A Neural Network Model for Predicting User Load and Photovoltaic Output in Home Microgrids

At present, many household users install solar panels and energy storage devices in their homes to form a small microgrid system. This system not only maintains the daily electricity needs of users, but also usually needs to fully utilize clean energy and save users' electricity costs. Therefore, in addition to maintaining power balance, its control system should also be able to optimize electricity costs, which requires a certain degree of predictive ability for user load size and photovoltaic output in the future. This paper constructs LSTM and BP neural networks and trains models by collecting electricity and photovoltaic data from four household users who have been in use for 8 months. For continuous user load data, it is treated as a time series, and thus user load prediction is treated as a time series problem. For photovoltaic output data, it is correlated with meteorological data at the same time, and photovoltaic output prediction is treated as a regression problem. Analyze using appropriate deep learning algorithms to explore the role of artificial intelligence prediction methods in controlling household microgrids, as well as the control methods of microgrids in household environments. Experiments have shown that this is an effective prediction method that can accurately predict future data trends and has certain application value.