The volatility and uncertainty of wind power often affect the quality of electric energy, the security of the power grid, the stability of the power system, and the fluctuation of the power market. In this case, the research on wind power forecasting is of great significance for ensuring the better development of wind power grids and the higher quality of electric energy. Therefore, a lot of new forecasting methods have been put forward. In this paper, a new forecasting model based on a convolution neural network and LightGBM is constructed. The procedure is shown as follows. First, we construct new feature sets by analyzing the characteristics of the raw data on the time series from the wind field and adjacent wind field. Second, the convolutional neural network (CNN) is proposed to extract information from input data, and the network parameters are adjusted by comparing the actual results. Third, in consideration of the limitations of the single-convolution model in predicting wind power, we innovatively integrated the LightGBM classification algorithm at the model to improve the forecasting accuracy and robustness. Finally, compared with the existing support vector machines, LightGBM, and CNN, the fusion model has better performance in accuracy and efficiency. INDEX TERMS Convolutional neural network, fusion model, LightGBM, ultra-short-term wind power forecasting, wind energy.

Land use change models are increasingly being used to evaluate the effect of land change on climate and biodiversity and to generate scenarios of deforestation. Although many methods are available to model land transition potentials, they are usually not user-friendly and require the specification of many parameters, making the task difficult for decision makers not familiar with the tools, as well as making the process difficult to interpret. In this article we propose a simple method for modeling transition potentials. SimWeight is an instance-based learning algorithm based on the logic of the K-Nearest Neighbor algorithm. The method identifies the relevance of each driver variable and predicts the transition potential of locations given known instances of change. A case study was used to demonstrate and validate the method. Comparison of results with the Multi-Layer Perceptron neural network (MLP) suggests that SimWeight performs similarly in its capacity to predict transition potentials, without the need for complex parameters. Another advantage of SimWeight is that it is amenable to parallelization for deployment on a cloud computing platform.t gis_1226 569..580 relationship between land change and driver/suitability variables to evaluate the readiness of the land for transition from one land cover to another (generation of transition potentials), the determination of the amount of land that will transition (demand of land) and finally, the allocation of land to the new land cover types (land allocation). The evaluation of transition potentials is a critical step in the process of land change modeling and prediction, as the final allocation of land is based upon them. Therefore the production of transition potentials with high accuracy is essential in the process of land change prediction. Although many methods exist for the generation of transition potentials, to our knowledge, only Eastman et al. (2005) have evaluated an extensive list of them. In their work they evaluated (among others) the Weights of Evidence, which is used in DINAMICA (Soares-Filho et al. 2002); empirical probabilities, which is used in GEOMOD (Pontius et al. 2001); logistic regression, which is used, for example, in CLUE-S (Verburg et al. 1999) and the Multi-Layer Perceptron neural network, which is used in LTM (Pijanowski et al. 2002) and LCM (Eastman 2009). The main conclusion of that work is that there are large differences in the patterns of transition potentials generated by the different methods, and therefore they are not interchangeable. Moreover, in their tests, they found that the Multi-Layer Perceptron (MLP) performed best.Non-parametric methods such as the Multi-Layer Perceptron are appealing for the generation of transition potentials since they can fit complex relationships, and they are capable of producing accurate results. However, in order to train the neural networks for the determination of transition potentials, many parameters need to be adjusted, which makes the model difficult to run and complex to understand....

The self-assembly of 3,10-dibromo-perylo­[1,12-b,c,d]­thiophene on Ag(111) leads to three types of ordered porous networks: honeycomb PN1, Kagome PN2, and hybrid PN3. Detailed experimental and theoretical analyses confirm the thermal stability order of the three constructed porous networks. High-resolution scanning tunneling microscopy images indicate the importance of two σ-hole interactions of Br···S and Br···Br in steering two-dimensional molecular assembly on metal surfaces.

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