Research on Prediction of Dynamic Load Complementarity Based on Big Data

To solve the problem of unstable prediction results of traditional power load forecasting methods, a power dynamic load complementary coefficient forecasting method based on big data technology is proposed. The prediction method is mainly divided into the following four parts. The data caliber and d...

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Bibliographic Details
Published in2019 International Conference on Computer Network, Electronic and Automation (ICCNEA) pp. 368 - 373
Main Authors Wang, Zhenghao, Qin, Jingjing, Cao, Chunlong
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.09.2019
Subjects
Big Data
Complementarity
Data models
Dynamic
Load forecasting
Load modeling
Power system stability
Prediction
Predictive models
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Summary:To solve the problem of unstable prediction results of traditional power load forecasting methods, a power dynamic load complementary coefficient forecasting method based on big data technology is proposed. The prediction method is mainly divided into the following four parts. The data caliber and density are collected to determine the reasonable data range. Then, according to the big data technology, the curve function of charge movement is calculated to calculate the complementary coefficient, and the complementary coefficient is combined to complete the prediction. The experimental results show that the power dynamic load complementarity coefficient prediction method based on big data technology proposed for the traditional charge prediction method is more stable, so this method is more effective.
DOI:10.1109/ICCNEA.2019.00074