掲載種別：研究論文（学術雑誌）   出版者・発行元：Applied Energy  

The use of Deep Reinforcement Learning (DRL) in building energy management is often hampered by data efficiency and computational challenges. The long training time, unstable, and potentially harmful control performance limit DRL's adaptability and practicality in building control applications. To address these issues, this study introduces a new method, called Generative Adversarial Imitation Learning (GAIL), which effectively utilizes expert knowledge and demonstrations. Expert demonstrations range from fine-tuned rule-based controls to strategies inspired by optimization algorithms. By combining the capabilities of the generative adversarial network and imitation learning, GAIL is known for effectively learning the optimal strategy from expert demonstrations through an adversarial training process. We conducted a comprehensive evaluation comparing GAIL's performance with the DRL algorithm Proximal Policy Optimization (PPO) in the scenario of controlling a variable air volume system for load shifting in commercial buildings. Impressively, GAIL, guided by expert demonstrations based on model predictive control, achieved significantly improved computational efficiency and effectiveness. In terms of unified cumulative reward, GAIL with data augmentation achieved 95% expert performance, 22% higher than baseline rule-based control, in 100 training epochs; GAIL also outperformed PPO by 7%, resulting in 2% lower energy costs and notably improved thermal comfort. This improvement in thermal comfort is evidenced by a reduction of 18.65 unmet degree hours during the one-week operation. In comparison, PPO requires more training time and still lags behind GAIL in cumulative reward even after 500 epochs. These findings highlight the advantages of GAIL in enabling faster learning with fewer training samples, resulting in cost-effective solutions due to lower computational requirements. Overall, GAIL presents a promising approach to building energy management and provides a practical and flexible solution to the shortcomings of learning-based controllers that require extensive computational resources and training time.

DOI： 10.1016/j.apenergy.2024.123753

Scopus

掲載種別：研究論文（学術雑誌）   出版者・発行元：Applied Energy  

The rising demands for comfort alongside energy conservation underscore the importance of intelligent air conditioning control systems. Model Predictive Control (MPC) stands out as an advanced control strategy capable of addressing these demands. However, accurate prediction of all relevant variables remains a challenge in practical scenarios, complicating MPC's ability to devise effective control actions amid prediction inaccuracies. To counteract this issue, this paper introduces an enhanced Double-Layer Model Predictive Control (DLMPC) algorithm. This innovative approach adjusts for discrepancies between forecasted and actual values without the need for additional variables and models, thereby reducing the adverse effects of prediction errors. Additionally, we develop precise models for room temperature simulation and for calculating air conditioning (AC) load and energy consumption, grounded in empirical data from residential settings and AC performance tests. Validation of these models demonstrates their efficacy in enabling MPC to formulate efficacious control strategies. When juxtaposed with a baseline model, the DLMPC algorithm significantly improves temperature regulation accuracy by up to 15.12% and achieves a 10.50% reduction in energy consumption over the heating season.

DOI： 10.1016/j.apenergy.2024.123652

Scopus

掲載種別：研究論文（学術雑誌）   出版者・発行元：Energy  

The prediction of renewable energy plays a critical role in optimizing the operation, fault diagnosis, and other essential tasks within its energy system. Given the scarcity of labeled data and the proliferation of newly established renewable energy systems, the concept of deep transfer learning can enhance the performance of deep learning prediction models in the renewable energy domain. Most existing studies primarily discuss the application of individual transfer learning algorithms, lacking comparative analysis and detailed methodological discourse among them. In this study, we compare the effectiveness of domain adaptation and fine-tuning as transfer learning methods in scenarios with limited labeled data. Furthermore, we introduce a composite transfer learning framework that initially applies domain adaptation followed by fine-tuning. Utilizing solar radiation data measured in Tokyo and Okinawa, we designed two sets of experiments with interchangeable source and target domains to verify the effectiveness and robustness of the proposed model. The experimental outcomes indicate that the sequential application of domain adaptation followed by fine-tuning surpasses the standalone use of either method, achieving prediction accuracy up to 98.89 % of the model trained with two full years of data. Additionally, this approach demonstrates superior prediction stability and lower outlier values.

DOI： 10.1016/j.energy.2024.131863

Scopus

掲載種別：研究論文（学術雑誌）   出版者・発行元：Applied Energy  

In the context of escalating energy consumption in buildings, particularly from air conditioning (AC), the intelligent control of AC has become increasingly crucial. Accurately predicting future energy consumption for AC, the indoor environment, and determining the optimal settings have emerged as key challenges in intelligent AC control. In this study, a hybrid self-learning dynamic graph neural network with self-attention mechanism is proposed for AC forecasting. Addressing the gaps in the existing graph neural network applications, this model overcomes the limitations of static graph structures by constructing evolving adjacency matrices integrated with a gated recurrent unit and self-attention, effectively capturing the dynamic relationships between changing feature quantities. Additionally, a multi-task prediction (MTP) module that utilizes both past and future data is proposed. The MTP enables the application of a single model to multiple prediction tasks, thereby obviating the need for separate model training for each task. An experiment in an actual outdoor environment was designed to verify the predictive performance of the proposed model. The results indicate that the proposed model achieves superior accuracy for all target variables across different tasks under various AC conditions, particularly for variables with strong non-linearity, which showed a maximum improvement of 24.94% in correlation coefficient (R2) compared to long-short term memory network. With the MTP, the single model applied to multiple prediction tasks exhibited only a minimal sacrifice in accuracy, resulting in a mere 0.64% decrease in average R2 of all target variables for the proposed model.

DOI： 10.1016/j.apenergy.2024.123156

Scopus

掲載種別：研究論文（学術雑誌）   出版者・発行元：Building and Environment  

Control strategies for variable air volume (VAV) air-conditioning systems play a pivotal role in ensuring indoor environmental quality and energy efficiency. However, conventional approaches, such as static pressure reset (SPR) control, focus on managing indoor air temperature without considering the room pressure, which can lead to unbalanced room pressure and undesirable air leakage. Moreover, with the application of prevalent building pressure control strategies, such as airflow tracking control, to multizone VAV systems, neutralization of the room pressure is difficult across multiple zones in VAV systems. Therefore, this study introduces a model-based optimal control strategy for multizone VAV air-conditioning systems. The proposed strategy uses a multiobjective optimization framework to regulate fan frequencies and damper openings on both the supply and return sides. This holistic approach facilitates the simultaneous control of the indoor air temperature and room pressure while minimizing fan energy consumption. To assess the effectiveness of the proposed strategy, four control strategies were tested using a Python-based simulation testbed. The results demonstrate that the proposed strategy effectively maintains the indoor air temperature, neutralizes room pressure, and reduces fan energy consumption, thereby contributing to the overall efficiency of the VAV system. Moreover, the results highlight the limitations associated with combining airflow tracking control with SPR control for room pressure regulation in multizone VAV systems. This highlights the importance of adopting a model-based approach to address the complexities of concurrent room pressure and indoor air temperature control.

DOI： 10.1016/j.buildenv.2024.111464

Scopus