Solar energy industries have benefited considerably from the potential of AI, machine learning predictive models, and data science.
FREMONT, CA: Climate variation and the depletion of nonrenewable energy sources are driving forces behind sustainable energy research and development, impacting all governments and businesses. Green energy generation is currently a vitally active and evolving area of study.
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Solar energy is a renowned renewable energy source that is easy to get and has fewer restrictions on shopping and deployment. Regardless, solar energy production stays relatively costly in comparison to fossil fuels.
Also, energy storage methods are often insufficient to supply energy, extended storms, and overcast cloudy nighttime conditions. Therefore, solar energy systems and connected technologies have grown into a primarily employed green energy source.
Nonetheless, solar power is still not a vastly applied energy source compared to customary energy sources, given the high deployment expenses, low conversion rates, and battery storage problems.
Irrespective of the hurdles, there are many innovative studies of new technologies like machine learning, artificial intelligence, big data, IoT, and new means to improve solar energy transformation efficiency to better the competitiveness of solar power in the marketplace.
Artificial intelligence (AI) and machine learning (ML) have evolved essential technology solutions. The sector is continuously looking to cater to the fastly growing demand for clean, cheap, and reliable power. These progressive technologies can analyze the past, streamline the present, and predict the future.
This indicates that AI and ML can amend most of the hurdles in the solar sector. With technology making swift improvements, the renewable power sector has made considerable progress in the past decade. Yet, a few hurdles still prevail that can be handled with the support of AI and ML. Indeed, the demand for solar power will rise shortly, making it important to invest in appearing technologies, along with AI, ML, and IoT, to improve productivity and overwhelm shortfalls.
