Proceedings of the 8th EAI International Conference on Green Energy and Networking, GreeNets 2021, June 6-7, 2021, Dalian, People’s Republic of China

Research Article

Short-term Ultraviolet Index Forecasting Using ARIMA Model

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  • @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
Shuangyue Xiao1, Shengchi Liu1, Li Liu1,*
  • 1: Department of Information Science and Engineering Dalian Polytechnic University Dalian, P. R. China
*Contact email: link_liuli@hotmail.com

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).