This paper investigates and compares people’s subjective impression of an office with a biophilic design and blue lighting. Existing studies have examined their influence on perception separately, but how they compare is unclear. Additionally, only a few studies have used an office setting as a case study. To address this research gap, this study collected people’s ratings and rankings of four simulated interior scenes of a private office using an online survey. The scenes include blue lighting, a biophilic design with daylight and view, a biophilic design with indoor plants, and a non-biophilic baseline with conventional white lighting. A total of 284 complete responses were collected and analyzed using a mixed-effect model. It was found that the two biophilic designs improved people’s perception of the office compared to the base case. The biophilic design with access to daylight and view outperformed the space with indoor plants in all the examined perceptual categories, specifically how the office space was perceived by participants as brighter, more comfortable, and spacious. On the contrary, the space with blue lighting decreased people’s ratings in most perceptual attributes in comparison to the baseline. The negative influence was notably significant in how lively, comfortable, bright, and appealing the space was perceived as being by participants. Subjects’ preference rankings of the four simulated office spaces showed a similar pattern.

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.

The traditional building energy use analysis classification is typically conducted according to the primary uses. However, some buildings with different primary uses have similar energy use patterns. This is because multiple attributes, other than building types, impact energy. Also, the same building type may have significantly different energy use patterns due to factors such as building age, size, equipment, and climate conditions. In order to better improve the performance of building energy benchmarking, this study employs the unsupervised K-Means++ model to classify the building energy benchmarking with comprehensive weather data from four cities in ASHRAE climate zones 3A, 4A, 5A, and 6A under five years from 2016 to 2020. During the classification process, the Elbow and Calinski Harabasz metrics are conducted to optimize the number of clusters. After classifying the total dataset, this study splits the data of each cluster into training and test sub-datasets for training and testing. Each test dataset is isolated by the same year in each climate zone. Then, LGBM tree-boosting ensemble regression model is applied to evaluate the model performance for each cluster. During the training process of each cluster, the Grid-search with Cross-validation is operated to optimize the hyperparameters and improve the running efficiency. The results show that each classified model by K-Means++ performs well, all achieving more than 80% R2s for actual and predicted values. Furthermore, the building clusters are significantly different from the primary use classification. In addition, the LGBM model features positive and negative impacts on the building energy use intensity of each cluster is also investigated, giving a deeper scope in understanding the model performance for future studies.