Research Article
Load Quality Analysis and Forecasting for Power Data Set on Cloud Platform
@INPROCEEDINGS{10.1007/978-3-030-99191-3_1, author={Jixiang Gan and Qi Liu and Jing Zhang}, title={Load Quality Analysis and Forecasting for Power Data Set on Cloud Platform}, proceedings={Cloud Computing. 11th EAI International Conference, CloudComp 2021, Virtual Event, December 9--10, 2021, Proceedings}, proceedings_a={CLOUDCOMP}, year={2022}, month={3}, keywords={Data quality Load forecasting Data cleaning Cloud platform}, doi={10.1007/978-3-030-99191-3_1} }- Jixiang Gan
Qi Liu
Jing Zhang
Year: 2022
Load Quality Analysis and Forecasting for Power Data Set on Cloud Platform
CLOUDCOMP
Springer
DOI: 10.1007/978-3-030-99191-3_1
Abstract
In the era of big data, The prediction management system combined with cloud computing platform can start from massive structured, semi-structured and unstructured data, which has a positive impact on improving the compliance quality analysis and prediction of power data sets. This paper focuses on the characteristics of all kinds of data sets needed in the research of power demand side business process of cloud platform at home and abroad, and analyzes, compares and summarizes all kinds of data sets. First, this paper analyzes the problems existing in various common data sets, and expounds the methods to improve the quality of data sets from two aspects of data cleaning and data preprocessing. Secondly, the LSTM prediction model and ARIMA prediction model are used to predict and analyze the collected power data to judge whether the data set has obvious defects in advance. Finally, through the experimental comparison of the two models, a more efficient prediction model is analyzed.
