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نتیجه جستجو - شبکه عصبی دندریتیک

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ردیف عنوان نوع
1 A photovoltaic power forecasting model based on dendritic neuron networks with the aid of wavelet transform
یک مدل پیش بینی قدرت فتوولتائیک مبتنی بر شبکه های عصبی دندریتیک با کمک تبدیل موجک-2020
The ever increasing proportion of photovoltaic (PV), which is, in effect, a random and intermittent energy source, makes PV power forecasting increasingly important for power grid stability. Artificial neural net- works (ANN) have become one of the commonly utilized methods in PV power prediction. Since there is no ideal theoretical guidance as yet on the determination of the number of hidden layers and hidden units, there are always abundant neurons in traditional neural networks in order to learn as many data characteristics as possible, which often results in overfitting and high computational costs. The dendritic model proposed in recent years has the characteristics of simple structure, fast convergence and better fitting ability. This paper proposes a PV power forecasting model based on the dendritic neuron networks, which seeks to improve the computational efficiency and prediction accuracy. In order to better extract characteristics of different frequencies of the input data, the approach introduces a wavelet transform. Firstly, the data is decomposed into high-frequency and low-frequency components via a wavelet trans- form. Thereafter, the input data of different frequencies obtained by the decomposition are transmitted respectively to different sub-models. Finally, the results of sub-models are reconstructed to obtain the final output. The proposed PV power forecasting model was tested upon actual photovoltaic datasets. Results obtained through simulation demonstrate significant improvement in terms of accuracy and efficiency.
Keywords: Photovoltaic power | Very-short-term forecasting | Dendritic neural network | Wavelet transform
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