A data mining research on office building energy pattern based on time-series energy consumption data

Liu, Xiaodong; Sun, Haode; Han, Shanshan; Han, Shuyan; Niu, Shengnan; Qin, Wen; Sun, Piman; Song, Dexuan

doi:10.1016/j.enbuild.2022.111888

Cited by 19 publications

(9 citation statements)

References 39 publications

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“…To overcome the issue, a Density-Based Spatial Clustering of Applications with Noise (DBSCAN) method [12] becomes more commonly used in detecting outliers in building energy data [13][14][15][16]. The algorithm clusters the data points into recognizable groups when they are close to each other.…”

Section: Conventional Outlier Detection Methodsmentioning

confidence: 99%

VOD: Vision-Based Building Energy Data Outlier Detection

Tian,

Zhao,

et al. 2024

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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.

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Section: Conventional Outlier Detection Methodsmentioning

confidence: 99%

VOD: Vision-Based Building Energy Data Outlier Detection

Tian,

Zhao,

et al. 2024

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“…IRR, by definition, means the discount rate that causes the NPV to be zero. Thus, the IRR can be obtained by solving Equation (10) [42].…”

Section: Economic Indexesmentioning

confidence: 99%

“…The energy demand for HVAC systems depends on many building factors, such as the building envelope, climate, and occupant behavior [8,9]. The aggregate result from profiling office building energy usage patterns reveals that building energy consumption throughout the day varied depending on the operation of lighting and air conditioning systems [10]. The heat generated from a lighting system in an office building is large enough to have a significant impact on the air conditioning system by increasing the building's cooling load [11].…”

Section: Introductionmentioning

confidence: 99%

A Study on Transparent Type Envelope Material in Terms of Overall Thermal Transfer, Energy, and Economy for an Office Building Based on the Thai Building Energy Code

et al. 2023

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Energy consumption in buildings has increased significantly as population and economic activities are concentrated in urban areas. Air conditioning accounts for a significant percentage of energy consumption in buildings, especially in tropical climates. The main area where heat can be transferred into the building is through glass windows. Thus, this study aims to evaluate feasibility in terms of overall thermal transfer value (OTTV), energy, and economics for retrofitting different glass materials in an office building in Thailand by using building energy code (BEC) software. The software uses Thailand’s building energy code as the standard to evaluate the energy performance of the case study building in comparison with different glass types used in retrofitted cases. From an economic perspective, the internal rate of return (IRR) and discounted payback periods (DPP) were used as determining indexes. The results demonstrated that retrofitted windows with the best energy-efficient glass might achieve energy performance, but installation cost can reduce economic feasibility, while the glass with the second lowest heat transfer coefficient can reduce the OTTV by 68.89% and building energy consumption by 16.87%. However, it can achieve the highest economic performance with 10.70% IRR and DPP at 11.83 years. Therefore, the balance between energy and economic factors must be considered to provide energy-efficient and investment-friendly glass materials for retrofit projects. In addition, the study focuses specifically on tropical climates. Thus, the finding may not be reflected similarly for buildings located in different regions.

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“…The specific energy consumption of office buildings showed an opposite trend to the floor area. [40] USA Cooling, lighting, equipment, natural gas Artificial intelligent algorithms and cluster analysis Cooling energy primarily manipulates the total energy indicating more than 80% degree in terms of confidence level. The main subentry energy determining total energy is different in disparate stages.…”

Section: Introductionmentioning

confidence: 99%