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Along with the rapid increase in wind power penetration into the power grid, wind power generation predicting is becoming increasingly important to power system operators and electricity market participants. However, the random nature of the wind power would increase the uncertainty of power systems. The influencing factor is one of the most important factors in the quality of wind power prediction. In order to obtain a higher prediction accuracy, a two‐stage prediction method combined with meteorological factor and fault time is proposed. In the first stage, we present a detailed review of fault time of wind power predicting with machine‐learning methods and compare ten different models based on different influencing factors, and then, the second stage is combined with the predicted results of first stage, coupled with meteorological factors for the final wind power prediction. The results show good prediction accuracy in operation data of one wind farm from the Hu‐Nan province with different machine‐learning algorithms. © 2019 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc. [ABSTRACT FROM AUTHOR]

Abstract Conventional methods for investigating heavy metal contamination in soil are time consuming and expensive. We explored reflectance spectroscopy as an alternative method for assessing heavy metals. Four spectral transformation methods, first-order differential (FDR), second-order differential (SDR), continuum removal (CR) and continuous wavelet transform (CWT), are used for the original spectral data. Spectral preprocessing effectively eliminated the noise and baseline drifting and also highlighted the locations of the spectral feature bands. Partial least squares regression (PLSR) and radial basis function neural network (RBF) were used to study the hyperspectral inversion of four heavy metals (Cr, As, Ni, Cd). The inversion models of four heavy metals were established in the bands with the highest correlation coefficient. The inversion effects were evaluated by the coefficient of determination (R 2), root mean square error (RMSE) and residual predictive deviation (RPD) indexes. The R values of the correlation coefficient were significantly improved after smoothing and spectral transformation compared to the original waveband. The method combining continuous wavelet transform (CWT) with radial basis function neural network (RBF) had the best inversion effect on the four heavy metals. When compared to partial least squares regression (PLSR), the RMSE values were reduced by approximately 2. The CWT-RBF method can be used as a means of inversion of heavy metals in mining wasteland reclaimed land. Graphical Abstract Unlabelled Image Highlights • Spectral preprocessing, especially CWT, can effectively remove the influence of noise factors. • There is a certain correlation between spectral reflectance and soil heavy metals. • Compared with other models, the CWT-RBF model has better inversion results. • The results can provide some references for heavy metal inversion in mining wasteland. [ABSTRACT FROM AUTHOR]

To explore a rapid detection technology for detecting heavy metals in soil based on hyperspectral data, this paper took an iron mine in Daye Country, Hubei Province, as the research area, used a FieldSpec4 portable ground object spectrometer to obtain soil spectral reflectance and combine the measured data, and used three spectral transformation methods: first-order differential, second-order differential, and continuum removal. We studied the indirect hyperspectral inversion of heavy metals in reclaimed soils in the iron mine area by using three models: partial least squares regression, support vector machine, and back propagation (BP) neural network. The results show that spectral transformation can effectively highlight the position of spectral characteristic bands and improve the correlation between spectral curves and iron (Fe) element concentration. The partial least squares regression model based on first-order differential had the highest inversion accuracy for Fe element concentration in the study area, R 2 and RMSE were 0.88 and 0.53, respectively. The correlation analysis of soil elements showed that the highest correlation coefficient between Cu and Fe was 0.81. We selected the copper (Cu) element with the largest correlation coefficient with the Fe element as an example and realized the indirect prediction of soil Cu concentration using a linear regression model and BP neural network model. Among them, the model based on BP neural network is better, R 2 was 0.82, RMSE was 0.62, compared with the direct method, the model R 2 increased by about 0.2, and the root mean square error decreased by about 0.1. The effect of the indirect method was better than that of the direct method. We selected the optimum statistical interpolation method for spatial analysis of Fe and Cu concentrations in the soil of the study area and further demonstrated the feasibility of the indirect inversion method of heavy metals in the soil of iron mine areas based on hyperspectral data. These results provide a theoretical basis and new ideas for the application of near-earth sensing technology in soil and for efficient detection of heavy metals in iron ore areas. Unlabelled Image • A specific method for reclaimed soil heavy metal detection is proposed. • The FDR-PLSR model can effectively realize the hyperspectral inversion of soil iron content. • The inversion model of heavy copper based on indirect method is obviously better than that based on direct method. • The correlation of heavy metals in soils was explored. [ABSTRACT FROM AUTHOR]