Artificial intelligence (AI) is officially transitioning from niche to trend to mainstream, with most businesses accepting—even embracing—its usefulness to improve operational efficiencies. Now, the next generation of AI is already here: generative AI, or GenAI.
Unlike traditional AI, which is trained to analyze data and make predictions within preset parameters, GenAI is further capable of creating entirely new data in all types of formats (text, images, music, videos, and more). Where traditional AI lets you ask Alexa to play your favorite hip-hop song, GenAI can re-create that song with the same lyrics but to the tune of, let’s say, Beethoven’s greatest symphony.
While that’s an amusing application of GenAI for entertainment, GenAI also has the potential to impact millions of people around the world by enhancing the one thing that supports our day-to-day lives: energy.
From AI To GenAI In The Energy Industry
Since it takes years of data collection and training to fuel GenAI, it’s important to first look at what traditional AI is doing in the energy industry today. Similar to other industries, AI has a foothold in customer-facing interactions like customer service chatbots. AI’s greatest impact, however, arguably has been helping utilities realize the full benefits of smart meters.
With nearly 120 million smart meters deployed across the U.S., smart meters make up a distributed network of intelligence already connected to the grid—and provide a treasure trove of data for AI to analyze and learn from.
Through smart meters, AI introduced the ability to disaggregate consumption data, which allows utilities to analyze data at the individual home level (and even appliance level) rather than at the standard neighborhood level. Utilities, as a result, are turning smart meter data into powerful insights that drive key initiatives—particularly load forecasting and grid planning, fuel switching and appliance upgrade programs, and theft detection.
Like most AI integration across industries, this new capability was originally a nice-to-have function. Now it’s essential and in nearly every new data analytics request for proposal (RFP).
‘Bolt-On’ To ‘Built-In’
Currently, AI is being layered onto data once it’s extracted from the smart meter (hence “bolt-on”). Now, some progressive meter companies are offering AI analytics features built into the meter itself.
Aside from the obvious benefit of not requiring customers to participate in surveys or install monitoring hardware, utilities gain AI-powered insights in much more real-time. This could include, for example, inside-the-meter monitoring of rooftop solar generation across the grid for a real-time view of supply versus demand.
What are your thoughts on the transition from traditional AI to GenAI in the energy sector? How do you think this evolution will shape the future of energy management and consumption? Let's discuss!
Read the article: https://www.forbes.com/sites/forbes...ding-the-hype-around-genai-marketing-edition/
Unlike traditional AI, which is trained to analyze data and make predictions within preset parameters, GenAI is further capable of creating entirely new data in all types of formats (text, images, music, videos, and more). Where traditional AI lets you ask Alexa to play your favorite hip-hop song, GenAI can re-create that song with the same lyrics but to the tune of, let’s say, Beethoven’s greatest symphony.
While that’s an amusing application of GenAI for entertainment, GenAI also has the potential to impact millions of people around the world by enhancing the one thing that supports our day-to-day lives: energy.
From AI To GenAI In The Energy Industry
Since it takes years of data collection and training to fuel GenAI, it’s important to first look at what traditional AI is doing in the energy industry today. Similar to other industries, AI has a foothold in customer-facing interactions like customer service chatbots. AI’s greatest impact, however, arguably has been helping utilities realize the full benefits of smart meters.
With nearly 120 million smart meters deployed across the U.S., smart meters make up a distributed network of intelligence already connected to the grid—and provide a treasure trove of data for AI to analyze and learn from.
Through smart meters, AI introduced the ability to disaggregate consumption data, which allows utilities to analyze data at the individual home level (and even appliance level) rather than at the standard neighborhood level. Utilities, as a result, are turning smart meter data into powerful insights that drive key initiatives—particularly load forecasting and grid planning, fuel switching and appliance upgrade programs, and theft detection.
Like most AI integration across industries, this new capability was originally a nice-to-have function. Now it’s essential and in nearly every new data analytics request for proposal (RFP).
‘Bolt-On’ To ‘Built-In’
Currently, AI is being layered onto data once it’s extracted from the smart meter (hence “bolt-on”). Now, some progressive meter companies are offering AI analytics features built into the meter itself.
Aside from the obvious benefit of not requiring customers to participate in surveys or install monitoring hardware, utilities gain AI-powered insights in much more real-time. This could include, for example, inside-the-meter monitoring of rooftop solar generation across the grid for a real-time view of supply versus demand.
What are your thoughts on the transition from traditional AI to GenAI in the energy sector? How do you think this evolution will shape the future of energy management and consumption? Let's discuss!
Read the article: https://www.forbes.com/sites/forbes...ding-the-hype-around-genai-marketing-edition/