Last year, the US Department of Energy announced that the SunShot Initiative successfully met its 2020 utility-scale solar cost target of $0.06 per kilowatt hour three years earlier than expected. This milestone marks a huge step forward for the industry as it moves toward a greener future that's less reliant on fossil fuels. While 6 cents may sound cheap, it's far from the bottom. The SunShot Initiative is now working towards another lofty goal for 2030: $0.03 per KwH. This benchmark would make solar energy one of the least expensive options for new power generation, and help drive mass adoption on a national scale.

The two biggest societal challenges for the twenty-first century are also the biggest opportunities – automation and climate change. The epitaph of fossil fuels with its dark cloud burning a hole in the ozone layer is giving way to a rise of solar and wind farms worldwide. Servicing these plantations are fleets of robots and drones, providing greater possibilities of expanding CleanTech to the most remote regions of the planet. As 2017 comes to end, the solar industry for the first time in ten years has plateaued due to the proposed budget cuts by the Trump administration. Solar has had quite a run with an average annual growth rate of more than 65% for the past decade promoted largely by federal subsidies.

Like most sectors, the solar industry is rapidly embracing ways to analyze and crunch data in order to lower the cost of solar energy and to open up new markets for their technology. The rise of data tools--algorithms, machine learning, sensors--are driving investments in, and acquisitions of, solar startups, while entrepreneurs are launching new companies that are using data to solve various solar industry problems. Meanwhile, big companies are spending money on tracking, monitoring and evaluating data from solar projects worldwide, helping to lower the cost of generating energy from the sun. It shouldn't come as a surprise that the solar sector is the latest to embrace the value of data. Other traditionally non-digital sectors, like the auto industry, oil and gas, and agriculture are turning to managing data as a necessity to keep their technology competitive and their companies in business.

New research has posited that artificial intelligence will increasingly automate operations for the wind and solar industries, boosting their efficiencies in areas such as decision making and planning, condition monitoring, robotics, and inspections. The new position paper published this week by DNV GL -- international accredited registrar and classification society headquartered near Oslo -- entitled Making Renewables Smarter: The benefits, risks, and future of artificial intelligence in solar and wind, outlines the advances being made in robotics, inspections, supply chain, and the way we work and showcases a variety of opportunities for the solar and wind industries to embrace artificial intelligence (AI) applications to improve their efficiency. "The use of artificial intelligence in industries continues at an impressive rate -- in manufacturing, engineering, healthcare, transportation, finance, telecommunications, services, and energy," the authors of the report explain. "Artificial intelligence's ability to use machine learning to analyse historical and new data, make predictions, control physical operations, and make decisions at increasingly higher levels is having an immense impact." The report explores ways in which AI applications like machine learning can impact the efficiency levels of areas involved in the wind and solar industries such as decision making and planning, condition monitoring, robotics, inspections, certifications and supply chain optimization, as well as the way technical work is carried out.

The two biggest societal challenges for the twenty-first century are also the biggest opportunities – automation and climate change. The epitaph of fossil fuels with its dark cloud burning a hole in the ozone layer is giving way to a rise of solar and wind farms worldwide. Servicing these plantations are fleets of robots and drones, providing greater possibilities of expanding CleanTech to the most remote regions of the planet. As 2017 comes to end, the solar industry for the first time in ten years has plateaued due to the proposed budget cuts by the Trump administration. Solar has had quite a run with an average annual growth rate of more than 65% for the past decade promoted largely by federal subsidies.

The concept of artificial intelligence (AI) involves machines learning and acting on data sets without human programming or intervention. AI can be broken down into machine learning, deep learning and neural networks. Without getting too technical, essentially the whole premise of AI is a machine mimicking the human brain. The machine can learn and adapt to different scenarios, and as times passes the machine, gets smarter and reacts differently to achieve better results. AI will play a pivotal role in many industries, through business intelligence and solving problems quicker than humans.

Like most sectors, the solar industry is rapidly embracing ways to analyze and crunch data in order to lower the cost of solar energy and to open up new markets for their technology. The rise of data tools--algorithms, machine learning, sensors--are driving investments in, and acquisitions of, solar startups, while entrepreneurs are launching new companies that are using data to solve various solar industry problems. Meanwhile, big companies are spending money on tracking, monitoring and evaluating data from solar projects worldwide, helping to lower the cost of generating energy from the sun. It shouldn't come as a surprise that the solar sector is the latest to embrace the value of data. Other traditionally non-digital sectors, like the auto industry, oil and gas, and agriculture are turning to managing data as a necessity to keep their technology competitive and their companies in business.

Like most sectors, the solar industry is rapidly embracing ways to analyze and crunch data in order to lower the cost of solar energy and to open up new markets for their technology. The rise of data tools--algorithms, machine learning, sensors--are driving investments in, and acquisitions of, solar startups, while entrepreneurs are launching new companies that are using data to solve various solar industry problems. Meanwhile, big companies are spending money on tracking, monitoring and evaluating data from solar projects worldwide, helping to lower the cost of generating energy from the sun. It shouldn't come as a surprise that the solar sector is the latest to embrace the value of data. Other traditionally non-digital sectors, like the auto industry, oil and gas, and agriculture are turning to managing data as a necessity to keep their technology competitive and their companies in business.

Like most sectors, the solar industry is rapidly embracing ways to analyze and crunch data in order to lower the cost of solar energy and to open up new markets for their technology. The rise of data tools--algorithms, machine learning, sensors--are driving investments in, and acquisitions of, solar startups, while entrepreneurs are launching new companies that are using data to solve various solar industry problems. Meanwhile, big companies are spending money on tracking, monitoring and evaluating data from solar projects worldwide, helping to lower the cost of generating energy from the sun. It shouldn't come as a surprise that the solar sector is the latest to embrace the value of data. Other traditionally non-digital sectors, like the auto industry, oil and gas, and agriculture are turning to managing data as a necessity to keep their technology competitive and their companies in business.