BP neural network based wireless sensor network for solar energy prediction

Wireless sensor networks are generally deployed in remote areas and areas with complex geographical environments. It is difficult to replace sensor node batteries. Energy acquisition sensor network nodes are powered by solar cells to provide energy to wireless sensor nodes. The randomness and uncertainty of solar energy in the monitoring area make it impossible to continuously provide energy for sensor network nodes. By predicting the results of solar energy and combining the information collected by the wireless sensor network, the energy usage of wireless sensor network nodes is reasonably planned, thereby improving wireless transmission. sensor network lifetime and the accuracy and reliability of sensor node measurement information. Prediction of solar energy in the monitoring area is an important part of improving the monitoring quality and life of wireless sensor networks. In this paper, the BP neural network combined with the climatic factors in the wireless sensor network monitoring area, such as illumination, the average diffuse reflection intensity of the solar panel on the day, etc., are used as reference data to predict the solar energy in the wireless sensor network monitoring area. Compared with the traditional Exponentially Weighted Moving Average algorithm (EWMA), the error rate of the prediction results is lower, and the prediction effect is better due to the comprehensive consideration of various climatic factors.