Opportunities for Decision Automation in the Energy Field

Automation can improve energy production and distribution, allowing for better renewable energy integration and forecasting, and optimizing energy production and consumption.

The energy industry is a billion-dollar market facing a range of challenges that include rising demand, fluctuating prices, power outages, insufficient grid infrastructure and the need to transition to a low-carbon future. As the demand for sustainable and efficient energy solutions continues to grow, companies must be able to make smarter, faster and cost-effective decisions to stay ahead of the curve.

Enters decision automation—a powerful tool that leverages machine learning/artificial intelligence (ML/AI) and data to optimize the energy supply chain, reduce costs and ultimately improve reliability. We’ll study more of this in this piece and focus on the most promising applications of decision automation in the energy industry.

What is Decision Automation and Why?

Decision automation involves the use of algorithms and machine learning to analyze data and make optimal decisions based on predefined rules and parameters.

In the energy industry, you can see this as a better approach to eradicate inconsistent human decisions and provide reliable decisions in an industry characterized by complex decision-making processes involving multiple stakeholders, data sources and random variables.

The reason for this development is not far-fetched. A new wave of intelligent automation technology has pushed enterprise automation to a tipping point. According to this report, the global market for intelligent process automation was valued at USD $9.52 billion in 2021 and is projected to reach USD $37.63 billion by 2030 with a Compound Annual Growth Rate (CAGR) of 16.50% from 2022 to 2030.

This development means that organizations can create significantly better decisions and outcomes for clients while also driving efficiency. It also implies good news for employees as it helps reduce work-related stress, promotes productive work and ultimately results in quality outputs.

The Role of AI in This Disruptive Industry

This research article explores three main areas of disruption driving transformation in the energy field: the data revolution, a switch to cleaner energy and a complex new business model.

The data revolution is characterized by an explosion of available data and multiple sources of data. The switch to cleaner energy is happening as the world moves away from fossil fuels like coal toward renewable sources like wind and solar, and the complex business model presents new challenges for power and utility companies, including increased competition and market volatility.

This is where AI comes in to save the day. AI can help to:

• Manage the unpredictable nature of renewable energy sources and optimize power generation efficiency
• Manage supply and demand by creating new energy trading capabilities, self-learning through prediction models, and short- and long-term forecasting of spot prices based on various factors
• Optimize power generation by analyzing large amounts of data from various sources, such as weather patterns, electricity demand and energy storage levels.

How Decision Automation Can Positively Impact the Energy Industry

Before delving into a list of things where decision automation can likely excel, it’s nice to see AI as a complementing factor. Some decisions may definitely require human intervention and in some cases, it’s an end-to-end automated AI decision-making process.

Energy Trading

Decision automation can be used to optimize energy trading platforms, which allow consumers and producers to buy and sell energy directly. Automated systems can analyze market trends and adjust pricing strategies accordingly. Automated decision-making can also be used to manage transactions in real time, ensuring that energy is exchanged efficiently and securely.

Energy Management

Decision automation can optimize the energy demand and supply balance by analyzing real-time data from smart meters, weather forecasts and other sources. By predicting energy demand patterns and adjusting supply accordingly, energy companies can reduce energy costs, minimize waste and increase the use of renewable energy sources. Energy management systems can also enable demand response, allowing companies to adjust demand in response to changes in supply or prices.

Grid Management

Decision automation can optimize the performance of the energy grid by predicting demand and supply imbalances, managing voltage and frequency fluctuations, and detecting and isolating faults. By automating grid management processes, energy companies can improve the reliability and resilience of the grid, reduce the need for manual intervention, and increase the use of renewable energy sources.

Asset Optimization

Decision automation can optimize the use of energy assets, such as power plants, wind turbines and solar panels, by analyzing real-time data on their performance, weather conditions and other factors. By predicting performance issues and adjusting operating parameters accordingly, energy companies can improve efficiency, reduce costs and increase the use of renewable energy sources. Asset optimization can also help companies to identify opportunities for asset replacement or upgrades.

Challenges and Benefits of Decision Automation in the Energy Field

John van Vliet, Content Writer at ClearVUE.Business, collated the inputs of data specialists to arrive at how automated decisioning can change the industry’s look:

“Decision automation can significantly impact the energy industry, resulting in lower carbon emissions, reduced energy bills and increased efficiency. It also has the potential to fill in supply chain emissions data gaps. Integrating sustainability key performance indicators into decision-making processes is critical for optimizing energy efficiency in businesses. For example, by utilizing energy management technology and implementing energy efficiency improvements based on automated data readings, one company achieved a projected yearly savings of £87,052 on its energy bills, reduced its energy consumption by a projected 189,070 kWh, and cut its CO2 emissions by 55,660 kg—all from a few operational adjustments and dependent on consistent and effective energy reduction behaviors.”

van Vliet also highlighted the risk of automated processes and artificial intelligence systems being built on faulty data:

“Predictions based on inaccurate data will lead to poor decision-making on the human end. The human-in-the-loop concept makes a strong case as the remedy to AI overreliance. Human-in-the-loop (HITL) also refers to the need to have a certain degree of human supervision in fields where errors can cost much more than just profits. While intelligent automation, where AI and automation join forces, serves simple situations like collating energy and carbon data for energy and climate-impact disclosures, prioritizing energy consumption during emergency situations, for example, is a fluid and complex issue that requires human decision-making.

As it concerns the world of energy management for businesses, HITL is crucial. Today’s most advanced energy management systems can reveal energy wastage and excess carbon emissions in seconds through the automated collation and calculation of energy data. However, they cannot determine how energy consumption should be redistributed to mitigate energy bills or diminishing carbon footprints. Human decision-making and intervention are cherished and demanded in the energy field. An energy manager or a sustainability consultant, for example, can steer decision-making that maximizes human well-being and business success when informed by granular energy data."

Concluding Thoughts

The energy industry faces the challenge of providing affordable, stable and sustainable energy for a growing population while transitioning from fossil fuels to renewable sources. To achieve this, data, digitization and automation will play critical roles. These technologies can improve energy production and distribution, allowing for better renewable energy integration and forecasting, as well as optimizing energy production and consumption.

A key factor in the adoption of decision automation is the availability of appropriate technology platforms. There are several decision automation platforms available on the market, including Corticon by Progress. These platforms provide the tools and resources necessary to design, implement and monitor automated decision-making processes. However, it is essential to select the right platform for your specific needs and to ensure that it is compatible with your existing systems and infrastructure.

Progress Corticon is a powerful decision automation platform that enables companies to automate complex decision-making processes in the energy industry. It provides a visual modeling environment that allows users to create decision models using business rules and natural language expressions, without the need for coding or programming. Corticon also provides real-time decision services that can be integrated with existing systems and applications, allowing companies to automate decisions across the enterprise.

One of the key benefits of Corticon is its ability to scale to meet the needs of large, complex energy organizations. With the ability to handle millions of transactions per second, Corticon can support high-volume decision-making processes across multiple systems and applications.

Another advantage of Corticon is its ease of use. With its intuitive visual modeling environment, energy professionals can quickly create and modify decision models, without the need for specialized programming skills. This can help to reduce the time and cost of implementing decision automation processes in the energy industry.

Check it out here: https://www.progress.com/corticon.