<abstract xmlns="http://www.w3.org/1999/xhtml"><p>Photovoltaic power system is taking a significant percentage of power system and the demands for accurate forecasting of the power outputs is surging. In prior works, the forecasting problem was formulated as a regression problem, however, which most cannot guarantee that the forecasted outputs is nonnegative. To solve this problem, we proposed a novel probabilistic model by using nonlinear regression and Bayesian learning method. In the paper, we present the detailed theoretical derivations and interpretations. The simulation results show the validity and feasibility of the proposed algorithm by comparing with the traditional SVM algorithm.</p></abstract>

Photovoltaic power system is taking a significant percentage of power system and the demands for accurate forecasting of the power outputs is surging. In prior works, the forecasting problem was formulated as a regression problem, however, which most cannot guarantee that the forecasted outputs is nonnegative. To solve this problem, we proposed a novel probabilistic model by using nonlinear regression and Bayesian learning method. In the paper, we present the detailed theoretical derivations and interpretations. The simulation results show the validity and feasibility of the proposed algorithm by comparing with the traditional SVM algorithm.

An Bayesian Learning and Nonlinear Regression Model for Photovoltaic Power Output Forecasting

Key Laboratory of Advanced Perception and Intelligent Control of High-end Equipment, Ministry of Education

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

Photovoltaic power system is taking a significant percentage of power system and the demands for accurate forecasting of the power outputs is surging. In prior...

Situations	Sunny	Sunny/Cloudy	Rainy/Cloudy
RMSE of PR-SBL	1.145	11.861	8.343
RMSE of SVM	22.290	22.281	18.715

An Bayesian Learning and Nonlinear Regression Model for Photovoltaic Power Output Forecasting

Published Online: Sep 15, 2020

Page range: 531 - 542

Received: Feb 24, 2020

Accepted: May 26, 2020

DOI: https://doi.org/10.2478/amns.2020.2.00032

Keywords
Bayesian Learning, Power Output Forecasting, Data-based Regression, Probabilistic Model

© 2020 Wengen Gao et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Fig. 1

Fig. 2

Fig. 3

Fig. 4

Fig. 5

The detailed RMSE of three different situations in two regression methods

Poisson Kernel Regression Based Sparse Bayesian Learning

An Bayesian Learning and Nonlinear Regression Model for Photovoltaic Power Output Forecasting

Published Online: Sep 15, 2020

Page range: 531 - 542

Received: Feb 24, 2020

Accepted: May 26, 2020

DOI: https://doi.org/10.2478/amns.2020.2.00032

KeywordsBayesian Learning, Power Output Forecasting, Data-based Regression, Probabilistic Model

© 2020 Wengen Gao et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Fig. 1

Fig. 2

Fig. 3

Fig. 4

Fig. 5

The detailed RMSE of three different situations in two regression methods

Poisson Kernel Regression Based Sparse Bayesian Learning

Keywords
Bayesian Learning, Power Output Forecasting, Data-based Regression, Probabilistic Model