Hourly solar irradiance prediction using deep BiLSTM network
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RESEARCH ARTICLE
Hourly solar irradiance prediction using deep BiLSTM network Cong Li 1,2,3
&
Yaonan Zhang 1,3 & Guohui Zhao 1,3 & Yanrun Ren 1,3
Received: 13 June 2020 / Accepted: 19 August 2020 # Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract Accurate measurement of solar irradiance is of great significance in many applications, such as climatology, energy and engineering. Deep learning models have achieved good results in solar irradiance prediction for a single site. However, most studies take meteorological parameters as the model inputs and the irradiance values as the model outputs. Because different regions have different climates, only considering the relationship between meteorological parameters and irradiance has limitations. This paper presents a novel scheme for forecasting irradiance. The method considers the hourly irradiance prediction model to be the superposition of two parts: a daily average irradiance prediction model and the irradiance amplitude prediction model. Two submodels were constructed by using deep bidirectional long short-term memory (BiLSTM) network. For the task of irradiance prediction for 25 stations located in the United States, which are located in five different climates, the proposed method performs best for 21 stations (84%) in terms of the root mean square error, 18 stations (72%) in terms of the mean absolute error, and 17 stations (68%) in terms of the coefficient of determination. Moreover, the method adopted in this study displays a stronger irradiance prediction ability than the traditional methods for 80% of the climates included in the experiment. Keywords Deep learning . Irradiance prediction . Meteorological parameters . BiLSTM . Climates
Introduction Solar irradiance plays an important role in the whole ecosystem of the earth. Variations will cause a series of changes in the surface temperature, atmospheric temperature, evaporation, water cycle, and carbon absorption in the biosphere (Ramanathan et al. 2001; Roderick and Farquhar 2002; Müller et al. 2014; Liepert et al. 2004). These changes will eventually cause significant natural environmental, social, and economic impacts. Solar irradiance is the main source of energy on the earth’s surface. It has attracted increasing attention because it is clean and renewable. According to the data of the International
Communicated by: H. Babaie * Yaonan Zhang [email protected] 1
Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China
2
College of Computer and Communication, Lanzhou University of Technology, Lanzhou 730050, China
3
University of Chinese Academy of Sciences, Beijing 100049, China
Energy Agency, global photovoltaic power generation totaled 460 TWh in 2018, which accounted for nearly 2% of the world’s total power generation. Reliable, real-time solar irradiance data are crucial to the design, operation, and performance evaluation of photovoltaic power generation systems (Ayvazogluyuksel and Filik 2018). Solar irradiance is also an
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