Day-Ahead Forecasting of Hourly Photovoltaic Power Based on Robust Multilayer Perception
School of Computer and Communication Engineering, University of Science and Technology Beijing (USTB), Beijing 100083, China
Key Laboratory of Wind Energy and Solar Energy Technology (Inner Mongolia University of Technology), Ministry of Education, Hohhot 010051, China
Beijing Key Laboratory of Knowledge Engineering for Materials Science, Beijing 100083, China
School of International Business, Southwestern University of Finance and Economics, Chengdu 611130, China
National Internet Finance Association of China, Beijing 100080, China
Department of Computer Science and Information Engineering, National Taipei University of Technology, Taipei City 106, Taiwan
Author to whom correspondence should be addressed.
Sustainability 2018, 10(12), 4863; https://doi.org/10.3390/su10124863
Received: 13 November 2018 / Revised: 11 December 2018 / Accepted: 14 December 2018 / Published: 19 December 2018
(This article belongs to the Special Issue Sustainable Energy Systems: From Primary to End-Use)
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).