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This is a Quantitative Study study.

September 30, 2025Journal of Computing and Electronic Information ManagementOpen Access

Reinforcement Learning Approaches for Intelligent Control of Smart Building Energy Systems with Real-Time Adaptation to Occupant Behavior and Weather Conditions

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Authors

LQLin Qiu

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Overview

This research demonstrates a reinforcement learning framework optimizing energy consumption and comfort in smart buildings through real-time adaptations to occupant behavior and weather.

Key Points

The framework achieved an impressive 27.3% reduction in total energy consumption compared to traditional systems, enhancing efficiency and sustainability.
With a remarkable adaptation time of 8.3 minutes, the system maintains acceptable thermal comfort levels for 96.7% of occupied hours, enhancing occupant satisfaction.
The use of Deep Q-Networks with attention mechanisms facilitates adaptive control of HVAC systems and lighting, optimizing energy use effectively.
Empirical evaluations across 89 buildings over an 18-month period highlight the significant benefits of RL-based approaches in energy management.

Cite This Study

Lin Qiu (2025) studied this question.

synapsesocial.com/papers/68dc12cc8a7d58c25ebb0b28 https://doi.org/10.54097/hr81cg02

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September 30, 2025Journal of Computing and Electronic Information ManagementOpen Access

Reinforcement Learning Approaches for Intelligent Control of Smart Building Energy Systems with Real-Time Adaptation to Occupant Behavior and Weather Conditions

View Full Paper

Ask AI

Bookmark

Authors

LQLin Qiu

Discussion

Member takes

Overview

This research demonstrates a reinforcement learning framework optimizing energy consumption and comfort in smart buildings through real-time adaptations to occupant behavior and weather.

Key Points

The framework achieved an impressive 27.3% reduction in total energy consumption compared to traditional systems, enhancing efficiency and sustainability.
With a remarkable adaptation time of 8.3 minutes, the system maintains acceptable thermal comfort levels for 96.7% of occupied hours, enhancing occupant satisfaction.
The use of Deep Q-Networks with attention mechanisms facilitates adaptive control of HVAC systems and lighting, optimizing energy use effectively.
Empirical evaluations across 89 buildings over an 18-month period highlight the significant benefits of RL-based approaches in energy management.

Cite This Study

Lin Qiu (2025) studied this question.

synapsesocial.com/papers/68dc12cc8a7d58c25ebb0b28 https://doi.org/10.54097/hr81cg02

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