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Digitization is the future, and it’s the next wave that driving disruption in every sector. According to Capgemini’s recent research, governments can save $237 to $831 spent on energy generation and utilization. It’s no doubt that renewable energy is the future source of energy consumption. And the increasing demand and less availability of renewable electric stations and the low volume of renewable energy production are the biggest fears. Integrating AI in the energy and utility sector can help in better-measuring consumption metrics and making preventive decisions for the future.
Integrating the concept of artificial intelligence, machine learning, pattern recognition, and automation has seen development in energy. AI is not just facilitating the hosting of new products and services, indeed, it is lending more efficient and effective utility operations by analyzing unstructured data, which is 80% in total. In this article, we constitute a few examples of AI and prominent practices in renewable energy management, demand management, and infrastructure management.
How do AI-powered Automation systems help businesses in energy and utility?
With an increasing number of organizations showing interest and integrating intelligence, automation offers the significant potential of technology in energy and utilities. Here are a few examples of how we can embed technology to drive potential results.
1. US-based energy and gas utility company embedded sensors on the wind turbines to collect and store data regarding pressure generated by air at distinct periods. Using artificial intelligence and predictive analytics, the companies estimated the amount of electricity needed to generate for the future using historical data records. And further based on these predictions, they saved the costs to $60 million.
2. AI being deployed to the operation and maintenance of energy generation sources. One of the successful applications of the neural network model in the maintenance of gas turbines. By altering the gas in the turbine burner this model minimized the emission of nitrogen oxide and increased efficiency.
The role of AI in the Energy and Utility Sector
The emerging AI applications in energy efficiently transform the management of systems by digitally transforming how we forecast energy usage and maintain generation management. The top three areas are;
1. Renewable energy management
There are several AI pilot projects and usecase when we talk about short-term energy forecasting and improving equipment management. And in common wind turbine operations, solar panel sensor data are analyzed using AI to gauge the sunlight intensity. This further helps energy units in estimating the lifetime value of that resource.
2. Energy demand management
AI-backed demand management solution with a series of platforms that primarily focused on managing the demand response of different devices that run in parallel. The efficiency of AI systems enhanced user behavior to optimize energy consumption by running the feedback on energy performance in buildings and solutions that gauge to learn and expect future consumption patterns.
3. Energy and utility infrastructure management
The disruption and advancement in AI and digital asset management tools, now helping us in leveraging machine learning algorithms to collate, compare, analyze, and highlight risks and opportunities across utility infrastructure to scale power generation companies.
The continuum of artificial intelligence and machine learning adoption will change the way we measure and monitor our energy consumption needs. The amalgamation of next-gen pilot projects and use cases is defining new techniques to restore non-renewable resources for the future. And with the technology serving the needs of future consumption, energy and utility infrastructure management can store infinite opportunities for humankind. If you resonate with our article, please share your thoughts with us.
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