Prediction of photovoltaic power plant output and related carbon reduction based on SSA-BP neural network with pattern recognition
This paper proposes a method to predict the photovoltaic output based on weather state pattern recognition and SSA-BP,which is more accurate than traditional single models under different weather conditions.Firstly,the historical data was cleaned using the 3sigma algorithm to obtain the data that can reflect the output of photovoltaic power plants and the regularity of weather changes.Then,based on the analysis of the parameters such as irradiance,temperature,and wind speed,Gaussian mixture models were applied to classify the professional weather types and three typical generalized weather types were obtained.Furthermore,the data was used as SSA-BP neural network input to predict the futuristic photovoltaic power plant output.Finally,the carbon accounting method was used to calculate the carbon emission reduction of the photovoltaic power generation project.The experimental results show that through classification recognition and the optimized SSA-BP neural network,the mean relative errors in the prediction for the three weather types are 0.195,0.243 and 0.310,respectively.Compared with other predication models,the relative errors are reduced by 17.8%～66.7%.In addition,the relative error between the predicted carbon dioxide emission reduction and actual value is only 3.37%.The model proposed in this work shows satisfactory prediction results.
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