Abstract Electricity load is crucial to managing energy grids and determining energy policies. Electricity tariffs offer diverse opportunities to consumers at different prices, necessitating proper usage, detection of non-compliant users, and appropriate sanctions. This study utilizes real-life electricity load data from 310 users in Türkiye's five most common tariff classes to create a three-channel time series. An innovative method combining Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) models is proposed to predict tariff groups based on electricity load data. After preprocessing, the time series are converted into two-dimensional matrices, and features are extracted using a 2D CNN network. These features are then utilized in an LSTM network to highlight sequential relationships. The developed model achieved 87% accuracy in training and 83% in validation, demonstrating its effectiveness in predicting tariff groups from electricity load data. This study proposes and validates a hybrid CNN-LSTM-based tariff classification approach using real-world electricity consumption data for smart grid management and energy pricing, thereby contributing to the research on analyzing and utilizing electricity load data.
Ustundag et al. (Sun,) studied this question.