How AI is transforming Energy sector
Artificial intelligence is expected to boost the efficiency of renewable energy resources by automating operations in the wind and solar energy industries. AI is capable of making the energy system more intelligent and flexible. AI algorithms can assist utility companies to understand and optimize consumer behavior and manage energy consumption across different sectors.
Complex machine learning algorithms merged with weather data from satellites and climate models can be used to forecast the electricity generated by renewable resources like wind, solar and ocean.
AI-based applications can create more opportunities for the energy sector by:
• Developing software applications to analyze large data sets to identifying patterns, detecting anomalies, and making precise predictions.
• AI integrated Smart applications can automatically make accurate decisions based on learning.
• Providing customer-centric solutions that understand new customer needs and provide solutions
• Using predictive analytics to improve equipment and predict downtime
• Leveraging blockchain to protect data.
Predictive Maintenance and Cognitive Vision
Predictive maintenance utility equipment, enabled by AI and IoT, prevents unforeseen equipment failure and also predicts wear and tear of equipment allowing timely maintenance that can extend the life cycle of complex and costly machinery. Predictive maintenance of machinery and equipment can ensure the safety of human crews who operate with massive equipment.
Autonomous Energy Production
The Renewable energy industry and global risk management consultants predict that one day wind and solar farms could produce energy without any human involvement. Autonomous mining plans its eventual operation without any human workers. Self-driving excavators and haulers will extract minerals from the 500-meter deep site. A 5G wireless network can connect all machinery and sensors to ensure high production.
Environmental and Safety Upgrades
Mining companies are leveraging IoT and machine learning to improve worker safety and environmental compliance using smart sensors. Wireless sensor networks ensure early detection of excessive vibrations that could lead to structural collapses and also detect the presence of dangerous combustible gases such as methane. Data collected by these sensors and workers’ wearable sensors are used to conduct site surveillance; which in minimizing future dangers.
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