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"Energy Utility Business Upgrade Failures and Successes"

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Kimberly McKenzie-Klemm's picture
Industry Technical Writing and Editing TPGR Solutions

After almost five years, I am happy to be up and writing again. Please tell me if you are still welcoming contributors articles at this time. I have had an enormous blessing of restored eyesight...

  • Member since 2013
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  • Dec 20, 2021
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Managing Energy Utilities’ Big Data efforts and upgrades to obtain faster emergency response times, seamless service, and risk factors mitigation in the Green Energy arena provides lower cost initiatives and environment safety at the business end of Energy Utilities’ operations. As we enter the era of Climate Change concerns and Green Energy promotion, the business endeavors of the Energy Utility Sector to upgrade and incorporate modern data use and response requires looking at both the failures and successes of implementing Big Data decisions. Some of the Energy Utility Sector’s efforts to increase efficiencies and reduce upsets include: using Machine Learning Systems, Artificial Intelligence (AI), Smart Grid Data Analytics, and Data Implementation Science.

Energy Utilities depending on Data Analytics most commonly rely on Machine Learning Systems to calculate energy losses and provide data monetization. Machine Learning Systems function differently than records retrieval storage and self-service analytic analysis. App developments and automated visualization tools create data transformation, including algorithms, for Big Data calculations. Big Data information sets are embraced and processed successfully at higher speeds through Machine Learning Systems than using older manual methods of incorporating data interpretation. Also, Machine Learning Systems' new software developments and adjustments keep pace with Energy Utility Big Data Analytics improvements still integrated with the existing Machine Learning Systems. 

While Data Analytics is widely used in current Energy Utilities' functions, it has been a struggle to include the business investment upgrade of Artificial Intelligence (AI) across the board to involve all Energy Utility providers. With the additional Machine Learning Systems' integrated use of AI, energy provision and customer value depreciations are detected earlier. The weights and measurements of the Energy Utilities’ business end and considerations for operable Energy Utilities implementing Digital Analytics are still awaiting final findings of the AI upgrade failures and successes.

Prevention measures against fluctuating business operations become automatic protection procedures delivered through Data Analytic Big Data reviews processed routinely by the Smart Grid. Smart Grid Data Analytics includes real time information and the capability of predicting or forecasting future Energy Utility operational situations. Stopping short of making automatic business decisions with Big Data, the Smart Grid responses to Energy Utilities’ Sector operations give a constant monitoring set of tools and a first response alert status on Utility dysfunctions. Ever since the Smart Grid has been embraced in the Energy communities, business with Energy Utilities’ customers has become faster, more accurate in needs responses, and easier to maintain concurrent situations in Energy Utility coverage areas.

Data Implementation Science in the Energy Utilities’ Sector begins where Machine Learning Systems, AI, and Smart Grid Data Analytics ends. Energy Utilities’ Data Implementation Science can utilize the cloud storage migration features to store and organize Big Data before composing comparison data sets. From data acquisitions obtained through the next phase of Big Data pipeline streaming, Data Implementation Science builds analysis models to develop insights and improve decision making. These models can be based on the current state of operations or on historical data used to examine past in-the-field response failures and successes to support risk management.

When evaluating the Energy Sector Machine Learning System use, AI inclusion, Smart Grid developments and Data Implementation Science development, these observations give some insights as to how the Energy Utilities’ business reactions are functioning:

  • Machine Learning Systems are the oldest and most compatible Data Analytic functioning applications, supporting the business' successful elimination of networking issues to integrate Big Data across all Energy Sector service providers. Often, updates of new software or improved programs show the wisdom of keeping the previous Machine Learning System intact and continuing to progress with compatible changes.
  • AI incorporation in the Energy Utilities’ arena is still fairly new to the Energy Industry. The reliability and requirement to individually tailor AI technology to each Energy business unit causes a questionable return on investment yet to be fully absorbed and measured for full performance merit. 
  • Unlike AI, the Smart Grid Data Analytics successfully captures real-time Big Data and employs or stores the data while interpreting and “learning” simultaneously for error-free Smart Grid fast response keeping pace with Energy customer and consumer situational and forecasted requirements covering business networking needs. 
  • Data Implementation Science involves the research and recovery aspects of maintaining the Energy Utilities’ Networks. The practices of Data Implementation Science allow for Energy Sector business recovery and development of better service/disaster planning with a knowledge base that widens Energy Utilities’ business awareness. 

While looking at the technology upgrades feeding new versions of Machine Learning Systems, AI use, and the Smart Grid rejuvenations, it becomes apparent that moving technology upgrades for Energy Utilities use is an overall positive, successful practice. The Energy Sector’s business successes in upgrading network and Data Analytics Big Data incorporation far outweigh the failure rates as cost effective, low risk management, and more efficient maintenance operations. As Climate Change and Green/Clean Energy raise challenges to sustain and drive through policies based on technology implementation and progressive change, Energy Sector business is an arena to watch for leadership and innovation.    

 

 

 

 

Kimberly McKenzie-Klemm's picture
Thank Kimberly for the Post!
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