Prediction Algorithm for Power Outage Areas of Affected Customers Based on CNN-LSTM

Predicting outage areas for affected customers in a disaster requires real-time meteorological data, which can be challenging to obtain and process with high quality. In particular, missing or inaccurate data can occur in certain regions or during extreme weather events. Therefore, a Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) based algorithm is proposed to improve the accuracy and reduce the prediction time in the outage region. Ground-based automated weather observatories are used to obtain real-time weather data, including extreme weather data. These data are pre-processed to enhance data quality and accuracy by removing duplicates, filling in missing values, handling anomalies, normalizing input variables, and reducing dimensionality. Based on the results of the data pre-processing, the outage rate was calculated for different types of meteorological disasters and the geographical characteristics of the outages were analyzed. This analysis provides insights into the impact of different types of meteorological disasters on power outages and helps improve the accuracy of predictive models. The proposed algorithm employs a CNN neural network to capture spatial and temporal information from raw meteorological data by stacking convolution and pooling layers. The extracted features are then organized and output by fully connected layers, laying the foundation for subsequent time series modeling. An LSTM network is further utilized to construct a prediction model for the outage area, which takes as input the feature extraction results of the meteorological data. By integrating the temporal dimension information of meteorological data, the model outputs accurate predictions for the outage area. The experimental results demonstrate a consistent outcome with the actual test results, achieving high prediction accuracy with a short prediction time of 4.3 s and a maximum non-outage detection rate of 2%. Therefore, the proposed algorithm proves to be significant for accurate and fast prediction of outage areas for affected customers in real world applications.