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Artificial Intelligence and Machine Learning Methods for Renewable Energy

Published in CRC Press

Global energy demands are growing rapidly. And, nonrenewable energy sources will
not be able to meet our future energy requirement. Renewable energy, which has the
benefit of minimum carbon emission, may be a feasible solution to make our planet
safer and energy proficient. Over the past few years, many sorts of renewable energy
resources such as wind, solar, geothermal, biomass, and tidal have been exploited.
Computing and machine learning (ML) improves the potency and accessibility of
renewable energy technology [1]. Computing (artificial intelligence [AI]), in conjunction
with many AI advanced technologies, has incontestable immense potential
to work on the renewable energy. AI and ML technologies will build a control by
reducing emissions and increasing production potency. In this era, ML algorithms
are applied to the information gathered from advanced sensors, good meters, intelligent
device, and grid operators to estimate how individual appliances behave.
Germany, as an example, uses advanced AI technology in the early warning system,
which takes time period information from wind turbines and star panels around the
country to predict the energy report for next two days. AI may also facilitate the
trade to improve safety, responsibleness, and potency. It may also offer visibility into
energy run, consumption patterns, and instrumentation health. As an example, prophetic
analytics will take device knowledge from a turbine to watch wear and tear,
and predict with a high degree of accuracy once it might want maintenance.

About the journal
PublisherCRC Press
Open AccessNo