Building operations consume 30% of total power consumption and contribute 26%
of global power-related emissions. Therefore, monitoring, and early detection
of anomalies at the meter level are essential for residential and commercial
buildings. This work investigates both supervised and unsupervised approaches
and introduces a dynamic anomaly detection system. The system introduces a
supervised Light Gradient Boosting machine and an unsupervised autoencoder with
a dynamic threshold. This system is designed to provide real-time detection of
anomalies at the meter level. The proposed dynamical system comes with a
dynamic threshold based on the Mahalanobis distance and moving averages. This
approach allows the system to adapt to changes in the data distribution over
time. The effectiveness of the proposed system is evaluated using real-life
power consumption data collected from smart metering systems. This empirical
testing ensures that the system's performance is validated under real-world
conditions. By detecting unusual data movements and providing early warnings,
the proposed system contributes significantly to visual analytics and decision
science. Early detection of anomalies enables timely troubleshooting,
preventing financial losses and potential disasters such as fire incidents.

该研究论文介绍了一个建筑运营中的异常检测系统，包括监测、早期发现、以及基于监测仪的实时异常检测方法。通过结合有监督学习和无监督学习算法，利用动态阈值实现对异常数据的实时检测，并通过真实数据的实证研究验证其有效性，这对于建筑运营中的决策科学和视觉分析有重要意义。

基于数据挖掘的高级计量系统集成动态异常检测方法

A Data Mining-Based Dynamical Anomaly Detection Method for Integrating  with an Advance Metering System

Model predictive control (MPC) can provide significant energy cost savings in
building operations in the form of energy-efficient control with better
occupant comfort, lower peak demand charges, and risk-free participation in
demand response. However, the engineering effort required to obtain
physics-based models of buildings is considered to be the biggest bottleneck in
making MPC scalable to real buildings. In this paper, we propose a data-driven
control algorithm based on neural networks to reduce this cost of model
identification. Our approach does not require building domain expertise or
retrofitting of existing heating and cooling systems. We validate our learning
and control algorithms on a two-story building with ten independently
controlled zones, located in Italy. We learn dynamical models of energy
consumption and zone temperatures with high accuracy and demonstrate energy
savings and better occupant comfort compared to the default system controller.

本研究提出了一种基于神经网络的数据驱动控制算法，以减少建筑物模型识别的成本，通过建筑物能耗和区域温度的动态模型，实现了较高的精度和与默认系统控制器相比的节能和更好的住户舒适度。