Wen-Tai Li

Jung-Chieh Chen, Hwei-Ming Chung, Chao-Kai Wen et al.

Efficient and accurate state estimation is essential for the optimal management of the future smart grid. However, to meet the requirements of deploying the future grid at a large scale, the state estimation algorithm must be able to accomplish two major tasks: (1) combining measurement data with different qualities to attain an optimal state estimate and (2) dealing with the large number of measurement data rendered by meter devices. To address these two tasks, we first propose a practical solution using a very short word length to represent a partial measurement of the system state in the meter device to reduce the amount of data. We then develop a unified probabilistic framework based on a Bayesian belief inference to incorporate measurements of different qualities to obtain an optimal state estimate. Simulation results demonstrate that the proposed scheme significantly outperforms other linear estimators in different test scenarios. These findings indicate that the proposed scheme not only has the ability to integrate data with different qualities but can also decrease the amount of data that needs to be transmitted and processed.

Xilei Dai, Ruotian Chen, Songze Guan et al.

Reinforcement learning (RL) has proven effective for AI-based building energy management. However, there is a lack of flexible framework to implement RL across various control problems in building energy management. To address this gap, we propose BuildingGym, an open-source tool designed as a research-friendly and flexible framework for training RL control strategies for common challenges in building energy management. BuildingGym integrates EnergyPlus as its core simulator, making it suitable for both system-level and room-level control. Additionally, BuildingGym is able to accept external signals as control inputs instead of taking the building as a stand-alone entity. This feature makes BuildingGym applicable for more flexible environments, e.g. smart grid and EVs community. The tool provides several built-in RL algorithms for control strategy training, simplifying the process for building managers to obtain optimal control strategies. Users can achieve this by following a few straightforward steps to configure BuildingGym for optimization control for common problems in the building energy management field. Moreover, AI specialists can easily implement and test state-of-the-art control algorithms within the platform. BuildingGym bridges the gap between building managers and AI specialists by allowing for the easy configuration and replacement of RL algorithms, simulators, and control environments or problems. With BuildingGym, we efficiently set up training tasks for cooling load management, targeting both constant and dynamic cooling load management. The built-in algorithms demonstrated strong performance across both tasks, highlighting the effectiveness of BuildingGym in optimizing cooling strategies.