Nanjing University of Science and Technology

About: Nanjing University of Science and Technology is a education organization based out in Nanjing, China. It is known for research contribution in the topics: Control theory & Catalysis. The organization has 31581 authors who have published 36390 publications receiving 525474 citations. The organization is also known as: Nánjīng Lǐgōng Dàxué & Nánlǐgōng.

An Improved Quantile Regression Neural Network for Probabilistic Load Forecasting

Abstract: Accurate and reliable load forecasting is essential for decision-making processes in the electric power industry. As the power industry transitions toward decarbonization, distributed energy systems, and integration of smart grid features, an increasing number of decision-making processes rely on uncertainty analysis of electric load. However, traditional point forecasting cannot address the uncertainties with only one forecasting value generated at each time step. As they are capable of representing uncertainties, probabilistic forecasts such as prediction intervals and quantile forecasts are preferred. Nevertheless, their practical application is limited partly due to the long training time of multiple probabilistic forecasting models. Traditional quantile regression neural network (QRNN) can train a single model for making quantile forecasts for multiple quantiles at one time. Whereas, the training cost is still unaffordable with large datasets. This paper proposes an improved QRNN (iQRNN) to address the issues of traditional QRNN, which incorporates popular techniques in deep learning areas. A case study on a publicly available dataset shows that not only can the proposed iQRNN generate remarkably superior quantile forecasts than state-of-the-art methods, but also the proposed iQRNN is more accurate, stable, and computationally efficient than traditional QRNN.

Command-Filter-Based Adaptive Fuzzy Finite-Time Control for Switched Nonlinear Systems Using State-Dependent Switching Method

Abstract: The adaptive fuzzy finite-time tracking control problem of a class of switched nonlinear systems is investigated in this study. Fuzzy logic systems are introduced to handle the unknown nonlinear terms in the considered system. To overcome the drawback in the recursive design method, a finite-time command filter is employed. By constructing a new state-dependent switching law and adaptive fuzzy control signal, the existing restrictions on subsystems of switched systems are relaxed, all subsystems of the considered system are allowed to be unstabilizable. To avoid the Zeno behavior, a new hysteresis switching law is derived. It is proven that all states of the closed-loop system are bounded in finite time under the proposed fuzzy finite-time control scheme. Additionally, the proposed control method is extended to a class of more general switched large-scale nonlinear systems. Finally, two examples are provided to verify the developed method's effectiveness.