Optimal Scheduling in IoT-Driven Smart Isolated Microgrids Based on Deep Reinforcement Learning

no code implementations • 28 Apr 2023 • Jiaju Qi, Lei Lei, Kan Zheng, Simon X. Yang, Xuemin, Shen

In this paper, we investigate the scheduling issue of diesel generators (DGs) in an Internet of Things (IoT)-Driven isolated microgrid (MG) by deep reinforcement learning (DRL).

Scheduling

Joint Energy Dispatch and Unit Commitment in Microgrids Based on Deep Reinforcement Learning

no code implementations • 3 Jun 2022 • Jiaju Qi, Lei Lei, Kan Zheng, Simon X. Yang

Nowadays, the application of microgrids (MG) with renewable energy is becoming more and more extensive, which creates a strong need for dynamic energy management.

energy management Management +2

Federated Reinforcement Learning: Techniques, Applications, and Open Challenges

no code implementations • 26 Aug 2021 • Jiaju Qi, Qihao Zhou, Lei Lei, Kan Zheng

This paper presents a comprehensive survey of Federated Reinforcement Learning (FRL), an emerging and promising field in Reinforcement Learning (RL).

Edge-computing Federated Learning +2

Patent Analytics Based on Feature Vector Space Model: A Case of IoT

no code implementations • 17 Apr 2019 • Lei Lei, Jiaju Qi, Kan Zheng

In order to address the above limitations, we propose a patent analytics based on feature vector space model (FVSM), where the FVSM is constructed by mapping patent documents to feature vectors extracted by convolutional neural networks (CNN).

Clustering Information Retrieval +2