Short-term photovoltaic power generation predicting by input/output structure of weather forecast using deep learning

Abstract

In Korea, weather forecasts for fundamental weather factors, such as temperature, precipitation, wind direction and speed, humidity, and cloudiness, are provided for a three-day period in each region. This can facilitate predicting photovoltaic power generation based on weather forecasting. For this purpose, in the present paper, we aim to propose corresponding model. However, the Korea Meteorological Administration does not forecast the amount of solar radiation and sunshine that mostly influence the results of photovoltaic power generation prediction. In this study, we predict these parameters considering various input/output (I/O) variables and learning algorithms applied to weather forecasts on hourly weather data. Finally, we predict photovoltaic power generation based on the best sunshine and solar radiation prediction results. The data structure underlying all predictions relies on four models applied to fundamental weather factors on sunshine and solar radiation data two hours ago. Then, the photovoltaic power generation prediction is implemented using four models depending on whether to add the predicted sunshine and solar radiation data obtained at the previous step. The prediction algorithm relies on an adaptive neuro-fuzzy inference system and artificial neural network (ANN) techniques, including dynamic neural network (DNN), recurrent neural network (RNN), and long short-term memory (LSTM). The results of the conducted experiment indicate that ANN perform better than the neuro-fuzzy approach. Moreover, we demonstrate that RNN and LSTM are more suitable for the time series data structures compared with DNN. Furthermore, we report that the weather forecast structure and the model 4 structure, which includes sunshine and solar radiation data two hours ago, achieve the best prediction results.