Application of Intelligent Algorithm in Power Load Forecasting and Energy Management

In this paper, a cloud platform-based power system energy load forecasting method is proposed to improve the efficiency and accuracy of power system energy load forecasting. Firstly, the architecture of distributed power grid data collection system is studied. Secondly, the extreme learning machine is used to predict the daily consumption of a certain area accurately. Experimental results show that the model presented in this paper can solve the problem of power grid energy consumption prediction. The multi-dimensional and multi-level collaborative optimization mechanism is integrated into the cloud platform load forecasting business to realize the correction and collaborative optimization of the original forecast data, thus greatly improving the smart grid load forecasting capability in the cloud environment. The platform architecture thus built will provide broad ideas and powerful technical support for smart grid load forecasting. This paper provides a scientific basis for power management and dispatching departments to make dispatch plan and operation control strategy in time.