The first consideration is recognizing which tasks — not just jobs — are being displaced. Most roles contain a mix of routine, automatable tasks and higher-value responsibilities that require judgment, creativity, and interpersonal skills. Mapping current workflows helps identify where AI can replace manual effort and where human skills remain critical. This clarity enables organizations to redesign roles rather than eliminate them outright.

Second, successful retraining requires a focus on transferable skills. Communication, problem-solving, analytical thinking, project management, and domain expertise remain highly valuable across industries. Training programs should emphasize how existing knowledge can be combined with AI tools, enabling workers to become supervisors of automation rather than victims of it. This shift empowers employees to deliver higher-impact outcomes instead of repetitive output.

Third, technical literacy is now essential for nearly every profession. Workers do not need to become data scientists or programmers, but they must understand how AI tools work, what they can and cannot do, and how to use them responsibly. Practical training in AI-assisted workflows — such as automated reporting, document drafting, customer interaction, and analytics — can significantly improve productivity while increasing employee confidence. The focus needs to be built around a structured AI security policy that ensures data safety and employee use definitions.

Fourth, retraining programs that are built around future job requirements, including roles such as AI operations support, data quality management, workflow design, system oversight, cybersecurity awareness, and human-centered service functions. One of the key recognitions is that AI requires a "human in the loop" to work with data verification and addressing AI hallucinations.

Finally, the process requires frequent employee communication. Messages such as "we're implementing AI and it will make our business more effective and efficient" without detailed implementation communication creates employee angst, turnover, and lowers morale. Position changes or eliminations may occur and employees need to know not only how AI will impact their jobs but what their future contributions will look like. Transparent communication, career counseling, mentorship, and phased transitions help workers remain engaged and motivated throughout reskilling efforts.

With intentional planning, targeted training, and supportive leadership, organizations that invest in reskilling not only protect their workforce but also build a more adaptable, innovative, and resilient organization for the future.

About the Author

Russ Hissom, CPA is a principal of UtilityEducation.com, a firm that provides power and utilities rate and expert witness services, and on-demand professional education classes in co-op and utility accounting, finance, ratemaking, artificial intelligence, and management.

Russ was a partner in a national accounting and consulting firm for 20 years. He works with electric investor-owned and public power utilities, electric cooperatives, and gas, water, and wastewater utilities. His goal is to share industry best practices to help your business perform effectively and efficiently and meet the challenges of the changing power and utilities industry.

Contact Russ at russ.hissom@utilityeducation.com

The material in this article is for informational purposes only and should not be taken as legal or accounting advice provided by Utility Accounting & Rates Specialists, LLC or UtilityEducation.com. You should seek formal advice on this topic from your accounting or legal advisor.

An exploratory machine learning study investigating whether operational energy patterns may provide early insights into abnormal cooling behavior in data center environments.

Artificial intelligence is increasingly being discussed within the data center industry as operators search for new ways to improve operational visibility, energy efficiency, and infrastructure resilience.

Much of the attention surrounding AI in data centers focuses on predictive maintenance, automation, and energy optimization. However, another emerging area of interest is the potential use of machine learning techniques to better understand complex operational behaviors within cooling-intensive environments.

Methods

This article presents an exploratory analysis investigating whether operational energy variables may reveal patterns potentially associated with abnormal cooling behavior in a data center environment.

It is important to emphasize that this analysis is not intended to propose a definitive anomaly detection framework. Instead, the study is designed to explore the feasibility of applying machine learning techniques to identify operational patterns that may be associated with cooling-related behavior, while explicitly acknowledging methodological limitations.

The dataset used in this study is publicly available and was obtained from the U.S. Department of Energy Office of Scientific and Technical Information (OSTI Data Explorer), accessed on May 14, 2026.

Proxy operational labels were constructed based on elevated cooling energy regimes. These labels represent constructed operational conditions rather than validated anomalies. As such, the results should be interpreted strictly within this exploratory context.

FIGURE 1 — Methodological Workflow

Distribution of anomaly scores generated by the Isolation Forest model, illustrating the continuous nature of deviations within the operational dataset.

Results

Below is a summary of the results obtained during this exploratory study.

