AI is an umbrella term for various software-based systems that use data inputs to make decisions on their own. There are strong AI use cases in the power industry. Many activities, from asset optimisation to customer segmentation, can be enhanced by AI. The past ten years have seen an explosion in the amount of data generated by power companies, mostly due to the rise of the Internet of Things (IoT). AI provides an opportunity to significantly increase performance using data collected from IoT devices. 

GlobalData anticipates that the global market for AI platforms in the power industry will grow at a compound annual growth rate (CAGR) of 24% over the forecast period. Large utilities will continue to develop their in-house capabilities, hiring machine learning and data science specialists, and more AI-specific start-ups will persist in growing the list of AI-focused partnerships in the power sector. Electricity trading, smart grids, and asset management represent primary growth areas for AI in the power sector. Machine learning, a key element of the AI value chain, will drive most of this growth. 

However, not all companies are equal when it comes to their capabilities and investments in the key themes that matter most to their industry. Understanding how companies are positioned and ranked in the most important themes can be a key leading indicator of their future earnings potential and relative competitive position.  

According to GlobalData’s thematic research report, Predictive Maintenance in Power, leading adopters include: Iberdrola, Enel, Électricité de France (EDF), Duke Energy, E.ON, Exelon, Schneider Electric, Dubai Energy & Water Authority (DEWA), National Grid and Southern Company.  

Insights from top ranked companies  

Enel 

Enel has deployed a predictive maintenance application at over 16,000 substations serving one million customers in Italy. It used sensors to compile data from several sources, including SCADA, maintenance work orders, historical equipment failures, and data on weather and terrain near the assets. Enel also built a machine learning algorithm to identify electricity theft and prioritise targets for field visits. Both of these machine learning projects were supported by C3.ai’s technology, which collects data from several sources, and provides much of the platform needed to develop the algorithms. In August 2017, Enel acquired EnerNOC for over $300m and renamed it Enel X. Enel X is now one of the world’s largest demand response aggregators, using a combination of energy efficiency and demand shifting across many energy users to avoid the need to build additional peak generation. Enel X’s system makes extensive use of machine learning. 

EDF 

EDF has an established programme using AI to create digital twins of its nuclear power plants in France. The initial data is collected by taking laser scans and panoramic images inside the power stations (an automated process). The data is processed, and the system can go through billions of pixels and identify over 30,000 components. This system used deep learning models to quickly identify the labels and save the company money and time. More than 100 experts from EDF, Framatome, and others are working on the project, which aims to structure the French nuclear industry’s innovation process through digital technology. EDF’s renewable energy subsidiary, EDF Renewables, has also been using machine learning models, in partnership with Bharat Power & Light, to analyse IoT data and develop predictive maintenance models for solar and wind installations. 

Iberdrola 

Iberdrola has made AI a core part of its digitalisation strategy, focusing on applying speech recognition in customer services, robotics, fraud detection, the analysis of complaints, and predictive maintenance. Iberdrola’s photovoltaic plants also use machine learning for the early identification of anomalies and automatic identification of faults. Iberdrola is a key partner in the ROMEO project, which uses machine learning to create effective predictive maintenance algorithms and ultimately reduce offshore wind energy costs. 

To further understand the key themes and technologies disrupting the power industry, access GlobalData’s latest thematic research report on Predictive Maintenance in Power.

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