By using AI in data science has made complex data easier to manage in existing energy generation power projects, this has given companies time and R&D capacity to discover new energy sources like tidal energy… and enable better usage of their existing infrastructures.
AI Applications in the Energy & Utilities Industry.
AI is proving to be a real enabler for these energy projects offering multiple applications. These include production optimization with computer vision to analyse asset utilization, reducing downtime for predictive maintenance of equipment, capacity understanding, and modelling for predicting corrosion risks on Offshore Wind turbines to reduce maintenance costs on Power Plants.
10 notable use cases and application areas are enlisted here:
#1 Optimizing Energy Production and Scheduling
Cost and schedule overruns are a problem for building offshore wind farm projects. This can be partly attributed to weather delays, resource and product limitations, and scheduling risks. The complexity of the problem further increases because of platform installation, fishing and environmental restrictions, Government & Local Authority regulations…. It thus becomes imperative to find robust project planning and scheduling models that consider these interacting components and associated risks to offshore windfarm projects.
For instance, an AI-based application enabled operators to pre-empt turbine blades, generators, and gearboxes failures on wind turbines whilst optimizing energy production. Cloud-based platforms provide offshore operators with access to advanced analytics software featuring AI algorithms that analyse incoming data for anomalies, ultimately signal trouble ahead in the monitored equipment.
#2 Asset Tracking and Maintenance/Digital Twins
Asset management, including its monitoring and maintenance, project planning, and lifecycle management, is one of the most critical areas where digital twin (DT) technology can play a crucial role. In such a scenario, Digital Twins enable Energy & Utilities companies to address challenges, including production imbalances, rapid changes in global economic conditions like the COVID-19 pandemic, and equipment reliability issues. To be responsive in these busy and somewhat chaotic times, Energy & Utilities companies need systems with real-time visibility and flexibility provided with digital twins technology, a bit like scenario planning for machines in the digital age!
#3 Defect Detection
One of the challenges that the Energy & Utilities companies encounter is detecting suspect pipes/wiring/machines or defects in fault-susceptible processes. Defects noticed at the end of the energy production line from sub-standard wind turbines ensue considerable losses to the energy companies, owners of turbines, turbine manufacturers and for budget resources.
To this end, AI can help validate production quality and provide deep insights into defects in analytics. AI-powered Defect Detection solutions are cost-effective and are extremely economical in comparison to the fundamental processes. Pattern recognition utilizing deep learning allows video streams recorded with cameras to alarm if an employee is not adequately dressed for the set of operations. Moreover, predictive analytics alarm the operators on the equipment’s health state, enabling pro-active actions to prevent a catastrophe with the consequences to health, safety, and environment.
#4 AI-led Cybersecurity
Many Energy & Utility – Oil & Gas organizations have endured security compromises. PwC’s Global State of Information Security Survey revealed that 42% of energy enterprises admitted being victims of phishing attacks. Unprecedented hack of Ukraine’s Power Grid in 2016 is a harsh reminder of what can happen to the European Power Market and their lack of Cybersecurity at the time. The rise in the number of physical and cyberattacks and its security spending have necessitated the need for artificial intelligence tools to encrypt the working system into their enterprise’s security. Video cameras as sensors help in monitoring the security threats in the utilities all day. When it is combined with software, the utilities get secured to every endpoint.
#5 Workplace Safety
Operations on the offshore energy market pose risks for personnel as it involves heavy equipment. Some trends, challenges, and scenarios for the future, points out that many IT systems based on deep learning help the safety officers spot safety protocols infringements. Operations at energy power stations and wind turbines pose risks for personnel as it involves heavy equipment, non-covered rotary equipment….
#6 Emission Tracking
Several Energy companies have already set net-zero emissions targets. Despite the economic challenges, many companies are working towards decarbonizing their operations and value chains. BCG studies state that the potential overall impact of applying AI to corporate sustainability amounts to $1.3 trillion to $2.6 trillion in value generated through additional revenues and cost savings by 2030. Energy & Utility producers also are deploying AI software to keep track of the volumes of fugitive emissions of greenhouse gases that escape from pipelines and energy equipment; the better to control them.
#7 Logistics Network Optimizations and Logistics
The supply chain in certain sectors of the energy – oil & gas industry are complex operations involving decision nodes such as, environmental recycling companies and gas producers/distributors for LPG… purchase, purchase price, petroleum refining operations, gantry operations, and transportation to the retail sale of end products can be complex. In this business, AI helps coordinate the operations team with the warehouse to ensure the availability of crucial products like the refill tanks.
In these LPG businesses, AI can support proper planning and execution, optimal route selection, etc. In contrast, it helps refiners plan optimal blending, forecasting the demand, estimating prices, and improvising customer relationships also typical in the downstream oil & gas business. In a nutshell, AI aids Energy & Utilities companies in the prediction of the market price of electricity/gas, proper planning and scheduling, enabling optimization of the energy price, creating a smart warehouse, maintenance of inventories, handling shipping operations for replacing assets, risk hedging, and improved delivery times & reduction in overall costs.
#8 AI Led Inventory Management
When inventory lags demand, companies suffer losses. AI helps ramp up efficiencies in network planning and predictive demand, allowing merchandizers to become more proactive. As Energy & Utilities companies gain more and more visibility into the demand patterns, they can plan for change, like with new driving habits, for example adjusting the number of vehicles recharging points and direct customers to nearby locations where recharging points are not in use. This leads to happier customers and lower operational costs.
#9 Optimized procurement
AI-driven specialized procurement solutions can help firms build interconnected digital supply networks (DSNs), enabling dynamism, flexibility, and efficiency in their planning and execution. AI can augment procurement experts’ decision-making capabilities with additional insights from data crunching and analysis of extremely complex and large sets of data to solve traditional problems.
Leveraging an AI-based solution can alleviate some of the current challenges in Energy procurement by helping firms in understanding major procurement spend categories; automate purchase-to-pay; identify critical and noncritical supply chain bottlenecks; and gain visibility into planned and actual figures by the supplier, material, geography, and other company-specific dimensions, to name a few.
#10 Reducing Equipment Downtime
Unplanned downtimes cost millions of dollars in a single day to offshore Wind Farms platforms in the event of critical asset failures!
For instance a whitepaper by World Economic Forum for the oil & gas industry states that 92% of refinery shutdowns were due to unplanned maintenance, costing those companies an average of $42 million a year to $88 million a year in the worst-case scenarios.
AI is a promising technology slated to play a key role in the future of Energy & Utilities. Sensor-rich utility business are already cashing on the immense opportunities leveraging the data analytics from the big-data engines. Energy & Utilities field personnel are already connected with mobile devices that have infiltrated our daily lives. Combining these technologies will result in a connectivity revolution especially for water companies finding water leaks etc, and AI will prove to be a great enabler.
Considering the AI’s potential to augment or even replace some human competencies, it’s not a surprise that a recent survey points that 92% of Energy & Utilities companies are either already invested in AI or will do so in the next two years.
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