Artificial intelligence (AI) is fast becoming an integral part of multiple industries. In the sphere of electrical engineering, it’s driving numerous improvements, from the way components and machines are maintained to the monitoring and planning of power usage.
While many businesses are exploring the possibilities of generative AI and natural language processing/generation (NLP/NLG) for content and marketing, AI in electrical engineering is more analytical. This emerging technology could be transforming the way electrical engineers create systems and, in turn, those advanced systems and processes provide constantly learning AI algorithms with more data.
Predictive Maintenance Algorithms
AI and machine learning use algorithms and huge amounts of data to sift for patterns that provide actionable insights. One use case for this is creating predictive maintenance algorithms – the ability to know when maintenance should occur to prevent potentially costly downtime.
Taking machines or components offline to fix problems caused by wear and tear impacts business continuity. Yet if maintenance is done too often, this is costly and uses personnel hours unnecessarily. Electrical engineers can now create systems that offer a schedule based on historical data and predictions which can include taking peak periods or even market fluctuations into account. Deloitte recently reported that these AI-enabled predictive maintenance algorithms could increase equipment uptime by as much as 20%.
Fault Detection and Diagnosis Systems
As well as being prone to wear and tear, sometimes IT systems or machinery break down due to unexpected faults. AI can assess faults and use the vast amount of data it’s been trained on to accurately diagnose the problem and suggest a solution.
Currently, this system is already being used in power grids and systems across the energy sector with great success. It’s not always practical to send a human to diagnose every problem that occurs. Aspects of power grids are dangerous plus manually seeking a fault takes time. AI can quickly assess the problem and proffer the next steps engineers should take.
Optimization of Power Distribution Networks
Similarly, AI is also being used in power distribution networks to improve efficiency and reliability. This is a critical aspect of what’s known as the energy transition – the move away from dependence on fossil fuels. Optimizing existing power distribution grids means they use less energy and reduce their carbon footprint, representing a more eco-friendly power solution.
AI is also being used to help balance loads within renewable power systems. Wind and solar power both carry the challenge of fluctuating power gathering and conversion. Deep learning techniques help forecast when more energy will be flowing into power systems to help balance out supply and demand, storing energy where necessary to compensate for low-input days.
Autonomous Control Mechanisms
The energy industry impacts all businesses but it’s not the only sector benefiting from AI-powered electrical engineering concepts. The rise in automation is a huge factor in how AI is changing the way people work across multiple niches.
Take the food manufacturing industry, for example. Many foodstuffs require highly specific environmental controls to keep temperature or moisture levels in check. In the past, someone would have to be on hand taking regular readings and adjusting heat or humidity accordingly. Today, electrical engineers can create AI-powered systems that remotely and automatically make those changes and report the relevant data as needed.
Machine Learning for Energy Forecasting
Veering back to energy management, machine learning techniques also help manage and forecast power consumption for various businesses from manufacturers to data centers. Understanding how much energy a facility will use helps business leaders plan budgets, create more relevant utility partnerships, and report on their environmental impact.
This AI-powered energy forecasting is being scaled up to consider how larger facilities and even cities consume energy and the factors that impact this. For example, when AI can deliver accurate forecasts on how weather, local events, and transport will impact power usage across a geographical area, it can help power suppliers plan for a peak or low-usage period. Energy suppliers have always tried to plan around these factors but research suggests that AI reduces errors in these predictions by 39.8%.
AI in Electrical Engineering: Driving Performance
By embracing AI, electrical engineers are able to improve efficiency, reliability, and performance in systems for multiple industries. As AI evolves, systems could become more complex yet more accurate and reliable. There’s also the opportunity to utilize AI to make company systems more sustainable by reducing power and water usage or focusing on integrating renewables where possible.
For information about hiring the right talent to help you integrate AI into your company’s systems, connect with MRINetwork, and let’s see how we can help.