Day-Ahead Forecasting of Hourly Photovoltaic Power Based on Robust Multilayer Perception

Chao Huang, Longpeng Cao, Nanxin Peng, Sijia Li, Jing Zhang, Long Wang, Xiong Luo and Jenq-Haur Wang
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Chao Huang: School of Computer and Communication Engineering, University of Science and Technology Beijing (USTB), Beijing 100083, China
Longpeng Cao: School of Computer and Communication Engineering, University of Science and Technology Beijing (USTB), Beijing 100083, China
Nanxin Peng: School of International Business, Southwestern University of Finance and Economics, Chengdu 611130, China
Sijia Li: National Internet Finance Association of China, Beijing 100080, China
Jing Zhang: School of Computer and Communication Engineering, University of Science and Technology Beijing (USTB), Beijing 100083, China
Long Wang: School of Computer and Communication Engineering, University of Science and Technology Beijing (USTB), Beijing 100083, China
Xiong Luo: School of Computer and Communication Engineering, University of Science and Technology Beijing (USTB), Beijing 100083, China
Jenq-Haur Wang: Department of Computer Science and Information Engineering, National Taipei University of Technology, Taipei City 106, Taiwan

Sustainability, 2018, vol. 10, issue 12, 1-8

Abstract: Photovoltaic (PV) modules convert renewable and sustainable solar energy into electricity. However, the uncertainty of PV power production brings challenges for the grid operation. To facilitate the management and scheduling of PV power plants, forecasting is an essential technique. In this paper, a robust multilayer perception (MLP) neural network was developed for day-ahead forecasting of hourly PV power. A generic MLP is usually trained by minimizing the mean squared loss. The mean squared error is sensitive to a few particularly large errors that can lead to a poor estimator. To tackle the problem, the pseudo-Huber loss function, which combines the best properties of squared loss and absolute loss, was adopted in this paper. The effectiveness and efficiency of the proposed method was verified by benchmarking against a generic MLP network with real PV data. Numerical experiments illustrated that the proposed method performed better than the generic MLP network in terms of root mean squared error (RMSE) and mean absolute error (MAE).

Keywords: forecasting; multilayer perception; photovoltaic; sustainable energy; pseudo-Huber loss (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:10:y:2018:i:12:p:4863-:d:191858

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