Cloud Computing Power Big Data Analysis Technology and Application Based on Improved Algorithm

This paper proposes a hybrid power load forecasting model based on improved reinforcement learning and adaptive filtering. The model combines reinforcement learning and adaptive filtering algorithms to improve the accuracy and real-time performance of power load forecasting. Specifically, the reinforcement learning part adopts the Q-learning algorithm to continuously adjust the prediction strategy according to real-time feedback, so that the model can flexibly respond to dynamic changes in the power system. The adaptive filtering part (such as Kalman filtering) is used to smooth the noise and uncertainty in the power load data, further improving the accuracy and stability of the prediction. Experimental results show that the hybrid model has obvious superiority in power load prediction. Compared with the conventional regression and SVM, the forecast error is reduced and the forecast precision is about 18%. Moreover, the model has good performance in real time response and computing efficiency, and it can be used to deal with the variation of load mode and outside interference. It is proved that the research provides a new and efficient method for power load prediction, and it is especially applicable to intelligent grid and real time monitoring system.