Estimation of Energy Demand and Sustainable Source for India & Study of Renewable Energy though Machine Learning

Mohammad Mamoon, Abhishake Jain
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Abstract

In the last decade, the energy industry has shifted its focus toward alternative energy sources such as solar, hydro, and nuclear power in order to satisfy individual needs. There is still an issue with not having enough access to energy in several regions of India. The production of electricity via thermal means, the generation of power through nuclear means, and the generation of power through hydro means are the three principal forms of power generating in India. This paper discusses the power generation from different energy sources and analyses the demand in India between 2017 to 2020 years. The purpose of this research is to examine data and depict it in a manner that may be understood quickly and easily. The purpose of this research is to compile secondary data drawn from reputable sources and examine that data in the context of existing knowledge gaps. Between the years 2017 and 2020, it will be necessary to conduct research and analysis on a variety of alternative energy sources, including thermal power and other thermal sources. Additional machine learning strategies have been used to the solar data in order to study and anticipate. As a result of using this method, the loss and MSE (mean square error) of the solar data time series were discovered. Therefore, the proper use of power is something that might be evaluated and put into practice from an Indian point of view.
通过机器学习估算印度的能源需求和可持续来源以及研究可再生能源
近十年来,能源行业已将重点转向太阳能、水能和核能等替代能源,以满足个人需求。在印度的一些地区,仍然存在无法获得足够能源的问题。火力发电、核能发电和水力发电是印度的三种主要发电方式。本文讨论了不同能源的发电情况,并分析了 2017 年至 2020 年印度的电力需求。本研究的目的是研究数据,并以易于理解的方式对其进行描述。本研究的目的是汇编从可靠来源获得的二手数据,并在现有知识差距的背景下检查这些数据。从 2017 年到 2020 年,有必要对各种替代能源(包括热电和其他热能)进行研究和分析。为了进行研究和预测,我们对太阳能数据采用了额外的机器学习策略。使用这种方法的结果是,发现了太阳能数据时间序列的损失和 MSE(均方误差)。因此,从印度的角度来看,可以对电力的合理使用进行评估并付诸实践。
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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