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Accurate regional distributed PV power forecasting provides data support for power grid management and optimal operation. Distributed PV has the characteristics of large quantity, small capacity and difficulty in obtaining meteorological data. Statistical upscaling method is commonly used to forecast regional power. However, the current research ignores how to reasonably divide the sub-regions with similar output characteristics and mine the spatial and temporal correlation between different sub-regions. Therefore, this paper proposes a short-term regional distributed PV power forecasting method based on sub-region division considering spatio-temporal correlation. Firstly, the representative power plant is selected after dividing the sub-region by the AP clustering algorithm. Then, the GCN is used to extract spatial correlation features, and the LSTM is used to extract the evolution features of dynamic spatial correlation features, and the power forecasting models of representative plants in different weather types are established. Finally, the data integrity and similarity of the sub-region are scored, and the upscaling weight is determined to realize the power forecasting of the whole region. The distributed PV power generation data of Pingshan County, Hebei Province, China is used for simulation test. The results show that the forecasting method proposed has higher forecasting accuracy than the traditional model. • The spatio-temporal correlation between distributed PV power plants is studied. • Introduce how to divide appropriate distributed PV sub-regions. • A power forecasting method considering spatio-temporal correlation is proposed. • Sub-regional data evaluation improves the forecasting accuracy of regional PV. [ABSTRACT FROM AUTHOR]

-Probabilistic wind power forecasting includes more detailed information than deterministic forecasting, which can provide reliable guidance for the optimal decisions of power system scheduling operation. However, there are certain laws in the magnitude and direction of the forecasting errors corresponding to different power series fluctuations, which leads to different predictability and forecasting accuracy of different power fluctuation patterns. As most studies still focused on the model algorithm improvement and pay less attention to the law of power data itself, this paper proposes a novel probabilistic forecasting method based on the swinging door algorithm (SDA), fuzzy c means (FCM) clustering method, long short-term memory (LSTM) neural network, and nonparametric kernel density estimation (KDE), considering the correlation between wind power fluctuation patterns and forecasting errors. SDA and FCM are used to assign appropriate pattern labels to the power fluctuations, and then LSTM and KDE are used to introduce pattern recognition results in probabilistic forecasting models, excavating the inherent law of the data for classification modeling. Simulation shows that the proposed model can adapt to different error distribution patterns, and the models introduced fluctuation pattern recognition can improve the skill score of probabilistic forecasting by 36.50% on average than those without pattern recognition. • Wind power fluctuation pattern recognition relies on fluctuation characteristics. • Model optimization is achieved through pattern forecasting and pattern search. • Forecasting error distribution affects the establishment of prediction intervals. • Pattern recognition can improve deterministic and probabilistic prediction accuracy. [ABSTRACT FROM AUTHOR]

Wind power has received extensive attention due to its superiorities of clean and pollution-free. However, because of the randomness and volatility of wind power, accurate power prediction is needed to help its consumption. Wind speed is the key to wind power prediction, but traditional prediction method cannot accurately grasp the wind speed variation trend and the traditional wind process partition method has some defects, an ultra-short-term wind speed hybrid prediction method based on wind process pattern forecasting is proposed in this paper. Firstly, a wind process (WP) division method considering the influence of wind speed on the operation state, mode switch and output power of wind turbine is proposed. Secondly, according to the operating characteristics of the wind turbine, all the WPs is classified into different wind process patterns (WPP), and the effectiveness of the classification is verified. Then, the Adaboost algorithm is used to forecast the WPP of the next 4 h. Finally, the wind speed hybrid prediction model of each pattern is established, the corresponding model is automatically selected based on WPP to predict the wind speed. Simulation results show that the proposed model can reliably forecast future WPP and the prediction accuracy is better than conventional models. • Wind fluctuation process is divided considering the key values of wind speed. • Wind process show varies fluctuation patterns according to operating characteristic. • Adaboost classification algorithm is applied to predict future wind process pattern. • Targeted modeling for specific wind process pattern can improve prediction accuracy. [ABSTRACT FROM AUTHOR]

Accurate ultra-short-term PV power forecasting is essential for the power system with a high proportion of renewable energy integration, which can provide power fluctuation information hours ahead and help to mitigate the interference of the random PV power output. Most of the PV power forecasting methods mainly focus on employing local ground-based observation data, ignoring the spatial and temporal distribution and correlation characteristics of solar energy and meteorological impact factors. Therefore, a novel ultra-short-term PV power forecasting method based on the satellite image data is proposed in this paper, which combines the spatio-temporal correlation between multiple plants with power and cloud information. The associated neighboring plant is first selected by spatial-temporal cross-correlation analysis. Then the global distribution information of the cloud is extracted from satellite images as additional inputs with other general meteorological and power inputs to train the forecasting model. The proposed method is compared with several benchmark methods without considering the information of neighboring plants. Results show that the proposed method outperforms the benchmark methods and achieves a higher accuracy at 4.73%, 10.54%, and 4.88%, 11.04% for two target PV plants on a four-month validation dataset, in terms of root mean squared error and mean absolute error value, respectively. • The spatio-temporal correlation between PV power plants is investigated. • Introduce how to select an appropriate neighboring plant. • Verify the mapping relationship between cloud characteristics of neighboring plants and the power of target plants. • An ultra-short-term PV power forecasting method based on cloud information from neighboring plant is proposed. [ABSTRACT FROM AUTHOR]