摘要：AbstractDue to strict legislation on greenhouse gas emission reduction, energy intensive industries include the bakery industry are all under pressure to improve the energy efficiency in the manufacturing processes. In this paper, an energy monitoring system developed through the Point Energy Technology from the research group is first introduced for the data collection in a local bakery company. The outliers in the collected data may include valuable information about the status of machines, however, they also affect the data quality and the accuracy of the consequent data analysis. This paper discusses two algorithms for outlier detection, connectivity-based outlier factor (COF) and local outlier factor (LOF). For COF, the concept of connectivity-based outlier facto is adopted to identify whether an object is an outlier. For LOF, the local outlier factor based on a notion of local density represents the level of an object being an outlier. Experiments are conducted on the dataset from the oven in a production line to evaluate the effectiveness of three kernel functions, namely the Gaussian kernel, the Laplacian kernel and polynomial kernel. The experimental results show that the Gaussian-COF and the Laplacian-COF are more effective on valid oven data detection, which is significant for the further research work on energy management in the bakery company.
