In this paper, a tool for the detection and diagnosis of anomalous electrical daily energy patterns relative to a transformer substation of a university campus was developed and tested. Through an innovative pattern recognition analysis consisting in a multi-step clustering process, six clusters of anomalous daily load profiles were identified and isolated in two-year historical data of total electrical energy consumption. The infrequent electrical load profiles were found to be strongly affected, in terms of both shape and magnitude, by the energy consumption behaviour related to the heating/cooling mechanical room. Then, a fault-free predictive model, which uses artificial neural network (ANN) in combination with a Regression Tree, was developed to detect anomalous trends of the electrical energy consumption. The model was able to detect the 93.7% of the anomalous profiles and only the 5% of fault-free days were wrongly predicted as anomalous. Eventually, a diagnosis phase was conceived and validated with a testing data set. A number of daily abnormal load profiles were detected and compared with the centroids of the anomalous clusters identified in the pattern-recognition stage. The work led to the development of a flexible intelligent tool useful for operating a continuous commissioning of the campus facilities.

在本文中，开发并测试了一种用于检测和诊断与大学校园变电站相关的日常用电异常模式的工具。通过包含多步聚类过程的创新模式识别分析，在两年的总电能消耗历史数据中识别并隔离了六组异常的每日负荷曲线。发现不频繁的电负载曲线在形状和大小上都受到与加热/冷却机械室有关的能耗行为的强烈影响。然后，开发了结合人工神经网络（ANN）和回归树的无故障预测模型，以检测电能消耗的异常趋势。该模型能够检测到93。错误地将7％的异常廓线和5％的无故障天数错误地预测为异常。最终，构思了一个诊断阶段并使用测试数据集对其进行了验证。检测到许多日常异常负荷曲线，并将其与在模式识别阶段识别出的异常簇的质心进行比较。这项工作导致开发了一种灵活的智能工具，可用于对校园设施进行连续调试。