A real-time detection method of abnormal building energy consumption data coupled POD-LSE and FCD

Abstract

To understand the energy consumption characteristics of buildings, numerous building energy consumption monitoring platforms and internet of building energy systems have been established in the large public buildings, government office buildings, as well as colleges and universities. Although the platforms provide us with a large amount of energy consumption in-formation, there are serious problems. However, the validity of the energy consumption data is one of the most common problems. In this paper, a real-time detection method, which coupled fractal correlation dimension and proper orthogonal decomposition linear stochastic estimation, is presented to identify abnormal energy consumption data. The proper threshold is selected with varying operational conditions. The result shows that using this real-time meth-od yields a higher correctness rate than using traditional method in fault detection of data loss and outlier. It’s worth pointing out that a hybrid method combining with other intelligent algorithms should be promising to identify and classify small bias abnormal data fault in further investigation.