Abstract:

The detection of abnormal energy consumption in building lighting sockets can effectively improve building energy efficiency, which is of great significance for implementing building energy optimization measures and achieving building energy conservation control research. Due to the fact that the energy consumption of building lighting socket systems is greatly affected by the random behavior of personnel inside the building, this paper proposes a method for detecting energy consumption anomalies in building lighting sockets based on Long Short Term Memory Autoencoder (LSTM-AE) to address the problems of high noise and difficult feature extraction in time series data of lighting sockets. On the basis of decision tree partitioning of working conditions, deep learning methods are used to automatically learn the reconstruction errors of normal and abnormal samples, achieving unsupervised recognition and detection of building energy consumption abnormal samples. The results show that the number of iterations, neurons, and activation function all have a significant impact on the model properties. The electricity consumption data during working hours on weekdays, non working hours on non working days, and non working hours on non working days are all normally distributed. The power consumption data under working day conditions is more stable, therefore its anomaly detection accuracy is higher than that under non working day conditions. The average accuracy, recall, and F1 Score of the proposed method are 91.23%, 90.87%, and 90.80%, respectively, which can effectively detect energy consumption anomalies in building lighting socket systems.

Key words: abnormal energy consumption detection, deep learning, lighting socket system, reconstruction error, unsupervised learning