Zhongyang Han

TL;DR: This paper reviews the state of the art developments in deep learning for time series prediction and categorizes them into discriminative, generative, and hybrids models, based on modeling for the perspective of conditional or joint probability.

TL;DR: Experimental studies involving application on a number of benchmark test problems and parameter determination for multi-input multi-output least squares support vector machines are given, in which the results demonstrate promising performance of the proposed algorithm compared with other representative multi-objective particle swarm optimization algorithms.

TL;DR: In this article, a multi-output least square support vector regressor is proposed, which considers not only the single fitting error of each tank level but also the combined one, and a particle swarm optimization is designed to determine the parameters of this model for the sake of improving the prediction accuracy.

TL;DR: A long-term prediction for the energy flows is proposed by using a granular computing-based method that considers industrial-driven semantics and granulates the initial data based on the specificity of manufacturing processes.

TL;DR: A nonlinear programming model for oxygen system scheduling is proposed in this study, which concerns not only the practical characteristics of the energy pipeline network, but also the electricity cost acquired by a fitting regression modeling between the load of air separation units U+0028 ASU U-0029 and its corresponding electricity consumption.