Open AccessShort-term Photovoltaic Power Forecast Based on Greenhouse Energy Supply Requirements
Author(s) -
Jianjun Li,
Songhuai Du
Publication year - 2020
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.179
H-Index - 26
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/598/1/012105
Subject(s) - photovoltaic system , greenhouse , power (physics) , environmental science , term (time) , computer science , automotive engineering , stability (learning theory) , electricity generation , reliability engineering , engineering , electrical engineering , physics , quantum mechanics , horticulture , biology , machine learning
In order to solve the problem of “disposal of light” in distributed photovoltaic power plants,this paper proposed that the energy supply of photovoltaic power plants could achieve local consumption in agricultural greenhouses, and predict the photovoltaic power generation in advance according to the load demand of greenhouses. The historical power data and meteorological data of the input variables were normalized, and the PV power prediction was performed at a time interval of 1 h for a time comparison. The prediction method based on the iterative improved BP neural network model was proposed. The prediction results showed that the prediction error was not More than 2%,could meet the energy supply stability requirements of greenhouse loads.