After several false starts over many decades, artificial intelligence (AI) has finally proven its worth in helping people achieve faster results and with greater success. Even utilities, which are generally conservative in adopting new technologies, are thoughtfully embracing AI to improve customer experience, operational efficiencies, and infrastructure planning through smarter grid management.

Imagine knowing exactly how much energy is consumed inside every home or business, down to the individual appliance and time of day.

Would your utility’s business managers be able to operate and plan grid infrastructure more efficiently?

Now, imagine knowing what type of appliance each home was using. Would your utility’s marketing team be able to target gas furnace customers for electric heat pump upgrades more accurately and cost-effectively, for example?

The capabilities of today’s AI algorithms are helping utilities do just this by illustrating with precise detail how energy moves along the grid. In just a few clicks, utilities can identify key power loads – including electric vehicle (EV) charging, solar, batteries, HVAC, and water heaters – detect behavior and appliance inefficiencies, track consumption history, filter consumption patterns by geography, income level, and more.

For the first time, utilities can draw meaningful insights from consumption data that lend to smarter decision-making for critical initiatives like time-of-use (TOU) load shifting, distribution planning, and demand-side management.

A Trusted ‘Knowledge’ Repository is Key to Generative AI Adoption in 2024

New in the Evolution of AI 

While AI has slowly been developing since the 1940s – and large breakthroughs in the first few decades brought us ELIZA, the first chatbot – it wasn’t until 2011 that the ‘smartness’ of AI started to pick up. Apple integrated Siri into its iPhones, IBM Watson won Jeopardy, and Google’s AlphaGo won the World Go Championship. All of a sudden, AI was on the map for tech and non-tech companies everywhere.

Current AI capabilities – which are considered ‘traditional AI’ – are being used in the utility world to connect what is happening on the grid with how utilities carry out top-level decision-making and day-to-day activities. For example, using AI to analyze power consumption on an hour-by-hour basis, network planners can optimize grid management by running capacity analysis on transformers, feeders, and substations. This enables them to identify areas of the grid that are being overloaded and which areas are capable of withstanding an increase in demand.

Another typical use is taking AI’s ability to detect appliance-level consumption patterns within each home to personalize customer communications that encourage behavioral energy efficiency. Specifically, utilities can send out usage alerts as a house comes close to crossing the high-bill threshold.

Because, AI can draw side-by-side comparisons of homes based on energy consumption, property attributes, individual lifestyles, and geography, utilities are also better positioned to establish benchmarks for initiatives like electrification and demand response. Some utilities are even using these AI-powered consumption insights to identify and prevent meter tampering and tariff misuse.

But the world is entering a new era with generative AI, which promises to be even more powerful.

When traditional AI algorithms see a picture of an elephant, they process the elephant as a whole and can describe it in intricate detail, capturing every pixel. When asked to draw the elephant, traditional AI algorithms will draw that elephant in nearly the same way again and again.

Generative AI, however, is more creative. These ML algorithms instead capture key features of the elephant to draw a different elephant each time.

This evolution brings incremental creativity to the problems around us – the type of creativity most of us have to apply on a day-to-day basis – and allows organizations to enhance operations from the top down without drastic restructuring of business models.

Although generative AI is still in its infancy in regards to scaled adoption and implementation, recent research from Capgemini Research Institute revealed that 33 percent of utility and energy companies worldwide have begun to pilot generative AI and almost 40 percent of utility and energy companies have established a dedicated team and budget for generative AI.

While Capgemini notes high-tech and industrial manufacturing as the two industries at the forefront of generative AI, it is perhaps more interesting to see industries notoriously slower to adopt technological innovations (like utilities) are supporting generative AI at the same pace as its tech-savvy counterparts.

Generative AI for Utilities

As impressive as Siri was when it was first introduced, generative AI is garnering that same admiration.

Generative Pre-trained Transformer 4 (GPT-4) passed the Bar Exam with flying colors, so it’s not difficult to imagine how applying this resource will continue to improve utility operations.

Traditional AI is incredibly valuable in analyzing data, which for utilities means appliance detection, consumption profiling, and everything that comes from this sophisticated energy intelligence like segmenting customers, sending automatic high-usage alerts, etc. With generative AI, utilities have the power to further amplify operations.

