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The electricity consumption forecasting is a challenging task, because the predictive accuracy is easily affected by multiple external factors, such as society, economics, environment, as well as the renewable energy, including hydro power, wind power and solar power. Particularly, in the smart grid with large amount of data, how to extract valuable information of those external factors timely is the key to the success of electricity consumption forecasting. A method of probability density forecasting based on Least Absolute Shrinkage and Selection Operator-Quantile Regression Neural Network (LASSO-QRNN) is proposed in this paper. First, important features are extracted from external factors affecting the electricity consumption forecasting by LASSO regression. Then, the LASSO-QRNN model is constructed to predict annual electricity consumption. The results of electricity consumption forecasting under different quantiles in the next several years are evaluated. Besides, we introduce kernel density estimation into our LASSO-QRNN model, which can give a probability distribution instead of a single-valued prediction. The prediction accuracy is evaluated through the empirical analyses from the Guangdong province dataset in China and the California dataset in the United States. The simulation results demonstrate that the proposed method provides better performance for electricity consumption forecasting, in comparison with existing quantile regression neural network (QRNN), back-propagation of errors neural network (BP), radial basis function neural network (RBF) methods, quantile regression (QR) and nonlinear quantile regression (NLQR). LASSO-QRNN can not only better learn the high-dimensional data in electricity consumption forecasting, but also provide more precise results.

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We have been working on the creation of media art, utilizing technologies. In this paper, we have focused on media art created based on the visualization of fluid behaviors. This area is named “fluid dynamics” and there has been a variety of research in this area. However, most of the visualization results of the fluid dynamics show only stable fluid behaviors and a lack of unstable or, in other words, unpredictable behaviors that would be significant in the creation of art. To create various unstable or unpredictable fluid behaviors, we have developed and introduced several new methods to control fluid behaviors and created two media arts called “Sound of Ikebana” and “Genesis”. Interestingly, people find and feel that there is Japanese beauty in these media arts, although they are created based on a natural phenomenon. This paper proposes the basic concept of media art based on the visualization of fluid dynamics and describes details of the methods that were developed by us to create unpredictable fluid dynamics-based phenomena. Also, we will discuss the relationship between Japanese beauty and physical phenomena represented by fluid dynamics.

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Customizing products based on customer needs is an irreversible trend, and many companies strive to provide customized products to customers in less time. Customer requirements are a key factor in the company's ability to provide customized products. In order to better meet customer needs, solve the problem of incomplete and inaccurate expression, and improve the correlation between customized product performance and customer demand, a customized product method based on Bayesian network is proposed. First, the company built a custom product model based on Bayesian networks. According to the model, the customer selects some nodes and their related information. Then, we can accurately predict the final product model through the link tree algorithm, test the customer demand node to determine the focus of the customer's needs, and optimize the model. Finally, an example of a multifunctional nursing bed is used to illustrate the effectiveness of the method.