The Role of Artifcial Intelligence in Renewable Energy

Title

The Role of Artifcial Intelligence in Renewable Energy

Creator

Vijayalakshmi S.; Savita; Genish T.; George J.P.

Description

Technology is evolving at an unbelievable pace, to the extent where many of us cant keep up effectively. With increasing Artifcial Intelligence (AI) complexity, our environment will be transformed in amazing ways over the years and decades that follow. The renewable energy (RE) sector is no different. AI can observe patterns and beneft from large amounts of knowledge. Consequently, AI is able to make improvements to enhance energy production, conversion, and even delivery. These systems allow precise forecasting of, for example, weather and loads, mitigating, among countless other uses, the possibility of electrical surges. AI systems would signifcantly improve the productivity of renewable systems by automation over the next 10 years. For solar and wind energy, this will become particularly prevalent. Independent power producers would have the latitude required to deliver ever-more sustainable business models and services by integrating increased generation coupled with low-cost savings provided by automation. We are all aware of the requirements of RE, including solar power. However, how can AI help to increase the availability of RE? The demand for global energy is growing day by day, but fossil fuels cannot fulfll our future needs for energy. Because of increased energy consumption, fossil fuel carbon emissions have reached very high levels over time. RE, however, is emerging as a good replacement for fossil fuels. It is safer and also very clean in comparison to traditional sources. The RE industry has made tremendous strides during the preceding decade with developments in technology. AI and machine learning technologies can analyze data to predict the future. So, the use of AI can solve the problems and challenges of RE. In this chapter we discuss RE, its sources and challenges, and how AI can address these challenges. The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

Source

Power Systems, pp. 253-269.

Date

2023-01-01

Publisher

Springer Science and Business Media Deutschland GmbH

Subject

Artifcial Intelligence; control system; General Electric; Hydrogen; powerScout; Renewable energy; Robotics

Coverage

Vijayalakshmi S., Department of Data Science, Christ University, Maharashtra, Lavasa, India; Savita, School of Computing Science and Engineering, Galgotias University, Uttar Pradesh, Greater Noida, India; Genish T., Department of Computer Science, KPR College of Arts Science and Research, Tamil Nadu, Coimbatore, India; George J.P., Department of Data Science, Christ University, Pune, Lavasa, India

Rights

Restricted Access

Relation

ISSN: 16121287

Format

Online

Language

English

Type

Book chapter

Identifier

https://doi.org/10.1007/978-3-031-15044-9_12

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85152423305&doi=10.1007%2f978-3-031-15044-9_12&partnerID=40&md5=3ae357f58a88f779ac17e4ba7b3fc418