This paper proposes Cluster-based Sequential Feature Selection (CSFS), a novel method for automatic and efficient feature selection in renewable energy prediction pipelines.

The paper proposes a multi-task scientific machine learning framework that jointly predicts key engine health indicators (TGTU, DTGT) and the Remaining Useful Life (RUL) while quantifying prediction u…

The paper introduces SPARROW, an autonomous, open-source platform that uses solar power, edge AI, and satellite communication to enable continuous, scalable biodiversity monitoring in remote global ec…

This paper proposes an Explainable Deep Reinforcement Learning (XRL) framework to optimize energy management in complex buildings, demonstrating that on-policy algorithms provide superior cost reducti…

This paper develops FGDSE, a feature-governed dynamic stacking ensemble for climate-resilient charging-asset management in electric vehicles, which predicts daily fault risk over a multi-week horizon…

This paper benchmarks five distinct uncertainty quantification methods—including Delta, Bayesian Dropout, and Bootstrap—to determine the optimal approach for predicting turbine gas temperature degrada…

The paper introduces an LLM-driven framework to automatically standardize, structure, and enrich unstructured free-text wind turbine maintenance logs, transforming qualitative field observations into…

This paper investigates the benefits of generating multiple solutions in each generation for Evolutionary Diversity Optimisation (EDO) and proposes efficient methods to achieve it.

A new method for selecting knots in Generalized Additive Models using an extension of adaptive splines and a customized Fellner-Schall scheme.

This paper compares the effect of different initialization methods on the accuracy and complexity of solutions in genetic programming for symbolic regression, finding no significant differences.

This paper proposes a new training objective for sample-based generative models that considers decision maker's cost structure.

The paper proposes an uncertainty-aware transfer learning framework using the Temporal Fusion Transformer (TFT) to achieve robust and scalable energy forecasting across different buildings, demonstrat…

This paper proposes a Model-Based Systems Engineering workflow for creating Digital Twins of Renewable Energy Communities using SysML and the SAREF4ENER ontology.

This paper provides the first non-vacuous generalization analysis for the Stochastic Variance Reduced Gradient (SVRG) method by establishing sharp, data-dependent algorithmic stability bounds, thereby…

This paper analyzes the performance and explainability requirements of evolutionary algorithms when applied to complex, real-world physics-informed optimization problems, identifying a gap between cur…

This paper introduces EVOTS, an evolutionary neural architecture search framework for discovering task-adaptive Transformer-like models for multivariate time-series forecasting.