Auto-Encoder-Extreme Learning Machine Model for Boiler NOx Emission Concentration Prediction

no code implementations • 29 Jun 2022 • Zhenhao Tang, Shikui Wang, Xiangying Chai, Shengxian Cao, Tinghui Ouyang, Yang Li

An automatic encoder (AE) extreme learning machine (ELM)-AE-ELM model is proposed to predict the NOx emission concentration based on the combination of mutual information algorithm (MI), AE, and ELM.

Wind power ramp prediction algorithm based on wavelet deep belief network

no code implementations • 11 Feb 2022 • Zhenhao Tang, Qingyu Meng, Shengxian Cao, Yang Li, Zhongha Mu, Xiaoya Pang

To improve the ramp prediction accuracy, a hybrid wavelet deep belief network algorithm with adaptive feature selection (WDBNAFS) is proposed.

feature selection Time Series +1

Dynamic Prediction Model for NOx Emission of SCR System Based on Hybrid Data-driven Algorithms

no code implementations • 3 Aug 2021 • Zhenhao Tang, Shikui Wang, Shengxian Cao, Yang Li, Tao Shen

Aiming at the problem that delay time is difficult to determine and prediction accuracy is low in building prediction model of SCR system, a dynamic modeling scheme based on a hybrid of multiple data-driven algorithms was proposed.

feature selection FLUE +2