Author: By Larry Pearl Published: June 28, 2019 Utility Dive
With all of the press that neural networks have been getting recently, you may be asking yourself, “What is a neural network, and should I be intrigued or scared?” A neural network is a form of artificial intelligence (AI) that is loosely modeled after the human brain, and it can help solve real-world problems in the energy sector and beyond. Whether it’s a threat or salvation depends on how it’s used.
In the 1990s, after the last AI hype cycle, a popular way to thumb one’s nose at AI was to point out that neural models could generate a 24-hour weather forecast that is more accurate than a meteorologist — it only takes 48 hours on a supercomputer to do so.
But over the past few years, AI and Machine Learning (ML) have become a potent force, not just because of their ability to process enormous amounts of data but also because they’re cheap. The impact is real — analyst firm Gartnerestimates the global business value derived from AI will reach $3.9 trillion in 2022 — but we are also just beginning.
The promise of advanced analytics in the energy sector
Currently, the entire power industry is leveraging only a small fraction of its data. It’s not that the data is unusable, but it requires very specific expertise to consume and gain insights. Overhyping AI/ML, particularly neural networks, fails to recognize how a versatile AI/ML platform integrated with domain expertise and focused on edge-to-Cloud applications can be. Such platforms are driving value today and are poised to drive a lot more in the future.
Recent advances in neural learning have moved whole new categories of value-add business applications, and utilities are recognizing the need to harness and leverage their data. According to a recent Accenture study of utilities executives, “99 percent believe that AI will be used routinely in decision support in the control room and in network planning by 2025.”
But too much focus on neural risks leaves value on the table. I instead advocate for a “combined arms” approach, attained by applying a broad and integrated set of AI/ML techniques — neural, heuristics, first principles, multivariate predictive control, etc. — informed by human knowledge.
For more than 20 years, utilities have used solutions to take action on power plant floors — moving critical variables of core processes, environmental compliance and KPIs in all aspects of fossil generation.
Deep learning, a recent neural advance, leverages a utility’s data and external data to anticipate outages and deploy response teams to quickly restore power and predict when systems will need repairs. This approach in turn drives faster storm recovery and better personnel and inventory management and reduces cost.
These seemingly very different applications are supported by a common, robust, integrated, industrial AI/ML platform.
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