Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Short-Term Photovoltaic Power Forecasting Based on a Feature Rise-Dimensional Two-Layer Ensemble Learning Model

Hui Wang
ORCID logo ,
Su Yan
,
Danyang Ju
,
Nan Ma
,
Jun Fang
,
Song Wang
,
Haijun Li
,
Tianyu Zhang
,
Yipeng Xie
,
Jun Wang
*
Version 1 : Received: 21 September 2023 / Approved: 22 September 2023 / Online: 22 September 2023 (11:37:45 CEST)

A peer-reviewed article of this Preprint also exists.

Wang, H.; Yan, S.; Ju, D.; Ma, N.; Fang, J.; Wang, S.; Li, H.; Zhang, T.; Xie, Y.; Wang, J. Short-Term Photovoltaic Power Forecasting Based on a Feature Rise-Dimensional Two-Layer Ensemble Learning Model. Sustainability 2023, 15, 15594. Wang, H.; Yan, S.; Ju, D.; Ma, N.; Fang, J.; Wang, S.; Li, H.; Zhang, T.; Xie, Y.; Wang, J. Short-Term Photovoltaic Power Forecasting Based on a Feature Rise-Dimensional Two-Layer Ensemble Learning Model. Sustainability 2023, 15, 15594.

Abstract

Due to the intermittency and fluctuation of photovoltaic (PV) output power, a high proportion of grid-connected PV power generation systems has a significant impact on power systems. Accurate PV power forecasting can alleviate the uncertainty of the PV power and is of great significance for the stable operation and scheduling of the power systems. Therefore, in this study, a feature rise-dimensional (FRD) two-layer ensemble learning (TLEL) model for short-term PV power deterministic forecasting and probability forecasting is proposed. First， based on the eXtreme Gradient Boosting (XGBoost), Random Forest (RF), CatBoost, and Long-short-term memory (LSTM) models, a TLEL model is constructed utilizing the ensemble learning algorithm. Meanwhile, the FRD method is introduced to construct the FRD-XGBoost-LSTM (R-XGBL), FRD-RF-LSTM(R-RFL), and FRD- CatBoost - LSTM (R-CatBL) models. Subsequently, the above models are combined to construct the FRD-TLEL model for deterministic forecasting, and perform probability interval forecasting based on quantile regression(QR). Finally, the performance of the proposed model is demonstrated with a real-world dataset. By comparing with other models, the proposed model displays better forecasting accuracy for deterministic forecasting and reliable forecasting intervals for probability forecasting, and good generalization ability in the datasets of different seasons and weather types.

Keywords

photovoltaic power forecasting; deterministic forecasting; probability interval forecasting; ensemble learning; feature rise-dimensional method

Subject

Computer Science and Mathematics, Artificial Intelligence and Machine Learning

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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