# Data-Driven Prediction Method for Power Grid State Subjected to Heavy-Rain Hazards
## 1 Introduction  
The electrical power grid constitutes a vital infrastructural component and serves as an essential foundation for modern life. Severe weather events have been considered a major cause of enormous power-outage events. Moreover, the frequency and intensity of severe weather events have increased over recent years [1; 2; 3; 4; 5]. Due to climate change, the frequency, intensity, and duration of extreme weather are expected to increase further, resulting in large-scale power outages [6] and motivating this study on grid resilience, defined as the ability to withstand and rapidly recover from a severe weather event. A key driver for improving grid resilience is to predict the state of the grid [7]. The grid state demonstrates how the grid, or its components, performs with respect to severe weather events and it can determine the operating conditions of the grid. Identification of the grid state is an essential factor in mitigating the impact of severe weather, preparing the emergency response, and managing the restoration crew within an acceptable time and cost limits. The grid is typically designed to operate under a certain weather intensity. If the weather intensity increases beyond the design criteria, there will be an increase in the probability of a power outage. Therefore, the grid state can be represented by either a normal or outage state, given the weather intensity. Modeling the grid state is very complex; therefore, the formulation of the relationship and its solution is difficult to determine. Data-driven methods can be utilized to model the complex relationship for the grid state; these methods are time efficient and the components do not require a physical model. Data-driven methods, such as machine learning (ML) models, can recognize key knowledge from a dataset without any prior knowledge about the dataset. Thus, ML methods have been utilized to solve power grid-related problems. ML applications include fault identification and detection, based on the support vector machine or neural network [8, 9], and risk analysis [10]. A more prevalent application of ML in the power grid field is in relation to forecasting. Several ML-based forecasting methods have been introduced for short-term load forecasting [11, 12], renewable power forecasting [13, 14], electricity price forecasting [15], and power grid damage forecasting [16, 17, 18, 19, 20], which utilize regression models and tree-based regression. A probabilistic approach based on the Tobit regression model to forecast power outage was proposed by [21]. A Bayesian network-based storm-induced power outage forecasting model for an electrical grid was presented in [22]. Predictive analytics for extreme weather events were presented in [23]. A power system contingency forecasting tool under extreme weather conditions was presented in [24]. These authors proposed a risk-based security method to predict the risky contingencies on the power system. This study presents an ML-based method for predicting the power grid state subjected to heavy-rain hazards. Two sets of historical data were used: Local weather data and power grid outage data. First, we investigated the heavy-rain-related outage distribution and analyzed the correlated characteristics between weather and outages in order to characterize the heavy-rain events. The analysis results show that multiple weather effects are significant in causing power outages, even under heavy-rain conditions. Furthermore, this study proposes a cost-sensitive prediction method using the support vector machine (SVM) model. The accuracy of the model was improved by applying a cost-sensitive learning algorithm to the SVM model, which was subsequently used to predict the state of the grid. The developed model was evaluated using G-mean values. The proposed method was verified using the actual data of a heavy-rain event that occurred in South Korea.