Residential Demand Response Using Reinforcement Learning

By Daniel C. O'Neill, Marco Levorato, Andrea J. Goldsmith, U. Mitra. 2010 First IEEE International Conference on Smart Grid Communications, 2010

301 citations recorded by the source corpus.

Subfields: Building and Construction, Electrical and Electronic Engineering, Renewable Energy, Sustainability and the Environment

View Paper: https://www.semanticscholar.org/paper/ed0eb0ea2e3d8a5b266e213ee8f2677c1f5c05ea

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Building and Construction

1. Zhe Wang
2. Yan Du
3. Jianhui Wang
4. Fangxing Li
5. Michael E. Webber
6. Tianzhen Hong
7. Wei Yu
8. Milos Manic

In Electrical and Electronic Engineering

1. Jianhui Wang
2. Tùng T. Kim
3. Fuad Un-Noor
4. David Flynn
5. Benoit Couraud
6. S. Elizondo-González
7. Sonam Norbu
8. Valentin Robu

In Renewable Energy, Sustainability and the Environment

1. Tùng T. Kim
2. Zhe Wang
3. Yan Du
4. Jianhui Wang
5. H. Vincent Poor
6. Fangxing Li
7. Tianzhen Hong

Citations in the corpus

• Artificial intelligence and machine learning approaches to energy demand-side response: A systematic review (2020, methodology): “One way is to represent the user preferences over home appliance use with a utility function, which can be pre-specified [81,112] or learned”
• On-Line Building Energy Optimization Using Deep Reinforcement Learning (2017, background): “solve stochastic optimal control problems [10] in the power system area as well as an energy consumption scheduling problem [11] with dynamic pricing [12].”
• Application of Big Data and Machine Learning in Smart Grid, and Associated Security Concerns: A Review (2019, background): “At the service provider domain, smart appliances provide information for energy price estimation [23], [103].”
• Scheduling Power Consumption With Price Uncertainty (2011, background): “Dynamic learning of these parameters may also be a viable option [ 18 ].”
• Intelligent Multi-Microgrid Energy Management Based on Deep Neural Network and Model-Free Reinforcement Learning (2020, methodology): “The utilization of RL to optimize the residential demand response schedule is first discussed in [18].”
• MPC-Based Appliance Scheduling for Residential Building Energy Management Controller (2013, methodology): “The statistical ON/OFF operations of appliances are modeled as Markov chains in [8].”
• Reinforcement learning for building controls: The opportunities and challenges (2020, methodology): “(2010) [18] IEEE Conference on Smart Grid Communications”