Implementation of generative AI within areas like call centers to reduce time spent on the phone with customers will be the most obvious and low-risk use of AI in the near term, it is also low-hanging fruit for most utilities. Where utilities will see the greatest impact overall, however, will be grid planning and management.

For instance, as utilities currently work to support rising EV adoption they rely on human input to determine their EV charging infrastructure strategy. This means planning when, where, and how many EV chargers to install at any given location. In the absence of much historical data (which is what traditional AI depends on), such a task requires significant amounts of creativity around human behavior as well as a firm understanding of how charging infrastructure is likely to affect EV purchases, demographic changes, and even real estate availability. This now is a task well suited to be tackled by generative AI.

Another great example is the use of generative AI chatbots by utility business managers, not just customers. Utility managers can interact with underlying big data in a natural language mode via initial questions such as, “Which five substations have the highest solar penetration?”

They can then dive deeper with follow-on questions such as:

“How many EV drivers are at this substation?”

“How many of those users have both solar and EVs?”

What are the time of use patterns for these users?”

With this, grid planners can easily identify areas of focus within minutes, rather than the weeks that it would have taken an analyst to download and analyze the data before answering these questions.

A Smarter Energy Future with Generative AI for Utilities

AI-powered consumption insights at this level can unlock considerable financial savings for utilities. EV load shifting can save utilities upwards of $1,350 per EV every year in bulk system investments and $1,090 per EV every year in distribution system investments – a financial benefit that compounds as adoption grows.

And, that’s just the beginning.

McKinsey Global Institute estimates AI and machine learning can drive $2.6 trillion to $4.4 trillion annually in business value. This could mean $200 billion in value add for the U.S. utility industry thanks to AI helping utilities avoid costly and unnecessary infrastructure upgrades, improving grid planning, reducing operating spending, and more.

While full-scale adoption of AI strategies may be decades away, the success of early pilot programs and projects proves there is no reason to wait. Utilities can start working more efficiently today – both in terms of employee workload and by uncovering new business opportunities – and build a stronger, more reliable grid.

[To share your insights with us, please write to sghosh@martechseries.com]

The post Decoding the Grid: A Practical Guide to Generative AI for Utilities appeared first on AiThority.

Artificial Intelligence has completely transformed industries across the board, and it’s hard to find one that isn’t benefitting from its capabilities. It’s not just about streamlining operations and cutting costs, but rather about establishing efficiency, improving timeliness, and empowering employees to focus on more crucial tasks. From the very beginning phase to the end user, AI is revolutionizing how we approach every aspect of oil and gas exploration – from exploration and development to production, transportation, refining, and sales.

According to the Oil And Gas Global Market Report 2023, big players in the oil and gas industry, like ExxonMobil and Shell, are jumping on the AI bandwagon, making significant investments in cutting-edge technology. They’re smartly using AI to centralize their data management and integrate it seamlessly across various applications. It’s all about streamlining operations and boosting efficiency.

Read: Big Shifts Make Data Essential to North American Oil Firms

But they’re not alone in this race. Sinopec, a Chinese chemical and petroleum giant, has taken a bold step by announcing its plans to construct ten intelligence centers. The goal? To slash operation costs by a whopping 20%! These companies are clearly seeing the immense potential of AI to revolutionize the way they do business and stay ahead of the competition.

This blog will delve into the current and future applications of AI in this field. According to a survey by EY, a whopping 92% of oil and gas companies are already investing in AI or have plans to do so within the next two years. The impact is undeniable.

Current Applications of AI and ML in Gas Exploration

Reservoir Characterization and Modeling

A crucial aspect of oil and gas exploration is understanding the reservoirs beneath the Earth’s surface. AI and ML technologies play a vital role in reservoir characterization and modeling, enabling engineers to make informed decisions. By analyzing vast amounts of data, including seismic information, well logs, and production data, these technologies uncover patterns and correlations that help in accurately characterizing reservoirs. Through predictive modeling, AI and ML algorithms simulate and forecast reservoir behavior, aiding in estimating reserves, optimizing production strategies, and mitigating risks.

