Jul 2026· 2026 International Conference on Emerging Trends in Information, Communication & Systems (ICETICS)· pp. 1-6· 0 citations· 20 references
Abstract
Sound prediction of existence of future generation based on stochastic sources is a critical issue because of high nonlinearities, high transitions in the environment, and the nature of inherent uncertainty in observational information. The paper proposes an integrated architecture of deep learning, which is the Hierarchical Regime-Adaptive Probabilistic Network (HRAPN) that aims to overcome these constraints using an end-to-end learning framework. The solution selection boasts of hierarchical representation induction with a latent regime adaptation mechanism that modulates dynamically the internal model behavior in a non-stationary environment. Besides that, attention guided dependency synthesis module performs informative temporal context aggregation selectively to allow an efficient long horizon modeling without impaired performance. In contrast to the more traditional deterministic approaches, HRAPN uses probabilistic model of output in order to explicitly model predictive uncertainty, which enhances robustness and reliability of the estimated decisions. The system takes directly heterogeneous and multivariate data, without explicit features or domain pre-treatment. It is experimentally assessed that the presented method achieves higher results as compared to existing baselines in accuracy, stability, and uncertainty calibration in various forecast periods. The findings validate the performance of regime perceiving, hierarchical abstraction and probabilistic inference in the same learning process. The proposed HRAPN model offers a scaffoldable and adaptable evaluation of the dynamic generation modeling under both variable operating conditions with data. The suggested framework attains an overall accuracy of 96.6%, illustrating its robust prediction reliability and exceptional performance relative to current methodologies.
A Meta-Optimized Deep Learning Fusion with Nonlinear Stacking (MODLF-NS) for forecasting RE usage, which combines Elman Recurrent Neural Networks (ERNN) with Particle Swarm Optimization (PSO) and Harris Hawks Optimization (HHO) to enhance model performance.
A. A. Bafti, M. Rezaei· Journal of Environmental Inf...· 0 citations
Reliable short-term PV forecasting is essential to ensure the reliability of energy management based on renewable sources, but the lack of interpretability of deep learning models and the environmental uncertainty still prevent their operation. In this research, an uncertainty-aware transformer framework is proposed which is explainable when forecasting the short-term PV power under dynamically changing environmental conditions. It introduces a framework which combines a Temporal Fusion Transformer (TFT), quantile-based uncertainty estimation, dual explainability mechanism based on attention analysis, and SHAP based feature attribution. The measurements from a real solar system having capacity of 1.005 kW were taken at the site of Sitapura, Jaipur, India and were synchronously matched with the POWER meteorological measurements taken by NASA. The framework was tested on the baseline models of ARIMA, LSTM and CNN-LSTM. These experimental results showed that the approach achieved better forecasting results compared to all the baseline approaches with RMSE of 0.067, MAE of 0.051 and MAPE of 4.38%. The probabilistic forecasting module was able to attain a Prediction Interval Coverage Probability (PICP) of 94.8% and narrow interval width, thus demonstrating good uncertainty estimation abilities. Irradiance and PV output of the past were found to be the most important factors for forecasting in this explainability analysis. The proposed framework is based on the transformer model, probabilistic forecasting, and then explains the renewable energy forecasting system in the entire system for photovoltaic uncertainty-aware and interpretable prediction.
Udit Mamodiya, I. Kishor, P. Mudholkar et al.· International Journal of Dat...· 0 citations
The high penetration of renewable energy and the large-scale integration of electric vehicles have significantly enhanced the nonlinearity and non-stationarity of the smart grid load sequence. Traditional time series models struggle to meet the prediction requirements in complex scenarios. This paper focuses on three kinds of deep learning load forecasting methods: recurrent neural network variants, CNN hybrid architectures, and Transformers, and systematically conducts a comparative analysis of them from three dimensions: prediction accuracy, long-term dependency modeling ability and computational efficiency. The results show that the three types of architectures each have distinct advantages, and there is no universal optimal solution: the recurrent variant balances accuracy and efficiency in stationary short-term prediction, the CNN hybrid architecture is more robust in strong coupling scenarios such as extreme weather, and the Transformers' trend modeling ability is outstanding but the computational cost for long sequences is large. The pros and cons of each method are highly dependent on the stationarity of the specific scenario, the forecasting horizon, and resource constraints. The model selection must be based on the task characteristics rather than the pursuit of general solutions.
Qiuhui Jiang· Applied and Computational En...· 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
This study compares two multi-step forecasting strategies for 24-h horizons: a direct 24→24 strategy and a recursive strategy based on sequential 24→1 predictions, and concludes that the selection of a forecasting strategy depends on the required balance between overall accuracy, temporal stability, and computational cost.
Erik Fernando Mendez-Garces, David Buldain, M. Comech· Energies· 0 citations
With the high penetration of distributed photovoltaic (PV) generation and wind power in active distribution networks, dayahead scheduling has become increasingly dependent on accurate source-load forecasting. Traditional mechanism-based models offer a certain degree of physical interpretability, yet they are often inadequate for capturing random fluctuations. In contrast, purely data-driven models possess strong nonlinear fitting capability, but may lack physical plausibility and engineering reliability. To address this issue, this paper proposes a physics-guided residual learning method for day-ahead scheduling in active distribution networks. Specifically, baseline forecasting models for PV generation, wind power, and load are first constructed based on irradiance, wind speed, and historical load patterns to provide trend priors. Then, a BP neural network is employed to learn the residual errors of the mechanism-based models and refine the baseline forecasts. Finally, the forecasting results are embedded into a day-ahead scheduling model with energy storage systems and demand response, and the proposed method is comprehensively evaluated from both forecasting and scheduling perspectives. Experimental results under three simulated test scenarios show that the proposed method achieves relatively low forecasting error for PV prediction and yields the lowest scheduling cost in all scenarios. Compared with the conventional mechanism-based method and the pure BP method, the average operating cost is reduced by approximately 15.50% and 4.70%, respectively. These results indicate that combining physical priors with residual learning can provide more effective forecasting inputs for source-load-storage coordinated scheduling in active distribution networks.
Chang-Wei Cao· European Conference on Elect...· 0 citations
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