving up to a 12.7% reduction in Root Mean Square Error (RMSE).
Accurate forecasting of solar irradiance and wind speed is essential for improving hybrid renewable energy systems and ensuring grid stability. This study develops and evaluates a multi-output deep learning framework for the simultaneous prediction of global horizontal irradiance (GHI) and wind speed across multiple Indian regions. Hourly data from the National Solar Radiation Database (NSRDB) for the period 2015–2020 were used to train light gradient boosting machine (LightGBM), long short-term memory (LSTM), bidirectional long short-term memory (BiLSTM), and convolutional long short-term memory (ConvLSTM) models, with cross-regional transfer learning applied across Tamil Nadu, Kerala, Karnataka, and Andhra Pradesh. Among the models, ConvLSTM achieved the best performance with a mean absolute error (MAE) of 0.061 and an R² value of approximately 0.91, while BiLSTM demonstrated comparable accuracy with lower computational cost. The proposed framework emphasizes cross-regional transferability, demonstrating robust generalization across heterogeneous climatic conditions. Error distribution analysis further indicates improved prediction stability, with ConvLSTM exhibiting lower variability compared to other models. These results support scalable and reliable renewable energy forecasting, with practical implications for grid operation, power electronic control, and hybrid energy system management.
S. Selvi, Annamalai Muthu, Murali Narayanamurthy et al.· International Journal of Pow...· 0 citations
Predictions of solar power output that are close to the mark may help with grid stability, energy management, and the integration of renewable energy sources. Prediction using conventional statistical models is challenging due to the substantial influence of nonlinear meteorological variables and temporal variations on solar power output. A deep learning-based forecasting framework integrating an attention mechanism and a Temporal Convolutional Network (TCN) is proposed in this study to enhance the precision of solar energy prediction. A number of feature engineering and pre-processing approaches are used by the model, including lag features, rolling statistics, temporal indicators, and outlier reduction using the Interquartile Range (IQR) methodology. To store long-range temporal associations, the TCN architecture employs dilated causal convolutions; the attention mechanism highlights the most important time steps in the input sequence. The proposed model outperforms state-of-the-art baseline models such as Long Short-Term Memory (LSTM) and traditional TCN when tested on a publicly available solar energy dataset. It can accomplish 253.32 MW Mean Absolute Error (MAE), 325.04 MW Root Mean Square Error (RMSE), 2.30% Mean Absolute Percentage Error (MAPE), and 0.9804 Coefficient of Determination (R2). These parameters are evaluated by experimental means.
Mohamed Shaik Honnurvali, Mazhar Baloch, Touqeer Ahmed et al.· IEEE Open Access Journal of...· 0 citations
Accurate wind power forecasting is imperative for ensuring grid stability and facilitating the large-scale integration of renewable energy—both central pillars of the global energy transition and the Dual Carbon strategic goals. However, existing methods often fail to fully capture the spatial heterogeneity and interdependencies among individual turbines, limiting their effectiveness for sustainable grid operation. To address this gap, this paper proposes an ultra-short-term wind power forecasting framework that incorporates explicit multi-dimensional spatial features. At the feature level, a 12-dimensional spatial feature system is constructed to quantify the microscale topology of wind farms. These static spatial attributes are seamlessly fused with dynamic temporal data using a dimensionality-balance factor strategy. Finally, a hybrid deep learning network comprising a multi-scale CNN, a multi-layer BiLSTM, and a multi-head self-attention mechanism is developed to capture complex spatiotemporal patterns. Experimental results on three real-world datasets show that the proposed method significantly outperforms baseline models, reducing the Mean Absolute Percentage Error by up to 11.09% and improving the coefficient of determination R2 up to 0.9120. By improving forecast accuracy and robustness, the method directly supports more reliable grid dispatching, reduces curtailment of wind energy, and thus contributes to the sustainable utilization of renewable resources. These findings demonstrate that incorporating explicit spatial correlation effectively enhances the accuracy and robustness of ultra-short-term wind power forecasting, providing robust decision support for power grid dispatching and advancing the sustainability of modern power systems.
Yanxia Wang, Weilong Yu, Ming-Han Ma et al.· Sustainability· 0 citations
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