Drilling and Well Optimization

Real-time Data Monitoring and Analysis: AI-powered systems continuously monitor and analyze real-time drilling data, including parameters like drilling rate, weight on bit, and torque. By detecting anomalies or abnormal conditions, these systems promptly alert drillers, enabling them to take immediate corrective actions. Real-time data analysis enhances drilling efficiency, minimizes downtime, and improves safety.

Also Read: Unleashing the Powerhouse: Unveiling the Mighty Role of AI in Energy Management

Automated Decision-Making: ML algorithms analyze historical drilling data to develop automated decision-making systems. These systems assist in selecting optimal drill bits, determining drilling parameters, and adjusting techniques based on rock formations. By streamlining the decision-making process, AI and ML optimize drilling operations, resulting in improved outcomes and cost savings.

Production Optimization

Predictive Maintenance: By analyzing sensor data and historical maintenance records, AI algorithms can predict equipment failures before they occur. This enables proactive maintenance scheduling, minimizing downtime, and reducing maintenance costs. Predictive maintenance also enhances safety by preventing unexpected equipment failures.

Intelligent Field Monitoring: AI and ML-based monitoring systems provide real-time insights into production fields. These systems analyze production data, monitor equipment performance, and detect potential issues. By identifying inefficiencies and optimizing production parameters, these systems enhance field performance, improve production rates, and reduce operating costs.

Environmental Impact and Sustainability

Environmental Monitoring: ML algorithms can process data from various sources, including sensors and satellite imagery, to monitor and detect environmental impacts, such as leaks and emissions. This allows for timely interventions and mitigates environmental risks.

Energy Optimization: AI and ML algorithms optimize energy usage in oil and gas operations, reducing carbon footprints and improving energy efficiency. These technologies identify opportunities for energy conservation, enable predictive analytics for energy demand, and facilitate the integration of renewable energy sources.

Future Applications of AI and ML in Oil and Gas Exploration

The future of AI and ML in oil and gas exploration holds immense potential for further advancements and transformative applications. Let’s explore some of the exciting areas where these technologies are expected to make a significant impact.

Advanced-Data Analytics and Machine Learning Algorithms

As AI and ML algorithms continue to evolve, they will become more adept at analyzing complex data sets in oil and gas exploration. Advanced data analytics will enable enhanced interpretation of seismic data, improving the accuracy of reservoir characterization and prediction of subsurface properties. Machine learning algorithms will refine their capabilities in pattern recognition and anomaly detection, enabling faster and more accurate identification of potential drilling targets and reservoir opportunities.

Robotics and Automation

The integration of AI and ML with robotics and automation technologies is set to revolutionize oil and gas exploration. Autonomous drilling and well intervention systems will be deployed, reducing the need for human intervention in hazardous environments. Robotic inspection and maintenance capabilities will be enhanced, enabling more efficient and cost-effective monitoring and upkeep of assets. This convergence of technologies will not only improve safety but also drive operational efficiency by minimizing downtime and optimizing maintenance schedules.

Also Read: Artificial Intelligence and Machine Learning Are Silently Saving Our Energy Grid

Predictive Analytics and Forecasting

AI and ML will continue to improve predictive analytics and forecasting capabilities in oil and gas exploration. With access to vast amounts of historical data and real-time information, these technologies will provide more accurate predictions of reservoir behavior, production rates, and equipment performance. This enhanced forecasting will enable better decision-making, optimizing field development strategies, and maximizing production efficiency.

Integration of Renewable Energy

As the industry focuses on sustainability, AI and ML will play a crucial role in the integration of renewable energy sources. These technologies will optimize the utilization of renewable energy in oil and gas operations, facilitating efficient storage and balancing supply and demand. By analyzing data on energy consumption, AI algorithms can identify opportunities for energy conservation and recommend strategies for reducing emissions.

From accurately characterizing reservoirs to optimizing drilling operations and enhancing production efficiency, these technologies offer unparalleled insights and opportunities for the industry. The future holds even more exciting possibilities, with advanced data analytics, robotics, and predictive capabilities leading the way. As AI and ML continue to evolve, their impact on oil and gas exploration will unlock new frontiers, revolutionizing an industry vital to global energy needs.

The post From Data to Discovery: The Role of AI and ML in Revolutionizing Oil and Gas Exploration appeared first on AiThority.

