The global energy demands are growing every year, and fossil fuels won't be able to fulfill our energy needs in the future. Carbon emissions from fossil fuels hit an all-time high in 2018 due to increased energy consumption. On the other hand, renewable energy is emerging out as a reliable alternative to fossil fuels. It is much safer and cleaner than conventional sources. With the advancements in technology, the renewable energy sector has made significant progress in the last decade.

Artificial intelligence, big data analytics, and machine learning are revolutionizing renewable energy sector and allowing the companies to improve their overall customer experience by means of automating work processes. Optimization and predictions are two major factors on which the energy sector heavily depends. The energy industry also produces vast amounts of data, and to turn this data into insights, major energy players are turning to AI. The historical data collected by power plants can now be combined with weather and satellite data through advancements in big data, AI, and machine learning. Consequently, solar and wind forecasting technology can predict weather conditions well in advance.

With the status of current wind and solar subsidies in the U.S. unclear, the industry needs to brace for impact by making up for the increase in investment risk post-subsidies. A post-subsidy world means the renewable energy sector needs to successfully harness and utilize AI and smart data analytics to maximize investment returns. According to a report by the International Energy Agency, carbon emissions hit a record high in 2018. A new record high is likely in 2019, too. Global energy demand is rising exponentially due to forces like globalization, industrialization and exploding populations. We can't yet offset the full impact of these massive forces with renewable energy, but with the right advancements and integrations of AI and increased investment in renewable energy, we can scale to meet the challenge.

Analytics, Machine Learning, and artificial intelligence (AI) are used to interpret the past, optimize the present and predict the future. The energy sector heavily depends on optimization and predictions for energy production, energy grid balancing, and consumption habits. Additionally, the energy industry produces massive amounts of data. To turn this data into insights to improve productivity and cut costs, major energy players are turning to AI. Here we will look at the scopes of advanced analytics, machine learning and AI in the renewable energy sector.

Analytics, Machine Learning, and artificial intelligence (AI) are used to interpret the past, optimize the present and predict the future. The energy sector heavily depends on optimization and predictions for energy production, energy grid balancing, and consumption habits. Additionally, the energy industry produces massive amounts of data. To turn this data into insights to improve productivity and cut costs, major energy players are turning to AI. Here we will look at the scopes of advanced analytics, machine learning and AI in the renewable energy sector.