Hierarchically non-continuous regression prediction for short-term photovoltaic power output

Siya Yao, Le Pan, Zibo Yu, Qi Kang, Mengchu Zhou

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

Photovoltaic (PV) power generation utilizing clean solar energy is increasingly conducive to relieving energy crisis and environmental pollution. Affected by the fluctuation and uncertainty of meteorological factors, short-term PV power is volatile in nature, posing threats to power supply reliability and stability. Consequently, accurate PV production forecasting plays a vital role in steadily running and managing a power system. However, due to the intrinsic characteristics of variability and fluctuation in PV data, it is challenging to get acceptable output prediction via conventional regression methods. Moreover, the raw data in our regression task originates from a PV plant whose stored PV values are hierarchically non-continuous with conspicuously diverse classes, making it even harder to conduct precise prediction. In this paper, we propose a tree-based prediction model based on the XGBoost regression algorithm. The experimental results show that the proposed prediction model achieves the highest average forecasting accuracy and stable generalization performance, indicating its validity for hierarchically non-continuous short-term PV output prediction.

Original languageEnglish (US)
Title of host publicationProceedings of the 2019 IEEE 16th International Conference on Networking, Sensing and Control, ICNSC 2019
EditorsHaibin Zhu, Jiacun Wang, MengChu Zhou
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages379-384
Number of pages6
ISBN (Electronic)9781728100838
DOIs
StatePublished - May 2019
Event16th IEEE International Conference on Networking, Sensing and Control, ICNSC 2019 - Banff, Canada
Duration: May 9 2019May 11 2019

Publication series

NameProceedings of the 2019 IEEE 16th International Conference on Networking, Sensing and Control, ICNSC 2019

Conference

Conference16th IEEE International Conference on Networking, Sensing and Control, ICNSC 2019
CountryCanada
CityBanff
Period5/9/195/11/19

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Networks and Communications
  • Control and Optimization
  • Instrumentation

Keywords

  • Hierarchical non-continuous regression
  • Outlier detection
  • Short-term photovoltaic (PV) power forecasting

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