In the coming years the Internet of Things will fundamentally change the way that the energy industry works, and challenges the very structure of the energy industry hitherto. In particular, the shift will be accompanied by a large volume of data which can be used in a wide variety of business areas, to improve upon energy sales where it will increase revenue for the organization as a whole, in the technical workstreams where it will help maintain and optimize the energy infrastructure that has already been built. In the field of HR where energy businesses will be restructured, artificial intelligence will be a tool to make workers more productive within their organization.
This will have the effect that organizations will become flatter, which means they will become less hierarchical, they will become leaner, as they will probably reduce their workforce and retrain their staff, their focus will be on solving problems in the most effective way. In effect, organizations will require more project managers familiar with data management techniques, in organizational and technical roles within the organization. Many roles that were being outsourced by energy businesses, such as project management roles that external management consultants were paid to do, will be done by internal management consultants in the future that work within the energy business. They will be in touch with the organization, are familiar with the products that are sold, know the people that work there, and feel closer to the business culture of the energy firm where they work.
In fact, many roles that were previously outsourced because they could be done cheaper in low-cost countries, or could be done by someone else who has had an information advantage, will return to places where they were originally outsourced from. For example, the IT departments of large energy businesses in English-speaking countries have outsourced a lot of their work to India, where to it was a lot cheaper to solve technical issues. Given the fact that IT is a critical component of every energy business these days, and the fact the IT security becomes increasingly important, it is not unreasonable to assume that a lot of these workstreams will be reintegrated in the organization.
From all of these work areas that I have mentioned above, the optimization and maintainance of grid infrastructure and energy generation will two areas that will be most effected by artificial intelligence due to the fact that many tasks are fairly repetitive and allow technicians to focus on less mundane tasks. This allows technicians to save a lot of time and sift through lage data volumes, being alerted when things go wrong before the problem becomes a real issue. In effect, this prepares the team for all eventualities.
Big data is the moving piece in all of this, because big data underlies all aspects of the energy transition and without it there would be no need for energy businesses to adapt to the new digital age. One could summize that the process that underlies narrow artificial intelligence is similar to the way that the human brain works. Our brain collects sensory information from the outside world, once the information has been collated it is transmitted electrochemically in the human body. There are various nerve points that bundle information and send it to the right place. Once information has been turned into a signal, it makes very little difference from where that information came from. This is similar to coding, although it is more sophisticated in the human body. The various node points and ways to transmit that information electrochemically leads to an almost infinite set of options which machines cannot reach (yet). Machines do have an advantage over humans, and that is the fact that they transmit information at lightening speed, much faster then humans ever could.
In much the same way, smart metering collects data from households but also from businesses and from industry, that information is send to advanced metering systems (AMS) from where it is send to substations and is bundled together. One can infer that other node points in the system, information that is coming from smart grid systems for example, will add to the amount of data. The more data becomes available, the more accurate forecasts can be made which helps to optimize grid networks and prevent black outs of the electricity grid, to give just one example. By doing that, one can save energy because one knows how to microtune demand and supply of electricity of the grid.
The critical part is how one can integrate data from smart metering systems and grid infrastructure. More and more data will be more data available of subhourly load series, which will help machines to control the electricity demand and electricity supply. This has been pointed out by Analytics. The real question is how to integrated the myriad of renewable energy installations into the grid system and control when to switch them and an when to switch them off, as the supply level of electricity from wind and photovoltaic installations fluctuates. Also, there is the question how to best integrate the grid infrastructure using telecommunications networks, as was pointed by Marcus Törnqvist from Ericsson.
Actual artificial intelligence (Narrow AI)
The energy industry will mostly be effected by narrow AI, at least in the period from 2020 to 2030. Weak AI is centered on specific aspects, solving problems which requires large amounts of data, butwithout making decisions based on his understanding of many different disciplines. It also does not require self-awareness of AI or any true intellectual capabilities. In the energy industry, the focus will be more on specific aspects such as trouble-shooting and forecasting demand and supply of electricity. Nevertheless, these work areas play a very important role in the energy industry, because they drive operating costs higher. Narrow AI and forecasting solutions based on big data will fit neatly into operations, as was pointed out in the SAS White Paper.
All this will come together in the coming years, and with data on electricity generation as well as data on operational grid management, trouble-shooting will reach a whole new level to forecast electricity demand, schedule investments to optimize grid infrastructure, and know when these investment will have to be made.
What we can expect in terms of intelligent power generation and transmission
In the next few years we will experience how artificial intelligence will impact on our lifes and will hopefully benefit from its many advantages. Many studies have shown that artificial intelligence will impact the energy sector like no other industry, comparable to the impact digitalization has had on the telecommunications industry. A lot innovations we have inherited from the telecommunications transition in the last 20 years can be put to good use in the energy industry, for example managing infrastructure components and renewable energy installations with a smart phone and overseeing many aspects of the electricity grid. In fact, the application of artificial intelligence in the energy sector would even be remotely possible without the telecommunications sector advancements having been made.