4.7 Article

A new framework integrating reinforcement learning, a rule-based expert system, and decision tree analysis to improve building energy flexibility

Related references

Note: Only part of the references are listed.
Article Energy & Fuels

A data-driven strategy using long short term memory models and reinforcement learning to predict building electricity consumption

Xinlei Zhou et al.

Summary: This paper presents a new strategy that integrates LSTM models and RL agents to forecast building electricity consumption. Results show that the proposed strategy can significantly improve prediction accuracy for buildings with large monthly variations in electricity consumption, while showing less improvement for those with insignificant variations.

APPLIED ENERGY (2022)

Article Computer Science, Interdisciplinary Applications

A reinforcement learning method for human-robot collaboration in assembly tasks

Rong Zhang et al.

Summary: The assembly process of high precision products requires human-robot collaboration to optimize efficiency, but the unpredictability of human behavior poses a challenge. A human-robot collaborative reinforcement learning algorithm has been proposed and validated through experimental analysis to optimize task allocation in assembly processes.

ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING (2022)

Article Computer Science, Information Systems

Building Energy Management With Reinforcement Learning and Model Predictive Control: A Survey

Huiliang Zhang et al.

Summary: Building energy management is crucial for improving system efficiency and reducing greenhouse gas emissions. The challenges and uncertainties in the field have increased with the rise of renewable energy and diverse electrical appliances. While classical model predictive control has been effective, data-driven solutions like data-driven MPC and reinforcement learning-based methods have gained research interest. However, the integration of these methods and the selection of suitable control algorithms require further discussion.

IEEE ACCESS (2022)

Article Energy & Fuels

Intelligent multi-zone residential HVAC control strategy based on deep reinforcement learning

Yan Du et al.

Summary: In this study, a novel deep reinforcement learning method, DDPG, is applied to optimize the HVAC control in residential buildings with the goal of minimizing energy consumption cost while maintaining user comfort. The simulation results demonstrate that the DDPG-based control strategy outperforms the state-of-the-art methods in reducing energy consumption cost and improving comfort level. Additionally, experiments with different building and price models show that the well-trained DDPG-based strategy has high generalization and adaptability for real-world applications.

APPLIED ENERGY (2021)

Article Green & Sustainable Science & Technology

Applications of reinforcement learning in energy systems

A. T. D. Perera et al.

Summary: Energy systems are transitioning to accommodate renewable energy technologies and face challenges in controlling energy flows. Research has shown potential for reinforcement learning in improving performance, but certain complexities remain unresolved, indicating room for further development.

RENEWABLE & SUSTAINABLE ENERGY REVIEWS (2021)

Article Computer Science, Artificial Intelligence

Self-play reinforcement learning with comprehensive critic in computer games

Shanqi Liu et al.

Summary: The study introduces a self-play actor-critic method for training agents in computer games, incorporating a comprehensive critic into the policy gradient method. Results demonstrate that the agent trained with the SPAC method outperforms other algorithms in various evaluation approaches, showcasing the effectiveness of the comprehensive critic in the self-play training process.

NEUROCOMPUTING (2021)

Article Energy & Fuels

Hybrid energy management for islanded networked microgrids considering battery energy storage and wasted energy

Ali Jani et al.

Summary: A two-level energy management strategy is proposed to optimize the operation costs of islanded networked microgrids by defining adjustable power concept and performing global optimization for optimal energy trading and cost minimization.

JOURNAL OF ENERGY STORAGE (2021)

Article Computer Science, Artificial Intelligence

Reinforcement learning for whole-building HVAC control and demand response

Donald Azuatalam et al.

ENERGY AND AI (2020)

Article Construction & Building Technology

Potential of energy flexible buildings: Evaluation of DSM strategies using building thermal mass

Jose Sanchez Ramos et al.

ENERGY AND BUILDINGS (2019)

Article Construction & Building Technology

A novel ensemble learning approach to support building energy use prediction

Zeyu Wang et al.

ENERGY AND BUILDINGS (2018)

Article Construction & Building Technology

IEA EBC Annex 67 Energy Flexible Buildings

Soren Ostergaard Jensen et al.

ENERGY AND BUILDINGS (2017)

Article Construction & Building Technology

An intelligent system architecture in home energy management systems (HEMS) for efficient demand response in smart grid

Mohammad Shakeri et al.

ENERGY AND BUILDINGS (2017)

Review Energy & Fuels

Photovoltaic self-consumption in buildings: A review

Rasmus Luthander et al.

APPLIED ENERGY (2015)

Article Chemistry, Physical

Optimal charge control strategies for stationary photovoltaic battery systems

Jiahao Li et al.

JOURNAL OF POWER SOURCES (2014)

Article Construction & Building Technology

Optimal temperature control of intermittently heated buildings using Model Predictive Control: Part II - Control algorithm

Ion Hazyuk et al.

BUILDING AND ENVIRONMENT (2012)

Article Construction & Building Technology

Model predictive control of a building heating system: The first experience

Samuel Privara et al.

ENERGY AND BUILDINGS (2011)