Electricity theft is a big problem faced by all energy distribution services and continues to rising. Therefore, studies on electricity theft detection techniques have increased in recent years. Unsuitable calibration and illegal calibration of energy meters during production may cause non-technical losses. Non-technical losses have been a major concern for the resulting security risks and the immeasurable loss of income. In most of the meter tampered locations, damaged meter terminals and/or illegal applications cannot be distinguishable during checking. In fact, electric distribution companies will never be able to eliminate electricity theft. But it is possible to take measure to detect, prevent and reduce it. In this paper, we developed by using deep learning methods on real daily electricity consumption data (Electricity consumption dataset of State Grid Corporation of China). Data reduction has been made by developing a new method to make the dataset more usable and to extract meaningful results. A Long Short-Term Memory (LSTM) based deep learning method has been developed for the dataset to be able to recognize the actual daily electricity consumption data of 2016. In order to evaluate the performance of the proposed method, the accuracy, prediction and recall metric was used by considering the five cross-fold technique. Performance of the proposed methods were found to be better than previously reported results.