Analysis and prediction of power distribution network loss based on machine learning

Abstract

AbstractIn this paper, the historical network loss is calculated by using the cleaned historical data collected by the user electricity meter, the transformer gateway meter every 15 min and the gateway meter of the transformer substation every half an hour. Use machine learning technologies to analyze the loss of distribution network, reveal the changing law of distribution network loss and its influencing factors (electrical/non‐electrical parameters), and predict future distribution network loss. Pearson correlation, characteristics of the Random Forest important attributes and MIC (Maximal Information Coefficient) are adopted to analyze correlations between the power network loss and the electrical parameters such as unbalanced three‐phase, power factor, load rate, active power, reactive power together with atmospheric factors (temperature, humidity, and wind speed). The factors strongly related to network loss as the input of the multi‐parallel input multi‐step output stacking LSTM (Long Term Memory) prediction model to predict network loss. The simulation data in this paper comes from the actual data of 44 subareas of the 10 KV distribution network. The experimental results show that the data cleaning methods: the cluster‐based Local Outlier Factor algorithm and the improved Random Forest imputation algorithm are effective for enhancing the prediction accuracy. Especially the improved Random Forest algorithm is robust to all missing forms. Compared with the baseline model, the multi‐parallel input multi‐step output stacking LSTM prediction model has higher forecasting accuracy.