Multi-step solar irradiation prediction based on weather forecast and generative deep learning model

Yuan Gao, Shohei Miyata, Yasunori Akashi
Renewable Energy, Volume 188, 2022, pp. 637-650
https://doi.org/10.1016/j.renene.2022.02.051

Abstract
With the rapid development of computer technology, more and more deep learning models are used in solar radiation (irradiation) prediction. There have been a lot of studies discussing the research of this type of model. However, how to better apply the deep learning model in the optimization method of building energy system, such as multi-step solar radiation (irradiation) prediction model in model predictive control (MPC), is still a challenging issue due to the complexity of the time series and the accumulation of errors in multi-step forecasts.
In this research, a deep generative model based on LSTM is developed for multi-step solar irradiation prediction at least 24 h in the future. Measured data and temperature forecast data from the Tokyo Meteorological Agency were used for training and testing in this experiment. The results show that generating the model first can effectively avoid the problem of error accumulation. The generative model can produce an accuracy improvement of 7.7% against traditional regression LSTM model. Secondly, the introduction of the temperature forecast data from the previous one day can increase the forecast accuracy by about 18% points. When the earlier temperature forecast is used, the forecast accuracy will gradually decrease, and the use of the temperature forecast released 3 days before can hardly improve the forecast effect. In the end, using hourly temperature forecasts will result in better forecast accuracy than using daily temperature forecasts.