A Review of Deep Reinforcement Learning for Smart Building Energy Management (2008.05074v3)

Abstract: Global buildings account for about 30% of the total energy consumption and carbon emission, raising severe energy and environmental concerns. Therefore, it is significant and urgent to develop novel smart building energy management (SBEM) technologies for the advance of energy-efficient and green buildings. However, it is a nontrivial task due to the following challenges. Firstly, it is generally difficult to develop an explicit building thermal dynamics model that is both accurate and efficient enough for building control. Secondly, there are many uncertain system parameters (e.g., renewable generation output, outdoor temperature, and the number of occupants). Thirdly, there are many spatially and temporally coupled operational constraints. Fourthly, building energy optimization problems can not be solved in real-time by traditional methods when they have extremely large solution spaces. Fifthly, traditional building energy management methods have respective applicable premises, which means that they have low versatility when confronted with varying building environments. With the rapid development of Internet of Things technology and computation capability, artificial intelligence technology find its significant competence in control and optimization. As a general artificial intelligence technology, deep reinforcement learning (DRL) is promising to address the above challenges. Notably, the recent years have seen the surge of DRL for SBEM. However, there lacks a systematic overview of different DRL methods for SBEM. To fill the gap, this paper provides a comprehensive review of DRL for SBEM from the perspective of system scale. In particular, we identify the existing unresolved issues and point out possible future research directions.

Deep Reinforcement Learning for Smart Building Energy Management: A Comprehensive Review

The paper, "A Review of Deep Reinforcement Learning for Smart Building Energy Management," identifies the pressing need to enhance energy efficiency and sustainability in buildings, given their substantial role in global energy consumption and carbon emissions. The authors present a comprehensive review of deep reinforcement learning (DRL) applications in smart building energy management (SBEM), emphasizing the challenges and promising avenues for future research.

Challenges in SBEM

In optimizing energy use in smart buildings, the following challenges are notable:

1. Complex Building Dynamics: Building thermal dynamics are complex and often intractable for creating accurate and efficient control models.
2. System Uncertainties: Parameters like renewable generation output, electricity price, and occupancy levels introduce significant uncertainty.
3. Operational Constraints: Spatially and temporally coupled constraints, especially in multi-energy subsystems, complicate optimization efforts.
4. Real-time Optimization: Traditional optimization techniques struggle with real-time execution due to expansive solution spaces.
5. Versatility: Existing methods often lack adaptability across diverse building environments.

Emergence of DRL in SBEM

Deep reinforcement learning, an artificial intelligence approach leveraging deep neural networks, offers effective solutions to these challenges:

• DRL eliminates the need for explicit building models by learning optimal control policies through interaction with the environment.
• It can address uncertainties by learning from real-time data, bypassing forecasting processes.
• Multi-agent DRL frameworks enable coordination across energy systems, satisfying both spatial and temporal operational constraints.
• DRL's computational efficiency in testing phases allows for real-time decision-making even with high-dimensional data.
• By employing simulated or real data for training, DRL methods can adapt to varying building environments, increasing their applicability.

Methodological Overview

The paper reviews various DRL methods, categorizing them into model-free and model-based approaches:

• Model-free DRL: Algorithms like DQN, DDPG, PPO, and A3C/A2C fall under this category, each with unique advantages such as supporting discrete/continuous action spaces and high data efficiency. These methods have been effectively applied in HVAC control and microgrid energy management.
• Model-based DRL: These involve constructing environment models to enhance sample efficiency. Notable algorithms include MuZero, LSTM-DDPG, and differentiable MPC-PPO, which have applications in microgrid management and HVAC control.

Application Landscapes

The review covers DRL applications across different scales:

1. Single Building Energy Subsystems: Predominantly HVAC systems, where DRL has shown considerable potential in reducing energy costs and consumption while enhancing comfort levels.
2. Multiple Energy Subsystems in Buildings: Coordination among various systems like HVACs, lighting, and blinds using DRL can lead to significant energy savings, reflecting the versatility of DRL in complex building environments.
3. Building Microgrids: Effective management of building-integrated microgrids, involving renewable energy management and peer-to-peer energy trading, benefits considerably from DRL's capability to handle uncertainties and optimize multiple objectives.

Future Prospects and Directions

This paper underscores the potential of DRL in revolutionizing SBEM, albeit with several unsolved issues that need attention:

• Data Efficiency: Enhanced learning methodologies to reduce exploration time and cost.
• Multi-objective and Multi-timescale Optimization: Addressing conflicting energy management objectives with scalable solutions.
• Integration with Transfer Learning: Building models from sparse data, leveraging knowledge transfer across different building environments.

Conclusion

The paper concludes with crucial insights into the current state and future avenues of DRL in SBEM. By mitigating SBEM's inherent complexities through advanced AI technologies, DRL stands poised to drive significant advancements in energy efficiency and operational versatility in smart buildings. Researchers are encouraged to explore these potential areas to further the adoption and refinement of DRL techniques in SBEM.