Short-term fast forecasting based on family behavior pattern recognition for small-scale users load

Household electricity consumption has been rising gradually with the improvement of living standards. Making short-term load forecasting at the small-scale users plays an increasingly important role in the future power network planning and operation. To meet the efficiency of the dispatching system and the demand of human daily power consumption, an optimal forecasting model Attention-CNN-GRU of small-scale users load at various periods of the day based on family behavior pattern recognition is proposed in this study. The low-level data information (smart meter data) is used to build the high-level model (small-scale users load). Attention mechanism and convolutional neural networks (CNN) can further enhance the prediction accuracy of gated recurrent unit (GRU) and notably shorten its prediction time. The recognition of family behavior patterns can be achieved through the users’ smart meter data, and users are aggregated into K categories. The results of optimal K category prediction under the family behavior model are summarized as the final prediction outcome. This idea framework is tested on real users’ smart meter data, and its performance is comprehensively compared with different benchmarks. The results present strong compatibility in the small-scale users load forecasting model at various periods of the day and swift short-term prediction of users load compared to other prediction models. The time is shortened by 1/4 compared with the GRU/LSTM model. Furthermore, the accuracy is improved to 92.06% (MAPE is 7.94%).