Mengmeng Yu

Electrical and Electronic Engineering · 2016–2019

A researcher in the Science corpus: 2 papers; trusts 8 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Electrical and Electronic Engineering — 18 citations into it; #11,691 of 16,539 by trust here, #13,165 by citations
• Control and Systems Engineering — 8 citations into it; #6,563 of 8,257 by trust here, #7,501 by citations
• Renewable Energy, Sustainability and the Environment — 7 citations into it
• Reinforcement Learning — 5 citations into it

Whom Mengmeng Yu trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Sabita Maharjan
2. Seung Ho Hong
3. Renzhi Lu
4. S. Borenstein
5. J. Gao
6. Quanyan Zhu
7. Martin J. Osborne
8. Christopher J. C. H. Watkins
9. Antonio J. Conejo
10. Peng Yang

Who trusts Mengmeng Yu overall

1. Renzhi Lu
2. Seung Ho Hong

Papers from Mengmeng Yu's perspective

Ordered by how strongly the researchers Mengmeng Yu trusts endorse each paper. Same corpus, a different order for every researcher.

1. Real-Time Demand Response Model (IEEE Transactions on Smart Grid)
2. Human-level control through deep reinforcement learning
3. Three-Party Energy Management With Distributed Energy Resources in Smart Grid (IEEE transactions on industrial electronics (1982. Print))
4. Electricity price forecasting: A review of the state-of-the-art with a look into the future
5. Dependable Demand Response Management in the Smart Grid: A Stackelberg Game Approach (IEEE Transactions on Smart Grid)
6. A Comprehensive Survey of Multiagent Reinforcement Learning
7. Recent advances in electricity price forecasting: A review of probabilistic forecasting
8. Autonomous Demand-Side Management Based on Game-Theoretic Energy Consumption Scheduling for the Future Smart Grid (IEEE Transactions on Smart Grid)
9. Day-ahead electricity price forecasting via the application of artificial neural network based models
10. The Long-Run Efficiency of Real-Time Electricity Pricing

Their papers

• Demand Response for Home Energy Management Using Reinforcement Learning and Artificial Neural Network (2019) — 334 citations
• A Real-Time Demand-Response Algorithm for Smart Grids: A Stackelberg Game Approach (2016) — 358 citations