Copertina

Forecast Aware Deep Reinforcement Learning for Efficient Electricity Load Scheduling in Dairy Farms

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Ali, Nawazish, Shaw, Rachael, Mason, Karl
Natura:	Preprint
Pubblicazione:	2026
Soggetti:	Artificial Intelligence
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

A Deep Reinforcement Learning Approach to Battery Management in Dairy Farming via Proximal Policy Optimization
di: Ali, Nawazish, et al.
Pubblicazione: (2024)

A Reinforcement Learning Approach to Dairy Farm Battery Management using Q Learning
di: Ali, Nawazish, et al.
Pubblicazione: (2024)

Reinforcement Learning Enabled Peer-to-Peer Energy Trading for Dairy Farms
di: Shah, Mian Ibad Ali, et al.
Pubblicazione: (2024)

Peer-to-Peer Energy Trading in Dairy Farms using Multi-Agent Reinforcement Learning
di: Shah, Mian Ibad Ali, et al.
Pubblicazione: (2025)

A Multi-Agent Systems Approach for Peer-to-Peer Energy Trading in Dairy Farming
di: Shah, Mian Ibad Ali, et al.
Pubblicazione: (2023)

Uncertainty-Aware Knowledge Transformers for Peer-to-Peer Energy Trading with Multi-Agent Reinforcement Learning
di: Shah, Mian Ibad Ali, et al.
Pubblicazione: (2025)

Optimizing Deep Reinforcement Learning for Adaptive Robotic Arm Control
di: Shianifar, Jonaid, et al.
Pubblicazione: (2024)

Short-Term Electricity-Load Forecasting by Deep Learning: A Comprehensive Survey
di: Dong, Qi, et al.
Pubblicazione: (2024)

Inferring Preferences from Demonstrations in Multi-objective Reinforcement Learning
di: Lu, Junlin, et al.
Pubblicazione: (2024)

Demonstration Guided Multi-Objective Reinforcement Learning
di: Lu, Junlin, et al.
Pubblicazione: (2024)

A Meta-Learning Approach for Multi-Objective Reinforcement Learning in Sustainable Home Environments
di: Lu, Junlin, et al.
Pubblicazione: (2024)

D-SPEAR: Dual-Stream Prioritized Experience Adaptive Replay for Stable Reinforcement Learning in Robotic Manipulation
di: Zhang, Yu, et al.
Pubblicazione: (2026)

QoS-Aware Scheduling in New Radio Using Deep Reinforcement Learning
di: Stigenberg, Jakob, et al.
Pubblicazione: (2021)

DiffLoad: Uncertainty Quantification in Electrical Load Forecasting with the Diffusion Model
di: Wang, Zhixian, et al.
Pubblicazione: (2023)

Automated Deep Learning for Load Forecasting
di: Keisler, Julie, et al.
Pubblicazione: (2024)

Hindsight Preference Replay Improves Preference-Conditioned Multi-Objective Reinforcement Learning
di: Shianifar, Jonaid, et al.
Pubblicazione: (2026)

Load-Aware Training Scheduling for Model Circulation-based Decentralized Federated Learning
di: Kainuma, Haruki, et al.
Pubblicazione: (2025)

Agent-Based Modeling of Low-Emission Fertilizer Adoption for Dairy Farm Decarbonisation using Empirical Farm Data
di: Jayakumar, Surya, et al.
Pubblicazione: (2026)

ProActor: Timing-Aware Reinforcement Learning for Proactive Task Scheduling Agents
di: Ding, Lei, et al.
Pubblicazione: (2026)

Policy-Based Deep Reinforcement Learning Hyperheuristics for Job-Shop Scheduling Problems
di: Lassoued, Sofiene, et al.
Pubblicazione: (2026)

On the Role of DAG topology in Energy-Aware Cloud Scheduling : A GNN-Based Deep Reinforcement Learning Approach
di: Hattay, Anas, et al.
Pubblicazione: (2026)

Dependency-Aware CAV Task Scheduling via Diffusion-Based Reinforcement Learning
di: Cheng, Xiang, et al.
Pubblicazione: (2024)

From ARIMA to Attention: Power Load Forecasting Using Temporal Deep Learning
di: Veluru, Suhasnadh Reddy, et al.
Pubblicazione: (2026)

Deep Reinforcement Learning Guided Improvement Heuristic for Job Shop Scheduling
di: Zhang, Cong, et al.
Pubblicazione: (2022)

Cost-Aware Dynamic Cloud Workflow Scheduling using Self-Attention and Evolutionary Reinforcement Learning
di: Shen, Ya, et al.
Pubblicazione: (2024)

Sample-Efficient Neurosymbolic Deep Reinforcement Learning
di: Veronese, Celeste, et al.
Pubblicazione: (2026)

Tracking Drift: Variation-Aware Entropy Scheduling for Non-Stationary Reinforcement Learning
di: Wang, Tongxi, et al.
Pubblicazione: (2026)

Topology-Aware and Highly Generalizable Deep Reinforcement Learning for Efficient Retrieval in Multi-Deep Storage Systems
di: Li, Funing, et al.
Pubblicazione: (2025)

Forecasting Anonymized Electricity Load Profiles
di: Fernandez, Joaquin Delgado, et al.
Pubblicazione: (2025)

Deep Reinforcement Learning for Flexible Job Shop Scheduling with Random Job Arrivals
di: Tang, Yu, et al.
Pubblicazione: (2026)

schlably: A Python Framework for Deep Reinforcement Learning Based Scheduling Experiments
di: de Puiseau, Constantin Waubert, et al.
Pubblicazione: (2023)

Interpretable Modeling of Deep Reinforcement Learning Driven Scheduling
di: Li, Boyang, et al.
Pubblicazione: (2024)

Load and Renewable Energy Forecasting Using Deep Learning for Grid Stability
di: Sarkar, Kamal
Pubblicazione: (2025)

Planner-Centric Reinforcement Learning for Deep Research with Structure-Aware Reward
di: Hussain, Mustafa Anis, et al.
Pubblicazione: (2026)

HGT-Scheduler: Deep Reinforcement Learning for the Job Shop Scheduling Problem via Heterogeneous Graph Transformers
di: Soykan, Bulent
Pubblicazione: (2026)

Graph-Enhanced Deep Reinforcement Learning for Multi-Objective Unrelated Parallel Machine Scheduling
di: Soykan, Bulent, et al.
Pubblicazione: (2026)

Scheduling Drone and Mobile Charger via Hybrid-Action Deep Reinforcement Learning
di: Dou, Jizhe, et al.
Pubblicazione: (2024)

Scheduling Your LLM Reinforcement Learning with Reasoning Trees
di: Wang, Hong, et al.
Pubblicazione: (2025)

Deep Reinforcement Learning for Wireless Scheduling in Distributed Networked Control
di: Pang, Gaoyang, et al.
Pubblicazione: (2021)

Scheduled Curiosity-Deep Dyna-Q: Efficient Exploration for Dialog Policy Learning
di: Niu, Xuecheng, et al.
Pubblicazione: (2024)