Research On Power Forecasting Approach For Renewable Energy Power Plant Based On Deep Learning

With the outbreak of the global energy crisis,wind energy,solar energy and other renewable energy have been rapidly developed,and renewable energy power generation has gradually shifted from supplemental energy to alternative energy.However,photovoltaic power generation is affected by external environmental factors such as weather conditions,the alternation of day and night,seasonal alternation and so on.As a result,when large-scale photovoltaic power generation is connected to the grids,it not only introduces security risks to the operation of the power system,but also brings difficulties to power grids dispatchers when they make scheduling plans for various power supplies.Therefore,accurate prediction of PV output power is still a problem worthy of further study.In this paper,the method based on deep learning is used to research power forecasting approach for renewable energy power plant.Firstly,a clustering algorithm based on irradiance index is proposed in this paper after analyzing the characteristics of PV plant daily power curve.Then,the LongShort-Term Memory(LSTM)is employed to build forecasting models for different types of weather.Meanwhile,according to the prediction results of the model,the probability distribution of prediction errors is studied to achieve the interval prediction of photovoltaic power.The main work and contributions of this paper are as follows:?A clustering algorithm based on irradiance index is proposed in this paper,and it has been proved that this clustering method can improve the accuracy of prediction.?The LSTM is used to construct forecasting models.The recurrent neural networks not only model the nonlinfear relationship between the input features of samples and the output power generation,but also capture the dependencies among multi-variable time series.It effectively improves the prediction performance.?The probability distribution of the prediction errors for the photovoltaic power forecasting model is analyzed.Firstly,we assume that the errors follow the Laplacian distribution,then the maximum likelihood estimation is applied to estimate the parameters of Laplacian distribution.Finally,the prediction interval of the power generation for test set is obtained by these parameters.