A Green Energy Application in Energy Management Systems by an Artificial Intelligence-Based Solar Radiation Forecasting Model

Ping-Huan Kuo and Chiou-Jye Huang
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Ping-Huan Kuo: Computer and Intelligent Robot Program for Bachelor Degree, National Pingtung University, No.4-18, Minsheng Rd., Pingtung City, Pingtung County 90003, Taiwan
Chiou-Jye Huang: School of Electrical Engineering and Automation, Jiangxi University of Science and Technology, No.86, Hongqi Rd., Zhanggong District, Ganzhou 341000, China

Energies, 2018, vol. 11, issue 4, 1-15

Abstract: The photovoltaic (PV) systems generate green energy from the sunlight without any pollution or noise. The PV systems are simple, convenient to install, and seldom malfunction. Unfortunately, the energy generated by PV systems depends on climatic conditions, location, and system design. The solar radiation forecasting is important to the smooth operation of PV systems. However, solar radiation detected by a pyranometer sensor is strongly nonlinear and highly unstable. The PV energy generation makes a considerable contribution to the smart grids via a large number of relatively small PV systems. In this paper, a high-precision deep convolutional neural network model (SolarNet) is proposed to facilitate the solar radiation forecasting. The proposed model is verified by experiments. The experimental results demonstrate that SolarNet outperforms other benchmark models in forecasting accuracy as well as in predicting complex time series with a high degree of volatility and irregularity.

Keywords: green energy; energy technology; artificial intelligence; solar energy; solar radiation; forecasting; deep convolutional neural networks (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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