As people spend most of their time indoors, the quality of the indoor lighting environment plays a crucial role in occupant health, mood, and productivity. While modern glazed curtain walls improve daylighting potential, they also heighten the risks of glare and associated solar heat gains that may result in occupant discomfort and overheating. To continuously ensure visual comfort while providing shading, kinetic responsive facades controlled by sensors and actuators can change the angles of the elements. Conventional control methods for shading devices mainly involve the unified control of each element. However, as each element of the kinetic responsive facade can be controlled independently, the number of potential control actions increases exponentially with the number of facade elements and possible angles. Traditional rule-based methods are challenging for handling this multi-objective high-dimensional control problem. This paper introduces a novel self-learning, real-time reinforcement learning (RL) controller that can interact with the environment to find a globally optimal control solution for each element in kinetic responsive facades, thereby meeting visual quality and shading goals. The configuration and workflow of the proposed RL controller are introduced and tested vertically, diagonally, and radially folding responsive facades. The results demonstrate that the proposed RL controller effectively maintains horizontal and vertical illuminance, with 72.92% of test points in occupied spaces falling within the defined comfort range. Additionally, it keeps the daylight glare probability (DGP) below 0.35, a level generally considered imperceptible.

Outlier detection plays a critical role in building operation optimization and data quality maintenance. However, existing methods often struggle with the complexity and variability of building energy data, leading to poorly generalized and explainable results. To address the gap, this study introduces a novel Vision-based Outlier Detection (VOD) approach, leveraging computer vision models to spot outliers in the building energy records. The models are trained to identify outliers by analyzing the load shapes in 2D time series plots derived from the energy data. The VOD approach is tested on four years of workday time-series electricity consumption data from 290 commercial buildings in the United States. Two distinct models are developed for different usage purposes, namely a classification model for broad-level outlier detection and an object detection model for the demands of precise pinpointing of outliers. The classification model is also interpreted via Grad-CAM to enhance its usage reliability. The classification model achieves an F1 score of 0.88, and the object detection model achieves an Average Precision (AP) of 0.84. VOD is a very efficient path to identifying energy consumption outliers in building operations, paving the way for the enhancement of building energy data quality, operation efficiency, and energy savings.