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Accurate and reliable ultra-short-term wind power forecasting (WPF) is of great significance to the safe and stable operation of power systems, but the current research is difficult to balance the prediction accuracy, timeliness, and applicability at the same time. Therefore, this paper proposes a ultra-short-term WPF model based on feature weight analysis and cluster dynamic division. The model introduces an analytic hierarchy process and an entropy weight method to analyze the subjective and objective weight of the influencing features of wind power, respectively, then the subjective and objective weight ratio is determined by the quantum particle swarm optimization (QPSO) algorithm to obtain a more reasonable comprehensive weight of each feature. On this basis, it uses the K-Medoids algorithm to dynamically divide the wind power clusters into class regions by cycles. Then, the class region is used as the prediction unit to establish the TCN-BiLSTM model based on temporal convolutional networks (TCN) and bi-directional long short-term memory (BiLSTM) for training and prediction and optimizes the hyper-parameters of the model by the QPSO algorithm. Finally, the regional predictions are summed to obtain the final ultra-short-term power prediction. In addition, in order to verify the performance of the model, the actual operation data of a power field in Xinjiang, China, are selected for the example validation. The results show that the proposed model can ensure the prediction accuracy while minimizing the training time of the model and outperforms other existing methods in terms of prediction accuracy, timeliness, and applicability. [ABSTRACT FROM AUTHOR]

In order to further improve the accuracy of photovoltaic (PV) power prediction and the stability of power system, a short-term PV power prediction model based on hierarchical clustering of K-means++ algorithm and deep learning hybrid model is proposed in this paper. First, hierarchical clustering of the K-means++ algorithm is used to cluster historical data into different weather scenes according to different seasons. Second, a hybrid model combining convolutional neural network (CNN), squeeze-and-excitation attention mechanism (SEAM), and bidirectional long short-term memory (BILSTM) neural network is constructed to capture long-term dependencies in time series, and the improved pelican optimization algorithm (IPOA) is used to optimize the hyperparameters in the prediction model. Finally, an example for modeling analysis is conducted by using the actual output and meteorological data of a PV power station in the Ili region of Xinjiang, China. The effectiveness and accuracy of the proposed model are verified by comparing with LSTM, BILSTM, CNN-BILSTM, and POA-CNN-SEAM-BILSTM models, and the superiority of IPOA is verified by comparing with particle swarm optimization and whale optimization algorithm. The results show that the proposed model can obtain better results under different weather scenes in different seasons, and the prediction accuracy of the model optimized by IPOA is further improved. [ABSTRACT FROM AUTHOR]

Power stations operating on PhotoVoltaic (PV) power generation are challenged by demand forecasting as PV power generation is random and intermittent. This paper presents an Efficient Shrinkage Temporal Convolutional Network (ESTCN) model, which combines the Temporal Convolutional Network (TCN) and an improved Deep Residual Shrinkage Network (DRSN) to forecast PV power output. First, the attention sub-network of the DRSN is improved, the fully connected layer of the sub-network is canceled, and feature extraction is done directly on the model features following global average pooling using a one-dimensional convolution to obtain cross-channel interaction and increased model efficiency. Next, an improved attention mechanism and adaptive soft thresholding are introduced into TCN to automatically determine the noise threshold to address the issue of information weight dispersion caused by redundant information in the input samples. By incorporating dilated causal convolution, attention module, soft thresholding, and residual connection, the ESTCN model is formed and is shown to enhance PV power output prediction with only a minimal increase in the number of parameters. The power station in Ningxia, China is adopted as a validation example with data taken from the year 2020. The fitting and prediction results of the ESTCN model are compared against the Convolutional Neural Network, Long Short-Term Memory, and TCN models. The ESTCN model yielded the following values of the evaluation metrics: Root Mean Square Error (RMSE) of 1.47 kW, Mean Absolute Error (MAE) of 0.79 kW, and coefficient of determination of 0.999. Compared to the TCN model, the prediction errors based on RMSE and MAE improved by 0.39 kW and 0.18 kW, respectively. Applying the ESTCN model to predict PV power generation of power stations in two regions in China, Ningxia and Xinjiang, shows the ESTCN model to be superior, scalable, and universal over other deep learning models. • Study the problem of photovoltaic power using a deep learning method. • A new Efficient Shrinkage Temporal Convolutional Network model is presented. • Attention sub-network of DRSN is improved by cancelling the fully connected layer. • Feature extraction is done on the model features using a one-dimensional convolution. • Improved attention mechanism and adaptive soft thresholding are introduced into TCN. [ABSTRACT FROM AUTHOR]