Electricity prediction model based on improved Gaussian process regression

Power system forecasting assumes a crucial role in ensuring the safe operation of oil and gas stations, encompassing three pivotal facets: load forecasting, output forecasting and health status forecasting. Health status prediction is mainly used for equipment maintenance. Through the continuous monitoring of equipment status, potential issues can be detected promptly, thereby guaranteeing the stable operation of the power system. In this process, machine learning technology plays a key role. It has the ability to analyze and predict features and can efficiently process power data. In addition, the research conclusion points out that machine learning regression methods can be used to predict the power output of oil and gas stations. This paper constructs an optimized Gaussian process regression model, named GPRS, for predicting electricity in cyclic power data. To optimize the hyperparameters of GPR, a Bayesian estimation method is introduced. In order to reduce the adverse effects of noise in complex environments, wavelet theory is also introduced to optimize data processing. Experimental results also prove that our GPRS model has excellent performance, with the accuracy of R ² reaching 0.976.