As compared to natural intelligence (NI), which describes the human capacity to perform both daily and complex tasks, artificial intelligence (AI) instead describes the completely autonomous behavior of computer systems. Equipped with the sensor technology to determine tasks that need to be performed, as well as any maintenance requirements, AI systems have become a routine technology that is incorporated into almost every device that we use and operate each day.
Current advancements in AI have allowed for the exceptional perception and cognition abilities of systems like Siri, Alexa and Google Assistant to recognize our voices and immediately provide us with a wide range of information.
The ultimate goal of factories is to mass-produce their products at a rapid speed while simultaneously minimizing production costs as much as possible. Innovative manufacturers have already implemented AI systems into their daily tasks in a number of ways, thereby allowing for factories to meet their global demands at an economically beneficial rate.
By performing heavy lifting and tedious tasks, robotic systems used in assembly lines have already shown to prevent a number of work-related injuries for factory workers, while also increasing the effectiveness of their output as compared to their human counterparts.
The highly sensitive sensors of factory AI systems allow this equipment to immediately recognize any potential problems within the system and transmit that information to human operators allowing for real-time maintenance to occur. By eliminating service engineers needed to periodically check factory equipment, companies are able to assess an issue before any problem in production even occurs. The economic advantages associated with the implementation of AI systems in factories can not only be seen in an immense reduction of labor costs, but also with the extremely efficient nature of these systems to diagnose a problem before it even occurs.
The renewable energy industry heavily relies upon sensor technology that is incorporated into solar and wind cells to inform individuals of any maintenance requirements during their active use. As sensor technology continues to advance each day, renewable energy manufacturers expect to see an increase in the machine learning (ML) tools incorporated into these systems to heighten their ability to monitor, process and analyze daily tasks without any possible disruption to the system.
ML is a branch of AI that describes a machine’s ability to self-regulate in order to improve its own performance without requiring human direction or interference within the system to do so.
Over the next several years, the solar and wind energy industries expect to see AI systems applied in a variety of ways, some of which include:
While these are just a few of the many speculated advancements that AI systems will produce in the solar and wind industries, the ultimate reductions in costs and improvement in production rates are unimaginable. As of right now, each of these proposed advancements have yet to be applied within any industry, let alone for solar and wind energy purposes.
Researchers are hopeful that AI systems will not only significantly reduce production costs, but will also improve the reliability of these energy systems so that their global use becomes a more practical and feasible technology.
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