Big Data Analytics, Machine Learning and AI in the Renewable Energy Sector

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By Tanmoy Ray, Scientist, Counselor, Career Advisor

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.

According to Bill Gates:

“If I were starting out today… I would consider three fields. One is artificial intelligence. We have only begun to tap into all the ways it will make people’s lives more productive and creative. The second is energy because making it clean, affordable, and reliable will be essential for fighting poverty and climate change.”

The third field, according to Gates is biosciences.

With the massive speed of growth of the world, the overall data size is increasing as well – Big Data. Along with big data, the other technological growth that has become the talk of the town is AI.

Since 2015, AI has taken the business world by storm. Machine learning is an integral part of AI. Together these technologies, are revolutionizing the industry. They allow the organizations to improve their overall customer experience by means of automating work processes. Machine Learning & AI also boost their employee performance, and most importantly, develop intelligent machines to provide them assistance in their day-to-day functioning.

Artificial Intelligence is utilizing data and specialized hardware and machine learning algorithm to augment humans and allow people to do things that they never imagined that they could do before.

According to Harvard Business Review (HBR), about 51% of AI leaders have predicted that, AI will create a massive internal impact on the back office functions of multiple organizations by the year 2020, thereby enabling the employees to take care of more important tasks while AI will take the charge of repetitive and automatable tasks.

Renewable Energy

Energy plays a key role in the economy and environment. Renewable energy is clean, affordable, and reliable, and has got the potential to counter poverty and climate change. Renewable energy, also termed as alternative energy, simply means the energy that is produced from sources other than the primary energy supply, the fossil fuels (coal, natural gas, oil).

The energy industry is considered as highly capital intensive along with the huge impact on employment. More importantly,  hundred years from now, there might not be any fossil fuels left. Hence, the renewable energy field promises a lot in terms of sustainability and jobs prospects.

Our planet is blessed with multiple natural sources of energy such as sunlight, air, wind, and other resources, however, its usage must happen in an appropriate way for both human welfare and the environment.

Thus there have been critical research and development going on over the exploitation of these renewable sources of energy which comprises of:

  • Technological Development – To attain increased production from the available natural resources
  • Environmental awareness, and
  • Better management and distribution system
Why the Renewable Energy Sector Needs Big Data Analytics, Machine Learning, and AI

With the automation of IT and business processes and advanced analytics, AI and Big data are seen as a revolution in achieving the future goals of RE, just like any other domain. A recent study by Infosys says that 48% of the RE and utility industries consider AI to be the central driving force behind their organization’s success, whereas the other 46% are already on the path to integrating AI.”

Below are some of the benefits these organizations are witnessing with AI and Big Data utilization:

  • Dependability – Smart grids, improving operations management and ensuring efficient usage and storage of renewable resources
  • Demand Management and Safety – Combat through predictive algorithms. Assists with outage prediction and response. Also predicts the production and consumption of small-scale producers and consumers respectively.
  • Optimization – Improved asset management and its maintenance, efficient operations management, workflow and portfolio management.
  • Delighted Customer Experience – Meet customer demands and provide delighted experience through IVR and personalization.
  • Forecasting and equipment efficiency – Forecasting of renewable energy resources by accumulating data from wind and solar plants and merging them with atmospheric data.

Read the source article at the blog by Tanmoy Ray.