Prediction of Tamil Nadu's Annual Electricity Consumption Using Adaptive Neuro-fuzzy network

M. M. Raja Paul, S. Amosedinakaran, A. Bhuvanesh, R. Kannan, M. Moses, A. Ramkumar
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Abstract

In this study, the electricity consumption is forecasted for 10 years from 2021 to 2030 using Adaptive Neuro Fuzzy Inference System (ANFIS) technique from the historical data. State GDP (State Gross Domestic Product), population and income rate are chosen as the independent input variables. In this study, four different model have been developed based on independent input variables for instance type A (State GDP, and population), type B (population and in-come rate), type C (State GDP and income rate) and type D (State GDP, population and income rate). The electricity consumption is the independent output variables for all the models. The different models have been developed to show the impact of independent input variables while predicting the electricity consumption. 3 scenarios are developed according with growth rates of input variables. The simulation results are verified with the National Electricity Plan (NEP) of India.
基于自适应神经模糊网络的泰米尔纳德邦年用电量预测
本研究利用自适应神经模糊推理系统(ANFIS)技术,从历史数据中预测了2021 - 2030年10年的用电量。选取州GDP(州国内生产总值)、人口和收入比率作为自变量。在这项研究中,基于独立输入变量开发了四种不同的模型,例如A型(州GDP和人口)、B型(人口和收入率)、C型(州GDP和收入率)和D型(州GDP、人口和收入率)。用电量是各模型的独立输出变量。在预测电力消耗时,已经开发了不同的模型来显示独立输入变量的影响。根据输入变量的增长率发展出3种情景。仿真结果与印度国家电力计划(NEP)进行了验证。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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