TABLE 1 — Final Operational Variables

Operational variables used in the analysis after feature selection and removal of variables directly associated with cooling-derived metrics. 

FIGURE 2 — Boxplot of cooling_kw by Operational Class

Distribution of cooling_kw across normal and proxy anomaly operational classes, highlighting differences in cooling-energy dispersion patterns within the analyzed dataset.

FIGURE 3 — Random Forest Feature Importance

Relative feature importance derived from the Random Forest model, indicating the contribution of each operational variable to the proxy classification task.

FIGURE 4 — XGBoost Feature Importance

Feature importance scores obtained from the XGBoost model, illustrating the relative influence of operational variables in the classification of proxy cooling regimes.

FIGURE 5 — CatBoost Feature Importance

Feature importance distribution calculated using CatBoost, highlighting differences in variable weighting compared to other ensemble models.

TABLE 2 — Model Performance Comparison

Performance metrics of supervised ensemble models used in the exploratory analysis, showing high recall and comparatively lower precision across models. 

FIGURE 6 — Precision-Recall Curves

Precision-recall curves for the evaluated models, showing the relationship between precision and recall across different classification thresholds.

FIGURE 7 — XGBoost Confusion Matrix

Confusion matrix of the XGBoost model, presenting the distribution of true positives, false positives, true negatives, and false negatives in the proxy classification task.

FIGURE 8 — Comparative Feature Importance Across Ensemble Models (Normalized)

Normalized feature importance comparison across ensemble models, where importance values were rescaled to a common proportional scale to account for differences in model-specific importance metrics and enable consistent interpretation of variable relevance.

FIGURE 9 — Isolation Forest Anomaly Score Distribution

Distribution of anomaly scores generated by the Isolation Forest model, illustrating the continuous nature of deviations within the operational dataset.

Key Insight

The exploratory results suggest that cooling-related operational behavior in data centers may not present as clearly separable anomalous events. Instead, the observed patterns indicate that system behavior may evolve along a continuous operational spectrum, where deviations occur gradually rather than as discrete anomalies.

This interpretation is supported by both supervised and unsupervised modeling results, which consistently indicate high sensitivity to deviations but limited ability to sharply distinguish between normal and anomalous conditions.

Methodological Considerations and Limitations

The use of proxy labels based on energy regimes does not represent validated anomalous events, which limits the interpretability of classification outcomes.

Residual dependency between selected features and cooling behavior may introduce indirect information leakage.

Because the proxy operational labels were partially derived from cooling-related operational regimes, elevated recall values should not be interpreted as evidence of validated real-world anomaly detection capability.

Model performance is sensitive to threshold definition, particularly given the observed class imbalance and continuous behavior of the data.

Unsupervised results (Isolation Forest) indicate a continuous anomaly distribution, without clear cluster separation.

The absence of temporal validation and real operational fault data limits applicability in real-world deployments.

Feature importance results reflect correlation structures rather than causal relationships.

Missing data handling resulted in partial dataset exclusion during unsupervised modeling, which may influence score distributions.

Challenges and Future Directions

Future work may focus on incorporating validated operational fault events, temporal analysis, and deployment-oriented validation strategies to further assess the practical applicability of machine learning techniques in data center cooling systems.

Reference

U.S. Department of Energy – Office of Scientific and Technical Information (OSTI).
Data Center Operational Dataset.
Available at: https://www.osti.gov/dataexplorer/biblio/dataset/3015212
Accessed on: May 14, 2026.

Author 

PhD Kleber Vânio Gomes Barros is a Brazilian Technologist and researcher focused on exploratory applications of artificial intelligence, operational analytics, energy systems, and infrastructure-related data analysis. He currently serves at the Secretariat of the National Council of Export Processing Zones (SECZPE), Ministry of Development, Industry, Commerce and Services (MDIC), Brazil.

In 2024, the state sales tax exemption for data centers cost Ohio about $555 million in revenue, four times more than the state Department of Taxation forecasted.

In 2025, it cost a whopping $1.6 billion, eleven times more than the original estimate of $136 million

And that’s to say nothing of the local sales taxes – another $166.8 million in lost revenue in 2024, according to new actual cost data provided this week by Ohio Department of Taxation spokesperson Andrea Lannom. 