AI in Energy Management Market Research Report: By Type, Solution, Application, Technology, End User – Industry Opportunity Analysis and Growth Forecast to 2024

According to the market research report published by P&S Intelligence, the global AI in energy management market share was valued at $4,439.1 million in 2018, which is projected to reach $12,200.9 million by 2024, growing at a CAGR of 19.8% during the forecast period (2019–2024). On the basis of end user, the utility category held the largest market share in 2018. This is attributed to the fact that companies, such as Duke Energy Corporation and Dominion Energy Inc., have been actively deploying AI solutions for managing supply–demand balance and optimizing business infrastructure.

The integration of IoT in EMSs is observed as a major trend in the market. In EMS, IoT plays a vital role in delivering software automation, remote controls, proactive monitoring, and data insight services. It helps to display the energy consumption value with the help of smart meters and sensors at the machine and production line levels. IoT provides an integrated suite of software & services that helps in reducing the energy costs.

Read More: Technology Watch: Don’t Miss These CES 2020 Themes And Sessions

AI-enabled robots have the potential to revolutionize the cost structure and operations of energy companies, along with the reduction in risks and health improvement of the energy personnel. AI-enabled robots are capable of inspecting, certifying, maintaining, and repairing energy installation units. Further, the robots can be used in cleaning up and decommissioning of nuclear waste.

Globally, North America and Europe collectively accounted for more than 50% of the market share in the AI in energy management market in 2018. The regions are home to some of the top AI solution providers and the governments of top markets such as U.S. and U.K. among these regions, have been actively investing toward the deployment of AI solutions. For instance, in February 2019, the U.S. government launched the American AI Initiative program, wherein an amount close to $1 billion for AI research across all agencies is to be invested as reported by the U.S. Office of Science and Technology Policy.

The U.S. held the largest market share in the AI in energy management market. The U.S. is the present leader in AI technology, the U.S. pays a strong emphasis on the energy generated through renewable sources which is 18.2% of the total energy produced, hence the U.S. is deploying AI solutions in the energy sector to integrate renewables into the power grid. Further, China is projected to be the fastest growing market across the forecast period. This can be attributed to the large amount of energy consumption in China which stood close to 1500 GW in 2018. With such a heavy consumption China is heavily investing in AI solutions for integrating and supporting the management of energy produced from different sources, such as wind, thermal, hydro, and solar, into the power grid.

Read More: The Future of Fintech at CES 2020 with AI, Crypto, Threat Intelligence and So Much More…

The AI in energy management market has a fragmented structure, with the presence of a large number of market players. Some top players in the market are General Electric Company, Schneider Electric SE, IBM Corporation, ABB Ltd., Eaton Corporation plc., and Mitsubishi Electric Corporation.

In recent years, the major players in the AI in energy management market have taken several strategic measures, such as mergers & acquisitions, product launches, and geographical expansions, to gain a competitive edge in the industry. For instance, in March 2019, Smart Energy Water (SEW), a cloud platform provider for data on energy and water, entered into an agreement with IBM Corporation to leverage the IBM cloud for global deployment of SEW platform over a period of next five years. Under the agreement, SEW will access the scalability and flexibility of IBM cloud. Further, in June 2019, Schneider Electric, a global player in energy management and automation, acquired the majority stake in AutoGrid Systems Inc., a provider of management software for the energy industry. Schneider Electric will leverage AutoGrid’s flex and energy internet platforms to provide AI-driven solutions used in the energy sector.

Other key players in the market include Siemens AG, Alphabet Inc., Honeywell International Inc, Johnson Controls International plc and Xcel Energy Inc.

More Reports of ICT And Media By P&S Intelligence

Next-Generation Firewall (NGFW) Market

The Asia-Pacific (APAC) next-generation firewall market would register the fastest growth during the forecast period, owing to the increasing number of SMEs cyber threats, and strong focus of the governments in the region on data security.

Natural Language Processing Market

Globally, North America held the largest share in the natural language processing market in 2018. The market in the region is primarily driven by the surging IT spending and the presence of a large number of multinational companies deploying AI technologies to automate their business processes.

Read More: The Future of Work’s Most Crucial Component: Artificial Intelligence

The post AI in Energy Management Market Is Poised to Attain Valuation of $12,200.9 Million by 2024: P&S Intelligence appeared first on AiThority.