The use of AI in energy operations has picked up the pace in recent years and is now moving beyond back office automation
It’s no secret that participating in energy markets and developing strategies requires processing massive amounts of data whether generating, selling, or buying power. Consider a trading and marketing company like DTE, Exelon or Vistra who participate in nearly all markets in North America and beyond.
To turn data into insights that can improve productivity and cut costs, energy players — from startup renewables companies to major utility giants — are turning to artificial intelligence (AI) to improve the accessibility and efficiency of the energy market. The use of AI in energy operations has picked up the pace in recent years and is now moving beyond back office automation toward projects that generate useful information for front office. Using base technologies like big data, cloud, and machine learning, AI can create a smarter (and a more streamlined) energy operation in three key ways:
Lessons learned from yesterday, last week, last month or even last year can shape the daily decisions made by traders.
Forecasting load, prices, wind speeds, cloud cover or any numerous weather metrics are all possible with the right machine learning and AI technology. The best part, once the technology has monitored these trends or “lessons,” it can then provide strategic, fast, analytical decisions based on learned history. The machine can retain longer, calculate faster and make better decisions from the past and apply it to the future.
For energy traders this is great news. The correlations between these metrics and trading strategies can be developed for the front office while the automation and learning of common discrepancies is a time saver and dollar generator in the back office.
Traders come and go, but AI is stable.
Unreliable manual processes cut into performance and profitability. So many times we see companies scrambling to regroup after an energy trader on their team has parted ways. It can cost more time than any energy trader has on their hands – especially during the summer.
However, transitioning between traders and experience levels is more efficient with the right AI technology in place. By adding AI applications to streamline front and back office applications, energy companies are enabling stable learning solutions that understand evolving customer needs while making automatic recommendations.
Back office lessons should be used to shape front office decisions.
The complexities in energy trade and management, especially for renewables in today’s volatile markets, requires a robust, end-to-end integration to better forecast and predict intermittent resources. By connecting the back office to the front office to further intelligent trading strategies is the holy grail and it is all made possible with predictive analytics and correlations analyzed through AI and machine learning.
At Adapt2 Solutions, we strongly believe in creating a single platform for all ISOs for front and back office functions enabled by AI to manage the complexities of contracting, operating, interfacing and settling renewable generation. For us, this is the best way to use, produce, and maximize your energy assets.
Artificial intelligence is becoming more critical than ever with the ability to boost efficiencies across the energy sector by automating operations in the solar and wind industries as well as allowing utilities to launch new business and service models. In this new world of digital transformation, forward-looking energy traders are leveraging automation to accomplish what their competitors are still performing manually. It’s no longer plausible to ignore the power of AI and what it can mean for your front and back office operations, creating a more advanced ISO market operation.
Jason Kram is the executive vice president at Adapt2 Solutions and has over 25 years of experience in the power industry. He holds multiple degrees in engineering from North Dakota State University and an MBA from the University of Houston.
