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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]

Coal mine gas accident is one of the most serious threats in the process of safe coal mine mining, making it important to accurately predict coal mine gas emission. To improve the accuracy of coal mine gas emission prediction, a hybrid machine learning prediction model combining random forest (RF) algorithm, improved gray wolf optimizer (IGWO) algorithm and support vector regression (SVR) algorithm is proposed, the model prediction effect is validated by using actual measured gas emission data from a coal mine. Firstly, the RF algorithm is used to screen 13 influencing factors of coal mine gas emission, and finally 6 influencing factors are selected as the input variables of the prediction model; Secondly, the GWO algorithm is improved using the nonlinear convergence factor and DLH search strategy to obtain the IGWO algorithm; Finally, the IGWO algorithm is used to optimize the parameters of the SVR algorithm, and the RF-IGWO-SVR model is established. The results show that the mean absolute percentage error, mean absolute error and root mean square error of the RF-GWO-SVR model are 1.55%, 0.0759, and 0.1103, respectively, and this result is better than the other comparative models, which indicates that the model can effectively improve the prediction accuracy of coal mine gas emission and provide a new model for coal mine gas emission prediction. [ABSTRACT FROM AUTHOR]