H2O.ai Case Studies AES's Transformation in Energy Business with AI and H2O.ai

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	AES's Transformation in Energy Business with AI and H2O.ai H2O.ai

AES's Transformation in Energy Business with AI and H2O.ai

H2O.ai

Technology Category	Functional Applications - Computerized Maintenance Management Systems (CMMS) Sensors - Utility Meters
Applicable Industries	Electrical Grids Renewable Energy
Applicable Functions	Logistics & Transportation Maintenance
Use Cases	Asset Health Management (AHM) Predictive Maintenance
Services	Cloud Planning, Design & Implementation Services Data Science Services
Challenge	AES, a leading renewable-energy company, was faced with the challenge of accelerating their transition to renewable energy at scale. This business transformation necessitated a digital and AI transformation to better predict and optimize the energy output from renewable sources, predict failures, and optimize load distribution. The company had to deal with the complexities of wind-turbine predictive maintenance, energy bidding strategy for hydroelectric power plants, and smart meters. The maintenance of wind turbines, which have numerous moving parts subjected to harsh environments, was particularly costly and time-consuming. The company also needed to optimize its energy bidding strategy to maximize revenue from its hydroelectric power plants. Additionally, the company had to manage over one million smart meters, which sometimes had maintenance issues or were subject to misuse. Read More
About Customer	AES is a globally operating renewable-energy company that produces and distributes energy for private, public, and governmental organizations. The company has been named one of the World’s Most Ethical Companies for nine consecutive years and has won the Edison Electric Institute’s (EEI’s) Edison Award for contributing to the advancement of the electric industry. AES has successfully transitioned their business from fossil fuels to renewables and is accelerating this change with successful digital and AI transformations. The company runs numerous wind turbine farms, owns and operates numerous global hydroelectric power plants, and leverages more than one-million smart meters to monitor energy consumption. Read More
Solution	AES started by creating an AI strategy and then worked closely with their business partners to determine the best starting use cases. They selected the H2O AI Cloud to accelerate their ability to make state-of-the-art AI models and get them into production. For wind-turbine predictive maintenance, they built about a dozen models that provided greater than 90% accuracy. The initiative delivered cost savings and more consistent power delivery, leading to the addition of more sensors to multiple wind turbine components and automating oil sampling. For energy bidding strategy, AES used models made and operated with the H2O AI Cloud to help set the daily price and amounts for their power plants. For smart meters, the team created AI based on data from the smart meters to predict actual maintenance issues vs. misuse. The company is also expanding its predictive maintenance efforts to include imagery from drones to assess blade damage and work on solar farms to identify poor performing panels and cleaning schedules. Read More Log in to view content
Contents

Technology Category

Functional Applications - Computerized Maintenance Management Systems (CMMS)

Sensors - Utility Meters

Applicable Industries

Electrical Grids

Renewable Energy

Applicable Functions

Logistics & Transportation

Maintenance

Use Cases

Asset Health Management (AHM)

Predictive Maintenance

Services

Cloud Planning, Design & Implementation Services

Data Science Services

Challenge

AES, a leading renewable-energy company, was faced with the challenge of accelerating their transition to renewable energy at scale. This business transformation necessitated a digital and AI transformation to better predict and optimize the energy output from renewable sources, predict failures, and optimize load distribution. The company had to deal with the complexities of wind-turbine predictive maintenance, energy bidding strategy for hydroelectric power plants, and smart meters. The maintenance of wind turbines, which have numerous moving parts subjected to harsh environments, was particularly costly and time-consuming. The company also needed to optimize its energy bidding strategy to maximize revenue from its hydroelectric power plants. Additionally, the company had to manage over one million smart meters, which sometimes had maintenance issues or were subject to misuse.

About Customer

AES is a globally operating renewable-energy company that produces and distributes energy for private, public, and governmental organizations. The company has been named one of the World’s Most Ethical Companies for nine consecutive years and has won the Edison Electric Institute’s (EEI’s) Edison Award for contributing to the advancement of the electric industry. AES has successfully transitioned their business from fossil fuels to renewables and is accelerating this change with successful digital and AI transformations. The company runs numerous wind turbine farms, owns and operates numerous global hydroelectric power plants, and leverages more than one-million smart meters to monitor energy consumption.

Solution

AES started by creating an AI strategy and then worked closely with their business partners to determine the best starting use cases. They selected the H2O AI Cloud to accelerate their ability to make state-of-the-art AI models and get them into production. For wind-turbine predictive maintenance, they built about a dozen models that provided greater than 90% accuracy. The initiative delivered cost savings and more consistent power delivery, leading to the addition of more sensors to multiple wind turbine components and automating oil sampling. For energy bidding strategy, AES used models made and operated with the H2O AI Cloud to help set the daily price and amounts for their power plants. For smart meters, the team created AI based on data from the smart meters to predict actual maintenance issues vs. misuse. The company is also expanding its predictive maintenance efforts to include imagery from drones to assess blade damage and work on solar farms to identify poor performing panels and cleaning schedules.

Impact #1	The implementation of AI and H2O.ai has transformed AES's operations, delivering significant business value, increasing revenue, decreasing costs, and improving energy commitments and uptime for its customers. The use of AI has enabled AES to predict and plan maintenance, reducing costs and ensuring more consistent power delivery. The AI models have also helped AES to optimize its energy bidding strategy, increasing energy revenue and supporting a reliable carbon-free grid. The use of AI in managing smart meters has eliminated non-essential technician trips, saving time and money. Furthermore, AES has been able to solve over 85 key business challenges across every line of business within just two years, demonstrating the transformative power of AI in the energy industry.

Impact #1

The implementation of AI and H2O.ai has transformed AES's operations, delivering significant business value, increasing revenue, decreasing costs, and improving energy commitments and uptime for its customers. The use of AI has enabled AES to predict and plan maintenance, reducing costs and ensuring more consistent power delivery. The AI models have also helped AES to optimize its energy bidding strategy, increasing energy revenue and supporting a reliable carbon-free grid. The use of AI in managing smart meters has eliminated non-essential technician trips, saving time and money. Furthermore, AES has been able to solve over 85 key business challenges across every line of business within just two years, demonstrating the transformative power of AI in the energy industry.

Benefit #1	$1M Annual Savings by eliminating non-essential technician trips
Benefit #2	10% reduction in power outages faced by customers
Benefit #3	Greater than 90% accuracy in predicting wind turbine component failures

Benefit #1

$1M Annual Savings by eliminating non-essential technician trips

Benefit #2

10% reduction in power outages faced by customers

Benefit #3

Greater than 90% accuracy in predicting wind turbine component failures

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Overview

AES's Transformation in Energy Business with AI and H2O.ai

Operational Impact

Quantitative Benefit