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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]

Solar irradiance data include temporal information and geospatial information, so solar irradiance prediction can be regarded as a spatiotemporal sequence prediction problem. However, at present, most of the research is based on time series prediction models, and the research studies on spatial-temporal series prediction models are relatively few. Therefore, it is necessary to integrate spatial-temporal information to construct a spatial-temporal sequence prediction model for research. In this paper, the spatial-temporal prediction model based on graph convolutional network (GCN) and long short-term memory network (LSTM) was established for short-term solar irradiance prediction. In this model, solar radiation observatories were modeled as undirected graphs, where each node corresponds to an observatory, and a GCN was used to capture spatial correlations between sites. For each node, temporal features were extracted by using a LSTM. In order to evaluate the prediction performance of this model, six solar radiation observatories located in the Xinjiang region of China were selected; together with widely used persistence model seasonal autoregressive integrated moving average and data-driven prediction models such as convolutional neural network, recurrent neural network, and LSTM, comparisons were made under different seasons and weather conditions. The experimental results show that the average root mean square error of the GCN-LSTM model at the six sites is 62.058 W / m 2 , which is reduced by 9.8%, 14.3%, 6.9%, and 3.3%, respectively, compared with other models; the average MAE is 25.376 W / m 2 , which is reduced by 27.7%, 26.5%, 20.1%, and 11%, respectively, compared with other models; the average R2 is 0.943, which is improved by 1.4%, 2.2%, 0.8%, and 0.4%, respectively, compared with other models. [ABSTRACT FROM AUTHOR]

Accurate wind speed forecast plays an important role in the safe and stable operation of large-scale wind power integrated grid system. In this paper, a new hybrid model for short-term wind speed forecasting based on hyperparameter optimization and error correction is proposed, where the forecasting period is 5, 10, and 15 min, respectively, for three sites. The empirical wavelet transform is used to decompose the original wind speed series. Then, the Elman neural network and kernel extreme learning machine, which adopt Bayesian optimization algorithm for hyperparameter optimization, are used as predictors for wind speed prediction and error processing, respectively. In addition, a new error correction model using wind speed as model input is proposed. In order to verify the performance of the proposed model, three datasets collected from different real-world wind farms in Gansu and Xinjiang were considered as a case study to comprehensively evaluate the prediction performance of ten forecasting models. The results reveal that the proposed model has higher prediction accuracy and better prediction performance than the contrast models. [ABSTRACT FROM AUTHOR]