Two cutting-edge technology trends are changing the energetic landscape

Keepler guides you on the journey to a more efficient and sustainable energy & utilities market

Advanced AI technologies in combination with IoT are the equation for automated maintenance, smart grids optimization, renewable energy generation, measuring carbon consumption in real time or improving the accuracy of energy consumption predictions and learning from historical events that could affect the energy market. Thus, resulting in proper management of energy consumption patterns and resources, which also promotes energy conservation practices for a sustainable future.

AI-enabled technologies facilitate the more sustainable and resilient grid of the future, which can accommodate a decentralised energy market and faster integration of renewable energy sources.

AI in the Energy Sector

The World Economic Forum predicts that AI will play an essential role in the world’s transition to clean energy.

The Internet of Energy

This new paradigm in energy infrastructure involves a significant level of automation in order to manage the new technology platforms as well as the financial framework required by markets to facilitate energy trading and distribution.

AI-based solutions for the Energy & Utilities sector

We can be your partner to advance your business with technology and data

Demand forecasting

Advanced AI technologies can predict how energy demand will change in the future. This information can be used to plan for the future and build the necessary infrastructure.

Predictive maintenance

Through the use of AI and ML technologies, energy companies can predict when a machine or equipment is likely to fail. This makes it possible to plan the replacement of critical and costly energy assets and avoid unplanned maintenance work, preventing unexpected outages and resulting in significant cost savings.

Fraud detection

Identification of potential fraud using Machine learning on hundreds of internal and external variables and unstructured data. This allows energy companies to protect their assets, reduce energy waste, and save money.

Grid security

The energy grid is a complex system vulnerable to cyber-attacks. AI and machine learning can be used to improve the security of energy grids by preventing cyber-attacks before they occur.

Smart grids

Grids can be integrated with sensors, energy storage systems, data analysis tools, energy management platforms and other types of energy technology to become ‘smart’. Data on energy use can be collected from each device on the grid and energy flow and use can be controlled in near real-time, reducing energy consumption through automated demand response systems, for example at peak times.

Customer engagement

Through the use of AI and machine learning, energy companies can provide customers with specific information about their needs. Through data analysis, it is possible to gain insights about their consumption and provide them with a more personalised service, for example, on how to reduce their energy consumption by changing their usage habits.

Accelaration of sustainability transition

Our future depends on clean, safe and sustainable energy. In the quest for sustainability, artificial intelligence plays an important role as it has a direct impact on the environment in terms of energy efficiency, increased integration of renewable energy sources and waste reduction.

We help our clients transform their business through data

“Keepler is helping us in achieving our digital transformation goals and having greater agility and resilience in our supply chains. We are delighted to work with them and leverage their deep expertise in cloud management to expand our global market presence and help our business address its key challenges. Keepler is being a very reliable and capable supplier, always understanding our needs and providing the right advice at the right time.”

- CIO, Energy Company -
ENERGY
CROSS
Big Data
IoT
SUCCESS CASE: Knolar

Multi-tenant IoT Platform for Real-time Analytics

The client wants a multi-client analytical platform for IoT events capable of storing and processing hundreds of millions of IoT events in real time, which allows users to define ingestion processes and consume the data in a simple and intuitive way.

ENERGY
CROSS
Big Data
ML
Cloud
SUCCESS CASE: IMAGINA ENERGÍA

Landing Zone and Data Environment for Analytics and Business Applications

The client is willing to run its operations in a new market, so they need to reduce time-to-market with the ability to scale rapidly as new crucial applications are deployed to capture market share.

ENERGY
Data Science
ML
Cloud
SUCCESS CASE: ENAGÁS

Anomaly Detection in Cryogenic Pumps

By observing abnormal behaviors in the data, maintenance can be scheduled before there has been a failure which would lead to loss of production and spoilage of gas.

Thinking about the future?

These are the goals for your business

Customer-centric strategies

Consumers want more information, easier access and greater autonomy, as well as sustainable power options. They want to be able to choose companies that understand their needs and offer tailor-made solutions. Advanced data analytics delivers big data insights into industry operations, directly impacting everything from demand forecasting to customer service.

Reshaping energy through AI

The energy market is increasingly competitive, with new challenges disrupting the status quo. Companies that implement AI will be better equipped to deliver the kind of transformative innovation needed to build sustainable and resilient energy systems, resulting in greater success and profitability.

AI as an accelerator towards sustainability

Technology has already proven its value in the renewable energy sector, bringing improvements in forecasting, grid operation and optimisation, coordination of distributed energy assets and demand management, as well as material discovery and innovation. AI as an intelligent layer in all processes can help the industry become more flexible and efficient, while reducing costs.

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