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Real-time coordination of distributed energy resources is increasingly complex because distribution grids now operate with high variability, bidirectional power flows, and rapidly changing local constraints. Digital twins, graph neural networks, state estimation, and uncertainty-aware dispatch provide complementary capabilities for managing this complexity. This article proposes a layered digital-twin architecture that combines state estimation, graph neural network analytics, and uncertainty-aware dispatch for real-time DER coordination. The objective is to describe an architecture that is conceptual, modular, and suitable for active distribution network operation. The architecture is designed conceptually, with layers for physical grid mirroring, measurement fusion, topology-aware learning, and robust dispatch. A validation strategy is positioned as a future pathway rather than as an experimental contribution. The proposed architecture modularizes the DER coordination task into interoperable layers with distinct responsibilities and interfaces. Three tables are used to define the architecture layers, applicable GNN functions, and uncertainty-handling mechanisms. The architecture provides a blueprint for next-generation distribution management systems that require real-time observability, learning-based analytics, and robust decision support. It is intended to guide future implementation, simulation, and field validation.

This paper introduces and assesses two innovative ordinal classification approaches for forecasting wind speed across three horizons: 1h, 4h, and 8h. To tackle the issue, wind speed values are categorized into four discrete classes, which are vital for effective wind farm operations. Each class conveys key information about wind farm output, ranging from very low wind speeds to extreme wind speeds and their associated generation scenarios, thereby supporting operational decisions by wind farm managers. Ordinal classifiers prove more appropriate than nominal ones for this task. The central goal of the study is to evaluate recently developed ordinal classifiers for wind speed forecasting, with particular emphasis on extreme wind conditions that frequently lead to turbine shutdowns. The analysis relies on hourly wind speed records from a wind farm in Spain, along with predictor variables derived from the European Centre for Medium-Range Weather Forecasts Reanalysis v5 (ERA5). The proposed techniques consist of an Artificial Neural Network (ANN) employing the Cumulative Link Model as its ordinal output mechanism (MLP-CLMO), which prioritizes overall effectiveness, and an ANN refined through a soft labeling strategy grounded in triangular distributions (MLP-TO), which performs particularly well for extreme class predictions. The findings show that both methods outperform other nominal and ordinal techniques across evaluation metrics that properly address the data’s imbalance and ordinal characteristics. MLP-CLMO outperforms MLP-TO across general and ordinal performance, whereas MLP-TO shows stronger performance in predicting the extreme class.

Energy efficiency in industrial systems remains a major challenge, as conventional data-driven models are often constrained by limited precision and insufficient data. Integrating the physical laws that govern energy systems can enhance performance and ensure greater physical plausibility; however, such models often struggle with loss computation and often overlook dynamic interactions among subsystems, leading to excessive simplification and reduced real-world utility. Accordingly, this study explores a theoretical framework for constructing a conservation-law-guided neural network to improve energy-efficiency forecasting in industrial processes. The framework embeds physical principles directly into floating nodes built upon a long short-term memory (LSTM) architecture, enabling the model to capture relationships among process variables more effectively. At the same time, gradient aggregation enhances both fluidity and interpretability. Performance was assessed through two large-scale industrial case studies—vinyl chloride monomer production and detergent powder manufacturing—where the proposed model achieved notable gains in prediction accuracy and overall reliability, delivering test prediction improvements of 12.2% and 5.87% compared to previously published approaches. Compared with methods that rely on network architecture modifications, the proposed model demonstrated superior reliability and reproducibility in energy-efficiency forecasts. Furthermore, the model effectively identified energy inefficiencies, resulting in a 4.21% decrease in energy use and a corresponding reduction of 377.35 tons in carbon emissions.

With the rise of the Smart Home sector and the growing adoption of Machine Learning (ML), the acquisition of power consumption data is becoming increasingly significant. Gathering real-world energy measurements using sensors is often time-intensive, costly, error-prone, and, in some cases, entirely infeasible. To address this, we introduce the VA-Creator, a framework designed for generating Virtual Appliances (VAs). These VAs produce synthetic power consumption patterns (PCPs) using Neural Networks (NNs) that dynamically adjust their architecture according to the structure of the training data, thereby facilitating the development of additional VAs. To cover every appliance category commonly found in a typical household, we employ a range of neural network types—including Multilayer Perceptrons (MLPs), Long Short-Term Memory networks (LSTMs), and a tailored Generative Adversarial Network (GAN)—alongside other ML approaches such as XGBoost, choosing the most suitable method based on the specific properties of each appliance. The outputs of these ML models are subsequently compared with actual measurements and evaluated using Dynamic Time Warping (DTW) and MLP-based discriminator classification accuracy as evaluation metrics. Furthermore, to verify that the VAs enable effective training of ML models, we generate synthetic datasets with them and conduct an in-depth evaluation by training Non-Intrusive Load Monitoring (NILM) models. The results of this comprehensive assessment confirm that the VA models yield realistic and practically useful outputs.

Photovoltaic systems contribute substantially to electrical grid capacity, yet the intermittent nature of solar resources and uncertainties in output make grid balancing and reserve allocation challenging. Accurately forecasting solar production begins with reliably estimating clear-sky irradiance levels. While broadband atmospheric turbidity parameters can capture reductions in clear-sky conditions, the precision of these models hinges on data capturing current and future aerosol concentrations and water vapor levels. Obtaining such information requires observations that are both temporally and spatially proximate, given the high variability in turbidity. Unfortunately, these kinds of measurements are available in near real time only at a small—and shrinking—set of locations. This study introduces an innovative approach to infer time-dependent, site-specific turbidity levels using more widely accessible data from pyranometers or monitored PV system outputs. The technique leverages a long short-term memory (LSTM) recurrent neural network to isolate the turbidity-related component embedded within global irradiance (or PV power-derived) records, overcoming the natural suppression of this signal. Designed for near real-time implementation, the method supports solar forecasting needs. Evaluation focuses on two aspects: (1) its capacity to replicate turbidity values calculated historically from beam irradiance observations during clear-sky periods; and (2) its effectiveness when supplying turbidity estimates (via persistence forecasts based on daily averages) as inputs to clear-sky models.