Proposed EnerInfer framework manages energy efficiency, throughput, and thermal comfort for on-device LLM inference, improving energy efficiency up to 65% without QoE violation.

The paper models how AI-driven data center demand stresses the electrical grid, finding that relying solely on renewable energy certificates (RECs) is insufficient and that on-site storage and spatial…

The paper introduces an integrated platform combining BIM, sensor data, and advanced algorithms to significantly optimize energy consumption in green building design, achieving a 29.3% reduction in en…

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…

The paper presents an IoT-enabled smart home system using Raspberry Pi 5 and environmental sensors to automatically manage devices, achieving over 46% energy savings compared to always-on models.

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

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…

The paper proposes Multi-Adapter Representation Interventions via Energy Calibration (MARI), a method that adaptively adjusts the strength and direction of interventions across different inputs to imp…

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 introduces PIRS, a physics-informed reward shaping method that replaces ad-hoc comfort proxies with the ISO 7730 PMV formulation, enabling deep reinforcement learning agents to achieve energ…

EnergyMamba proposes an uncertainty-aware, graph-enhanced selective state space model to significantly improve both the accuracy and reliability of energy consumption prediction by explicitly modeling…

The paper introduces a Cognitive Taxonomy and a rigorous evaluation protocol to provide an objective, multi-faceted framework for measuring system capabilities and tracking progress toward Artificial…

TIMEGATE introduces a resource-aware policy layer that manages continual ML adaptation by dynamically budgeting time and evaluation resources, achieving significant compute and energy savings without…

This paper proposes a behaviour-conditioned Attentive Neural Process framework for residential short-term load forecasting, using behavioural structure as conditioning signals.

OccuReward introduces an LLM-guided framework and a Comfort Equity Index (CEI) to shape building energy rewards, demonstrating that iterative refinement significantly improves occupant comfort equity…

The paper addresses the reliability of open-weight LLMs for power system code generation by identifying structured API-knowledge boundary errors and proposing a boundary-aware intervention that signif…