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Online Power Grid Emergency Material Demand Forecasting Model in Typhoon Disaster Based on Relevance Vector Machine

Power grid is often destroyed due to frequent typhoon disasters in coastal area of China, when making urgent repair on it, the guarantee of emergency supplies of power grid became vital. Currently, the sample data collected by the power grid company for typhoon events is relatively few, while typhoons events continue to occur, the data could be constantly obtained along with time series, but the traditional predictive methods for emergency supplies quantity is offline mode mostly. In this paper, we come up with an Online-RVM model to predict emergency supplies quantity by extract feature of typhoon event. In this model, we first use micro-genetic algorithm to find out the optimal RVM kernel function parameter, and introduce fast marginal likelihood algorithm to speed up the model update. It turned out that the prediction model we came up with gain good performance in small sample training data compared with classic predictive methods for emergency supplies, and it could has a quick and accurate prediction for emergency supplies quantity in next typhoon event.