Description

Electricity theft behavior has serious influence on the normal operation of power grid and the economic benefits of power enterprises. In a conventional power grid, energy theft is difficult to detect due to limited communication and data transition. The smart meter along with big data mining technology leads to significant technological innovation in the field of energy theft detection. The smart grid (SG) infrastructure generates a massive amount of data, including the power consumption of individual users. Utilizing this data, machine learning, and deep learning techniques can accurately identify electricity theft users. A deep learning model for automatic electricity theft detection is presented. This process considers experimentation to find the best configuration of the sequential model (SM) for classifying and identifying electricity theft. This process we proposes the different deep learning algorithms are considered to detect the anomaly from energy theft data. Here we process the artificial neural network algorithm to analysis the energy theft data and generates the result in the form of metrics like, accuracy, precision, recall, f1-score, specificity and Mathew correlation coefficient.