Research Article
Short-term Ultraviolet Index Forecasting Using ARIMA Model
@INPROCEEDINGS{10.4108/eai.6-6-2021.2307719, author={Shuangyue Xiao and Shengchi Liu and Li Liu}, title={Short-term Ultraviolet Index Forecasting Using ARIMA Model}, proceedings={Proceedings of the 8th EAI International Conference on Green Energy and Networking, GreeNets 2021, June 6-7, 2021, Dalian, People’s Republic of China}, publisher={EAI}, proceedings_a={GREENETS}, year={2021}, month={8}, keywords={arima model ultraviolet index forecasting the smart micro-grid}, doi={10.4108/eai.6-6-2021.2307719} }- Shuangyue Xiao
Shengchi Liu
Li Liu
Year: 2021
Short-term Ultraviolet Index Forecasting Using ARIMA Model
GREENETS
EAI
DOI: 10.4108/eai.6-6-2021.2307719
Abstract
Solar energy is recognized as an ideal renewable energy. Solar photovoltaic power generation is an important way to use solar energy. It can alleviate the existing energy crisis and various environmental problems caused by traditional energy. Photovoltaic power generation as a clean renewable energy gradually plays an increasingly prominent role in smart micro-grid. Due to the fluctuation of solar radiation intensity, accurate power prediction is one of the important conditions for the successful grid connection of solar power plants. This paper focuses on the prediction of ultraviolet power index. Based on the analysis of cumulative autoregressive moving average model, the stationary of ultraviolet index time series was detected, the order of ultraviolet index model was estimated, and the ARIMA model of ultraviolet index was determined. The prediction accuracy of the model is determined by the root mean square error (RMSE) and mean absolute error (MAE).