Artificial intelligence and machine learning are rapidly transforming the utilities industry; energy, gas, water and waste management rely on smart devices for optimization of infrastructure and the supply and demand balance. Now smarter utilities like a whole ecosystem of technology driven, sophisticated marketplaces are emerging. They have a lot of things to gain from the use of ML. The energy sector and smart power grids will be beneficial from recent advances in ML and AI.
The energy sector and the infrastructure it relies on is incredibly complex. As a result, it’s often plagued by maintenance issues, system or equipment failures, and management challenges that can be caused by a variety of factors including inclement weather, surges in demand, and misallocation of resources. In fact, it has been estimated that as much as 86% of energy on the US grid is wasted due to overloading and congestion.
Energy grids provide a treasure of valuable data, much of which can help operators triage issues as they arise. However, collecting and aggregating this data is a significant challenge given the high volume of information constantly passing through the grid. Think about the signals coming from billions of pieces of equipment and from millions of sources across the grid. It’s an incredibly difficult task for operators to keep up with this flood of data, which can often lead to missed insights that can cause malfunctions, or worse,outages.
Properly collecting this data is only half the challenge. Once it is collected and organized, making use of it is a consistent headache for data scientists. A diverse group of algorithms must be built to uncover the insights needed to ensure grids run efficiently. From there, they must be constantly maintained to guarantee accuracy, which requires a significant investment in time and resources for those involved.
Like many other business applications, control the power of ML to automate processes within data management can provide significant benefits for the energy sector. Some of the most applicable applications include: Predicting failures. With the right algorithms in place, operators can better predict grid failures before they reach the customer. As a result, energy companies can avoid customer dissatisfaction and the corresponding financial losses that come with it.
Energy grids cover massive sections of the country and can often be hit with different weather scenarios at the same time high winds in one area, lightning strikes in another, heavy rain in entirely different region. Being able to automate the intake of maintenance signals and predict where maintenance may be needed enables operators to prioritize work, save money, and reduce downtime.
The energy sector is rapidly adopting ML capabilities to automate the way the grid is operated, creating new demands on development teams. To accomplish their goals and to stay on pace, developers need fast and easy access to ML capabilities. They can’t afford to sink weeks into building the code and architectures required to make automation possible.