Distribution Characters Improvement and ALN-LSTMP approach in enabling big data analytics for serving precise load classification

Accurate load classification can provide support for scenarios such as demand response scheme design, load characteristic analysis and high-precision forecasting. Aiming at the low classification accuracy of traditional load classification methods, a distribution characters improvement and ALN-LSTMP approach in enabling big data analytics for serving precise load classification is proposed. Firstly, considering the data missing and category imbalance in the load data set, a data distribution improvement strategy is proposed to improve the model training effect; Secondly, an improved ALN-LSTMP model is proposed to further enhance the accuracy of load classification in view of the low calculation efficiency and the shortage of over fitting easily of traditional machine learning models when dealing with load big data; Finally, an example is given to verify the effectiveness of the proposed method.