Predicting Solar Radiation Using Machine Learning Techniques

被引：0

作者：

Moosa, Aaftaab ^{[1
]}

Shabir, Hamza ^{[1
]}

Ali, Huzefa ^{[1
]}

Darwade, Rishikesh ^{[1
]}

Gite, Balasaheb ^{[1
]}

机构：

[1] Sinhgad Acad Engn, Dept Comp Engn, Pune 411048, Maharashtra, India

来源：

PROCEEDINGS OF THE 2018 SECOND INTERNATIONAL CONFERENCE ON INTELLIGENT COMPUTING AND CONTROL SYSTEMS (ICICCS) | 2018年

关键词：

Solar Energy forecasting; Artificial Neural Network; Global Horizontal Irradiance (GHI); Random Forest; Gradient Boosting;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Today, energy is something that is taken for granted. Though non-renewable energy has a harmful impact on environment, its usage is all time high and is soon going to deplete. As the world looks for more environmental friendly energy resources, the solar energy comes out as an important clean energy source. The quantity of solar energy which strikes the surface of earth each hour is enough to meet the energy requirement of complete human population for a complete year. This calls for industries to soon adopt renewable energy sources as the primary source of energy. The main goal with this paper is to build a model that makes information regarding one's potential for switching to solar energy and making it available to a wider audience, bringing down the cost of sales and marketing and removing the uncertainties with the production of solar energy. A precise forecast of the solar irradiance can also be used in calculating the system load and accurately planning the grid system in advance.

引用

页码：1693 / 1699

页数：7

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