Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Long term rolling prediction model for solar radiation combining empirical mode decomposition (EMD) and artificial neural network (ANN) techniques
Long-term prediction of solar radiation intensity plays an important role in the planning and design of photovoltaic power stations. Unlike previous research on solar radiation prediction requiring various meteorological and topographic data, this study proposed a rolling prediction model combining Empirical Mode Decomposition (EMD) and Artificial Neural Network (ANN) techniques with the need for historical solar radiation data only. To overcome the inconsistency problem of the number of intrinsic mode functions derived from the EMD, they are classified into high-frequency term, low-frequency term, and trend item, which are taken as the input parameters of the ANN model. With the historical data one-year after as the output parameter, the ANN model implies the complex, non-linear relationship between the adjacent periods, and it can be used to predict long-term solar radiation. The proposed methodology is applied to Gonghe county in the Qinghai province of China, where a large-scale photovoltaic power plant is under planning. The results indicate that the correlation coefficients between the daily and monthly predicted value and the historical data are 0.698 and 0.930, respectively, which are comparable to previous studies with a greater data requirement and a simpler model.
Long term rolling prediction model for solar radiation combining empirical mode decomposition (EMD) and artificial neural network (ANN) techniques
Long-term prediction of solar radiation intensity plays an important role in the planning and design of photovoltaic power stations. Unlike previous research on solar radiation prediction requiring various meteorological and topographic data, this study proposed a rolling prediction model combining Empirical Mode Decomposition (EMD) and Artificial Neural Network (ANN) techniques with the need for historical solar radiation data only. To overcome the inconsistency problem of the number of intrinsic mode functions derived from the EMD, they are classified into high-frequency term, low-frequency term, and trend item, which are taken as the input parameters of the ANN model. With the historical data one-year after as the output parameter, the ANN model implies the complex, non-linear relationship between the adjacent periods, and it can be used to predict long-term solar radiation. The proposed methodology is applied to Gonghe county in the Qinghai province of China, where a large-scale photovoltaic power plant is under planning. The results indicate that the correlation coefficients between the daily and monthly predicted value and the historical data are 0.698 and 0.930, respectively, which are comparable to previous studies with a greater data requirement and a simpler model.
Long term rolling prediction model for solar radiation combining empirical mode decomposition (EMD) and artificial neural network (ANN) techniques
Li, Fang-Fang (Autor:in) / Wang, Si-Ya (Autor:in) / Wei, Jia-Hua (Autor:in)
01.01.2018
14 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
Artificial neural network based prediction model of daily global solar radiation in Morocco
| American Institute of Physics | 2013