Read the whole story: https://signalohio.org/ohio-data-center-tax-break-cost-1-4-billion-more-than-expected-in-2025/

A✌️569-word✌️3.5-minute✌️read

Note: Planet Pulse has diversified its coverage. Although it will remain focused mostly on clean technology and climate change, general interest stories of note will also be intermixed.

On the cusp of a holiday weekend in the U.S. I suppose I should have included only cheery news, but that’s not how I roll. So if you don’t wish to learn about some inconvenient truths, wait to read this installment of Planet Pulse.

This week stories include a measure of something many of us intuitively know, the pitfalls of relying on the sun, a fun story about hydrogen, and a not so fun story about mundane but essential elements of the grid.

And oh yes – Happy Memorial Day to all in the U.S. And let’s keep in mind that this weekend isn’t just a long one to enjoy. It’s a time to reflect on those who have made the ultimate sacrifice for freedom.

𝗧𝗵𝗲 𝗵𝗲𝗮𝗱𝗹𝗶𝗻𝗲𝘀:

▶ Probably just fake news

▶ The wrong kind of sun block

▶ Faster than the speed of hydrogen

▶ Transformers – and not the movie

#hydrogen #transformers #electricgrid #coal #solarpower

As I dive in to this new role, I’m eager to get to know the community better and you might see me reaching out to talk! Please feel free to reach out to me as well at carrie@smoothmedia.co, or on LinkedIn.

As batteries move from the margins to the centre of India’s energy transition, coal, renewables, EVs and Chinese supply chains are becoming deeply interconnected.

👉 Read more:
https://indoen.com/news/coals-new-survival-strategy-how-batteries-are-reshaping-indias-power-future

I want to be careful about how I frame this because it is genuinely painful. A contractor working for Nightwing, based in Dulles, Virginia, maintained a public GitHub repository called “Private-CISA” from November 2025 through mid-May 2026.

The repository contained AWS GovCloud administrative credentials, dozens of internal CISA system usernames and passwords in plaintext, and files literally named “importantAWStokens.”

The administrator had disabled GitHub’s default secret detection. GitGuardian researcher Guillaume Valadon called it “the worst leak that I’ve witnessed in my career.” Congress is now demanding a classified briefing.

For the agency responsible for securing federal cybersecurity infrastructure, this is the kind of incident that erodes institutional credibility. The exposed credentials were reportedly still valid 48 hours after the repository was taken down. As I have been writing since Cybersecurity First Principles, the basics remain the hardest part. No amount of frontier AI capability matters if the people operating the infrastructure leave the keys in the open.

Tom Alrich’s Blog, too is a reader-supported publication. You can view new posts for two months after they come out by becoming a free subscriber. You can also access all my 1300 existing posts dating back to 2013, as well as support my work, by becoming a paid subscriber for $30 for one year (and if you feel so inclined, you can donate more than that or become a founding subscriber for $100). Whichever option you choose, please subscribe. 

If you would like to comment on what you have read here, I would love to hear from you. Please comment in my chat or email me at tom@tomalrich.com.

Central Hudson Gas & Electric is in the middle of a major five-year transformation aimed at becoming what CEO Stephanie Raymond calls a “premium utility” — one that leads on performance, reliability, trust, customer focus, and innovation. But what exactly does that mean?

In this episode, host Kinsey Grant Baker chats with Stephanie Raymond of Central Hudson and Josh Eife, Managing Director at Greencastle, to unpack how Central Hudson 2.0 came together, what sparked the need for a sweeping reevaluation, and what happens when a utility decides the status quo is no longer enough. Stephanie walks us through the six pillars shaping the transformation: Growth, Organizational Effectiveness and Optimization, Workforce Engagement, Customer Excellence, Grid Resilience and Reliability, and Work Transformation. The conversation also gets into the practical reality of leading change while keeping day-to-day operations running.

Now about a year into execution, the conversation covers what has changed, what surprised leadership, and what lessons emerged early in the process. For utility leaders navigating their own change journeys, this episode offers a grounded look at what it takes to build a “premium utility” in practice, and what that future might mean for both customers and employees.

