• Energy Research

Classification of time series is a fundamental problem in energy distribution, especially to extract information about events that occurred during the observation period. In this paper, we propose a solution to the problem of identifying energy thefts by introducing a classification method based on convolutional neural networks. The input structure to the model is based on real data that have been certified by external authorities and regards thefts operated by the final user with physical intervention. The training of the neural network is done by means of yearly time series of monthly data, which pertain to different physical quantities relevant to the user profile. The proposed method has been experimentally tested and verified against acceptable test results in different conditions, even giving an indication on where in the sequence the theft has occurred.

Classification of time series is a fundamental problem in energy distribution, especially to extract information about events that occurred during the observation period. In this paper, we propose a solution to the problem of identifying energy thefts by introducing a classification method based on convolutional neural networks. The input structure to the model is based on real data that have been certified by external authorities and regards thefts operated by the final user with physical intervention. The training of the neural network is done by means of yearly time series of monthly data, which pertain to different physical quantities relevant to the user profile. The proposed method has been experimentally tested and verified against acceptable test results in different conditions, even giving an indication on where in the sequence the theft has occurred.