Advanced Hybrid Information Processing. Second EAI International Conference, ADHIP 2018, Yiyang, China, October 5-6, 2018, Proceedings

Research Article

Research on Parallel Forecasting Model of Short-Term Power Load Big Data

Download
119 downloads
  • @INPROCEEDINGS{10.1007/978-3-030-19086-6_14,
        author={Xin-jia Li and Hong Sun and Cheng-liang Wang and Si-yu Tao and Tao Lei},
        title={Research on Parallel Forecasting Model of Short-Term Power Load Big Data},
        proceedings={Advanced Hybrid Information Processing. Second EAI International Conference, ADHIP 2018, Yiyang, China, October 5-6, 2018, Proceedings},
        proceedings_a={ADHIP},
        year={2019},
        month={5},
        keywords={Short-Term load forecasting Big data Electrical load Prediction algorithm},
        doi={10.1007/978-3-030-19086-6_14}
    }
    
  • Xin-jia Li
    Hong Sun
    Cheng-liang Wang
    Si-yu Tao
    Tao Lei
    Year: 2019
    Research on Parallel Forecasting Model of Short-Term Power Load Big Data
    ADHIP
    Springer
    DOI: 10.1007/978-3-030-19086-6_14
Xin-jia Li1,*, Hong Sun1, Cheng-liang Wang1, Si-yu Tao2, Tao Lei3
  • 1: Jiangsu Fangtian Power Technology Co., Ltd.
  • 2: Southeast University
  • 3: South China Normal University
*Contact email: rending0620@163.com

Abstract

The parallel prediction model of big data with traditional power load has a low prediction accuracy in different working conditions, so the parallel prediction model of big data for short-term power load is designed. The short-term power load forecasting theory is analyzed, and the short-term power load data are classified to select the short-term power load forecasting theory. The Map/Reduce framework is built on the basis of the theory, and the prediction process is designed through the Map/Reduce framework. The short-term power load data of the subnet and the big data of the short term power load are predicted respectively, and the construction of the parallel prediction model of the short-term power load big data is realized. The experimental results show that the proposed big data parallel prediction model is better than the traditional model, and can be switched under different working conditions, and the deviation between the forecasting curve and the actual load is small, the average deviation is 1.7, and the overall prediction effect is good.