Thanks to Greencastle Consulting for making this episode possible. For nearly 30 years, Greencastle has implemented critical initiatives in highly regulated, high-stakes environments—like utilities—turning strategy into real, measurable results. 100% veteran-owned and operated and headquartered in Pennsylvania, Greencastle specializes in strategy execution: aligning leadership, strengthening governance, and ensuring critical initiatives actually deliver. Whether you’re navigating enterprise-level transformations, grid modernization, or other complex technology programs and regulatory demands, Greencastle embeds with your team to drive clarity, accountability, and real outcomes.

Watch on YouTube

Listen on Spotify

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The Federal Energy Regulatory Commission yesterday (May 21) began a process to update its fast-track “blanket permitting process” under the Natural Gas that allows routine natural gas pipeline projects to proceed without full regulatory review. The commission issued a notice of proposed rulemaking (NOPR) outlining how it plans to streamline and modernize the commission procedure in a way that, as Commissioner David Rosner put it, “balances both environmental and ratepayer concerns.”

A FERC news release said the proposal “will empower companies to build much needed critical infrastructure without the delay of case-by-case approvals. These changes should dramatically accelerate construction by cutting through red tape and driving progress for the nation’s energy future.” 

Among the provisions in the NOPR would essentially double the cost limits that allow companies to use the blanket process, from $14.5 million to $30 million for projects that use an “automatic authorization” provision, from $41.1 million to $86 million for projects requiring prior FERC notification, and the “annual cost limit for natural gas facilities, including storage wells, used for the testing or development of underground storage reservoirs” from $7.9 million to $17 million.

The proposal also would remove the cost limits for expansions of existing gas compressor stations for work eligible for the “prior notice” procedure. 

According to a FERC staff presentation, “The NOPR also proposes to allow natural gas companies to charge incremental rates for prior notice projects and extends the in-service deadline from one year to two.” That is designed to prevent gas ratepayers not affected by the project from seeing the cost reflected in their bills.

FERC created the blanket certificate program in 1982. It’s most recent update was in 2006. Last year the Interstate Natural Gas Association of America (INGAA), the gas pipeline industry’s Washington lobbying group, asked FERC to take a look at updating the program. In June 2025, the commission issued a “notice of inquiry” seeking stakeholder comments.

In approving the NOPR this week, the commission also OK’d a companion order that INGAA proposed. It extends a deadline for projects with existing temporary waivers of the cost limits under the existing blanket authorizations to May 31, 2008, a year later than the previous May 31, 2007 deadline. FERC said, “This extension provides regulatory certainty to the industry as the Commission finalizes its permanent policy.”

At a press briefing following the meeting, Swett said updating the gas pipeline fast track proposal is “clearing the way for expansion” of the energy infrastructure “critical to the nation’s needs.” She added that FERC “is not cutting any corners here. Everything we are doing is grounded in commission experience and staff experience.” Swett added that the pipeline blanket program update is part of a “suite” of FERC initiatives to streamline routine repair and upgrade programs for hydropower projects the commission regulates and liquified natural gas projects.

In other commission news:

• FERC Thursday rejected a complaint by two small solar projects aimed at the PJM Interconnection, which refused to let them withdraw their application and recoup their deposits. PJM argued that allowing the withdrawal late in the interconnection’s process would lead to an incentive for late-stage withdrawals and shift costs to the remaining projects. FERC agreed with PJM.

• Approved a final rule, proposed last October, adopting revised “mandatory enforceable requirements” implementing rules for interstate gas pipelines by the North American Energy Standards Board “designed to streamline the process for accessing publicly available gas-electric coordination data during extreme cold weather or emergency events.” This item won praise from Commissioner Judy Chang, who described it as “a particular interest.”

• Swett and commissioners Rosner, Chang, and Lindsay See congratulated sitting commissioner David LaCerte for winning Senate confirmation for a full five-year term on the commission on Monday evening. His nomination was among a list of Senate committee approved nominations waiting for an en-masse floor vote. It is officially his second FERC term, as President Trump nominated him last June to fill the remaining months of a five-year term of departing Republican Chairman Mark Christie. He was confirmed for that FERC seat last October.

The Quad Report