An intelligent WSN-based digital twin design for photovoltaic power generation forecast
Abstract
Photovoltaic power generation has been extensively utilized for power supply in remote areas. Due to the effect of weather and environmental conditions, its power generation experiences fluctuations. This paper proposes a dedicated WSN-based digital twin design for photovoltaic power generation. In this design, WSN is used to regularly collect and transmit monitoring data. The cloud platform and neural network are then integrated to displays equipment's status and predict power generation. Experimental results demonstrate that this digital twin design can effectively display the real-time operating status and environmental information. Using the LSTM-CNN algorithm achieve an accurate power prediction, with the RMSE and MAE values of 0.436 and 0.209 respectively. The real-time status monitoring and power prediction of photovoltaic equipment can not only provide a basis for fault diagnosis, but also optimize power grid dispatch in remote areas.