Power Output Prediction of Photovoltaic Plant Based on Big Data

DOI

10.2991/icaemt-15.2015.120How to use a DOI?

Keywords

big data, smart grid, PLSR, PV plant, output forecast

Abstract

The development of smart grid has a higher requirement for the real-time data acquisition, processing and association analysis of mass data, big data is one of the core technologies. Accurate prediction of the output of PV (Photovoltaic) plant is of great significance to the safety and stability of power system. The output of PV plant is influenced by the light intensity, temperature and humidity, and there is a complex relationship between factors, there will be a large error when this prediction using LSR(Least Square Regression) analysis. On the basis of electric power data platform, this paper proposes a method for predicting the output of PV plant based on PLSR(Partial Least Squares Regression), this method considers parallel advantage of MapReduce, focuses on the independence of concurrent events, can meet the coverage requirements of power big data attribute dimension and reduction. Through the experimental of data simulation prove that this method is feasible in the treatment of big power data attribute reduction.

Copyright

© 2015, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Bin Zhang
AU  - Chi Zhang
AU  - Wenqing Zhao
AU  - Gang Li
PY  - 2015/08
DA  - 2015/08
TI  - Power Output Prediction of Photovoltaic Plant Based on Big Data
BT  - Proceedings of the 2015 International Conference on Advanced Engineering Materials and Technology
PB  - Atlantis Press
SP  - 620
EP  - 624
SN  - 2352-5401
UR  - https://doi.org/10.2991/icaemt-15.2015.120
DO  - 10.2991/icaemt-15.2015.120
ID  - Zhang2015/08
ER  -