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Short-term Load Forecasting of Regional Distribution Network Based on Generalized Regression Neural Network Optimized by Grey Wolf Optimization Algorithm

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CSEE Journal of Power and Energy Systems 2021年第5期7卷 1093-1101页

作者： Leijiao Ge Yiming Xian Zhongguan Wang Bo Gao Fujian Chi Kuo Sun Key Laboratory of Smart Grid of Ministry of Education Tianjin UniversityTianjin 300072China State Key Laboratory of Reliability and Intelligence of Electrical Equipment Hebei University of TechnologyTianjin 300132China China Electric Power Research Institute Co.Ltd Beijing 100192China. State Grid Tianjin Electric Power Company Tianjin 300010China.

Short-term load forecasting of regional distribution network is the key to the economic operation of smart distribution systems,which not only requires high accuracy and fast calculation speed,but also has a diversity of influential factors and strong *** paper proposes a short-term load forecasting model for regional distribution network combining the maximum information coefficient,factor analysis,gray wolf optimization,and generalized regression neural network(MIC-FA-GWO-GRNN).To screen and decrease the dimension of the multiple-input features of the short-term load forecasting model,MIC is first used to quantify the non-linear correlation between the load and input features,and to eliminate the ineffective features,and then FA is used to reduce the dimension of the screened input features on the premise of preserving the main information of input *** that the high-precision short-term丨oad forecasting based on GWO-GRNN model is *** is used to regressively analyze the input features after screening and dimension reduction,and the parameter of GRNN is optimized by using the GWO,which has strong global searching ability and fast *** a case study of a regional distribution network in Tianjin,China verifies the accuracy and applicability of the proposed forecasting model.

关键词： Factor analysis generalized regression neural network gray wolf optimization maximum information coefficient short-term load forecasting

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Online Output Estimation for Multimode Process with Dynamic Time-delay

Online Output Estimation for Multimode Process with Dynamic ...

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第三十九届中国控制会议

作者： Pang Chen Guo Xie Han Liu Lili Liang Huan Gao Dan Wang WenJiang Ji Xi’an University of Technology

There is a time-delay problem between the correlated process variables of the industrial. It is very important to get accurate time-delay for estimating hard-to-measure variable. The existing methods for solving time-delay need to obtain both easy-to-measure variables and hard-to-measure variable. But the hard-to-measure variable cannot be directly measured due to some technical and cost reasons. To solve this problem, a random forest regression model combining sliding window and maximum information coefficient method is proposed. The on-line estimation of the time-delay between the easy-to-measure variables and the hard-to-measure variable is realized. The process variables are reconstructed by the time-delay parameters. The reconstructed process variables are used to build the soft sensor. Finally, the hard-to-measure variable can be estimated by soft sensor accurately. This method is applied to the process variable estimation of nitric acid produce process by dual-pressure method. The effectiveness of the method is verified by experiment results.

关键词： Time-delay maximum information coefficient Random Forest Regression Soft Sensor

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