The energy transition is bringing rapid change to the infrastructure that is essential to businesses regardless of their size or the sector in which they operate. And the energy transition will take on even greater significance as global demand for electricity continues to grow. Our energy systems of the future will need to be flexible and intelligent; artificial intelligence has an important role to play in delivering a smooth energy transition.
Artificial Intelligence in the big energy world
At the network level - the long-distance infrastructure carrying the power from generators to consumers - artificial intelligence can identify when assets such as cables will need to be replaced, allowing maintenance work to be scheduled in long before actual faults arise. And in terms of getting power on to the grid, artificial intelligence supports calculations around how much wind will be available to an individual turbine site over a particular period, assisting with maximising power production from it.
Artificial intelligence can clearly make a major contribution to the energy transition at the system end of things, but it has an important role to play at the individual organisation level too. Machine learning and algorithms can be used by individual businesses to improve their energy efficiency, understand their energy consumption and optimise performance
Intelligent smart meter use
Many large retail chains already have smart meters which capture and report the energy consumption of each shop individually. These can also be used to record energy use for particular devices (such as HVAC, fridges etc) or defined parts of the site (bakeries, restaurants, coffee shops). Using artificial intelligence techniques, we can work with this data provided by smart meters to provide energy efficiency insight, which can be used to bring about significant savings and lower carbon emissions at the individual shop level.
Machine learning around root cause analysis
A sudden spike or fall in energy consumption can be the 'canary in a coal mine', warning that energy systems, processes or equipment are not running as they should and require investigation. With the right intelligent data in place, machine learning models can be trained to pinpoint the root cause of an exceptional occurrence. Spotting anomalies where the actual energy consumption differs from the forecast figure is straightforward for regular consumption shapes and usages, as with production plants and retail outlets. Tools such as our Optimum energy management solution leverages artificial intelligence and advanced analytics to do just this, enabling manufacturing customers to quickly spot energy use anomalies and correct them.
Solar panels can provide businesses with their own on-site generation and immediate, low carbon power. And though they work even on cloudy days, clearly, the amount of energy generated by a solar panel is heavily dependent on the prevailing weather conditions. That said, multiple additional factors have a bearing on it too. We are working to develop algorithms that accurately forecast an individual panel's energy production, detect any under-performance and allow measures to be taken which will improve solar power production. This will be a case of artificial intelligence being used to provide actionable insights into optimising power output and maximising return on investment.
The energy transition is well under way, and in a world of rapidly changing energy systems, artificial intelligence provides insight and delivers efficiencies in several aspects of production, distribution and consumption, helping businesses along the way. Algorithms and intelligent data are helping to deliver the transition to new energy systems, with benefits for businesses across several sectors.