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 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 presents LightFARM, a predictive lighting control framework for energy-efficient indoor farming that combines finite-horizon predictive control with compact models of photosynthesis, therma…

This paper tests different INT8 quantized Multi-Layer Perceptron models for malware detection on Android devices, achieving high accuracy with reduced energy consumption.

This paper investigates the energy efficiency of Large Language Model (LLM) serving using emerging memory technologies, specifically monolithic 3D (M3D) integration, and presents simulation results sh…

This paper introduces energy-aware learning, an approach that reduces actuator energy in closed-loop deep brain stimulation systems by incorporating actuator energy into the reinforcement learning rew…

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.

The paper proposes EnThM, a lightweight, hierarchical verification scheme that uses statistical and rule-based checks on aggregated metering data to mitigate real-time power theft in smart grids.

This paper introduces Ichnos+, a system to estimate the environmental footprint of Nextflow scientific workflows using post-hoc analysis and node-specific power models.

This paper studies the autarky problem of scheduling energy-consuming jobs with time windows using a battery and an energy forecast, and shows NP-hardness, polynomial-time solvability, and fixed-param…

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…

This paper proposes a thermodynamic computing stack for machine learning using stochastic analog processes and energy-based models.

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…

This paper investigates the latency performance of Mobile Edge Computing (MEC) on a 5G cellular network for real-time power transmission line analytics, demonstrating a low latency of 44.62 ms compare…

This paper evaluates and compares OvA and OvR classification strategies for AI-based waste sorting solutions in German municipalities, focusing on the city of Goslar.

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…

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…