An Experimental Fuzzy Inference System for Global Grid Electricity Peak Power Load Forecasting Third Core Module of First Console on G2P3S

Authors : Burak Omer SARACOGLU

Pages : 75-101

Doi:10.30521/jes.338575

View : 29 | Download : 8

Publication Date : 2017-12-03

Article Type : Research Paper

Abstract :Our World gives several symptoms of climate change. Devastating draughts increase insert ignore into journalissuearticles values(negative for World insert ignore into journalissuearticles values(-););, global mean temperature increase insert ignore into journalissuearticles values(-);, lightning strikes increase insert ignore into journalissuearticles values(-);, sea ice cover melt insert ignore into journalissuearticles values(-);, tree mortality increase insert ignore into journalissuearticles values(-);, and forest degradation increase insert ignore into journalissuearticles values(-); have been observed for decades. They are all negative measures for continuity of life. Diversity of species has been decreasing, so that life on Earth is dying. Only responsible specie for this situation is humankind. This study presents a small footstep to prevent this situation. Modeling of a 100% renewable power grid on World insert ignore into journalissuearticles values(Global Grid); is eminent. Annual peak power load insert ignore into journalissuearticles values(Gigawatt: GW, Kilowatt: kW); insert ignore into journalissuearticles values(peak demand or load); forecasting in power demand side is crucial for global grid modeling. This study presents an experimental fuzzy inference system for the third core module insert ignore into journalissuearticles values(100 years’ power demand forecasting); of the first console insert ignore into journalissuearticles values(long term prediction); of Global Grid Peak Power Prediction System insert ignore into journalissuearticles values(G 2 P 3 S);. The inputs insert ignore into journalissuearticles values(world population, global annual temperature anomalies °C); and the output insert ignore into journalissuearticles values(annual peak power load demand of Global Grid in GW); are modeled with seven triangular fuzzy input membership functions and seven constant output membership functions. The constant Sugeno-Type fuzzy inference system is used in the current experimental model. The maximum absolute percentage error insert ignore into journalissuearticles values(MAP); is calculated as 45%, and the mean absolute percentage error insert ignore into journalissuearticles values(MAPE); is found as 39% in this experimental study. The MAP and MAPE of the first core module model insert ignore into journalissuearticles values(Type 1); were 0,46 and 0,36. The MAP and MAPE of the second core module model insert ignore into journalissuearticles values(Interval Type 2); were 0,46 and 0,36. As a result, this study is a good start for the third core module of the first console on Global Grid Peak Power Prediction System research, development, demonstration, & deployment insert ignore into journalissuearticles values(RD 3 ); project. This experimental study also warns humankind in this subject. Hopefully, the most polluting societies on our World such as China, United States, India, Russia, Japan, Germany, South Korea, and Canada take urgent actions to start to build the foundations of 100% renewable power global grid by organizing a global grid consortium.
Keywords : Scilab, Sugeno, Takagi Sugeno Kang, Fuzzy inference system, Global grid, Peak power

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