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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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Enhancing the efficiency of cabin heaters in emergency shelters after earthquakes through an optimized fuzzy controller

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建筑模拟(英文) 2023年第9期16卷 1759-1776页

作者： Erkan Duman Dila Seckin Computer Engineering Department Engineering FacultyFirat UniversityElazig 23119Turkey

In just one and half minutes,more than fifty thousand died due to the 7.7 and 7.6 magnitude earthquakes that struck Turkey’s southeast on February 6,2023;thousands of families who barely escaped struggled to survive in the freezing weather.A warm shelter was the most basic requirement of these *** buildings are a rapid and easy solution to this ***,there is a need for a more effective and safe heating option than a wood fire for these *** this study,cabin heaters,which allow truck drivers to warm up when they park their vehicles to sleep,are specially optimized for emergency shelters after an *** optimized fuzzy controller was developed to use in such buildings,which allows an air–fuel ratio in the combustion chamber of the cabin heater to be controlled adaptively based on system dynamics to get lower carbon emissions and fuel *** TRNSYS software was used to establish the transient simulation model of a cabin heater with a capacity of 4 kW for a typical 21 m^(2) shelter building in Turkey’s cold *** developed fuzzy controller carried out the heating process of this shelter from the 15th of November to the 15th of *** of using expert knowledge,the gray wolf optimization(GWO)method was applied to optimize the fuzzy controller parameters developed for the cabin *** the optimized fuzzy controller,the fuel consumption at the end of the heating season was reduced by an average of 0.2 L/h,and the cabin heater’s efficiency increased by more than 13%.Our simulation results show that the intelligent controller we developed could improve diesel fuel combustion efficiency by up to 85%.

关键词： parking heater cabin heater emergency shelters fuzzy controller gray wolf optimization TRNSYS simulation

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