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Many utility companies are shifting from time-based maintenance to smarter and proactive asset health management systems. These systems aim to reduce customer downtimes and manage to expand infrastructure within a budget while ensuring compliance with regulations.

Asset Investment Planning

Asset Investment planning is a process that organizations use to plan and manage resource allocation, with the main goal to maximize benefits, control costs, and mitigate risks to ensure high-value returns. We designed an AIP solution to assist our customers in planning and managing investments in high-value assets. The solution provides many capabilities and enhanced intelligence to optimize these investments’ benefits, costs, and risks. The AIP solution includes features such as Maintenance Decision Intelligence (MDI) and AIP Simulation based on predictive analytics. These help investment managers identify assets requiring replacement or maintenance, simulate various investment scenarios, and allocate funds based on risk minimization or fixed budget plans.

Predictive analytics helps make informed financial decisions based on predictive models and data. The AIP solution includes a Risk Matrix that aggregates assets on a substation and cost level, allowing users to analyze health scores, probabilities of failure, and consequences to identify critical investment areas. This data and analysis provide valuable input for strategic investment decisions.

Additionally, the IPS®APM solution supports investment planning by providing insights into asset conditions, risk assessments, and performance evaluations. Executives can use this information to prioritize investments based on asset health, criticality, and financial impact. The solution includes MDI diagrams, risk matrices, simulations, and predictive analytics to support decision-making.

How does predictive analytics work in asset investment planning?

The predictive analytics in AIP utilizes machine learning algorithms and standardized asset performance calculation logic methods. It integrates an advanced analytical model to track asset health and reliability by predicting asset aging and identifying end-of-life. Our AIP solution also includes a simulation engine that evaluates different investment options. This engine combines inputs from Asset Performance Management (APM) asset analysis indexes with data-driven algorithms to determine the preferred investment solution. The predictive models analyze health scores, probabilities of failure, and consequences to identify critical areas for investment.

The AIP Simulation feature enables users to simulate various investment scenarios by varying budget allocations, offering risk minimization and fixed budget plans. This lets users make informed financial decisions based on predictive models and data. The Risk Matrix aggregates assets on a substation and cost level, aiding in analyzing health scores, probabilities of failure, and consequences, which serve as valuable inputs for strategic investment decisions.

The data for these analyses is leveraged from multiple systems, such as enterprise asset management, asset performance management, GIS systems, and outage management systems. Additionally, the solution supports predictive analytics using historical values from visual inspections, monitoring systems, electrical test equipment, statistics (e.g., malfunction KPIs, Taylor series, Weibull functions), or any other data source. Integrated algorithms evaluate asset health and prioritize preventive maintenance approaches for all key assets over the reactive approach.

Risk Matrix

The Risk Matrix in the AIP solution plays a crucial role in aggregating assets based on their health scores, probabilities of failure, and consequences (both technical and financial). This aggregation allows users to identify critical areas for investment by analyzing these factors. The Risk Matrix helps make informed financial decisions by providing valuable input for strategic investment planning. It supports the AIP Simulation feature, enabling users to simulate different investment scenarios, minimize risks, and optimize budget allocations. The data and analysis from the Risk Matrix serve as essential inputs for the Asset Investment Planning team to make strategic decisions.

Probability of Failure

The probability of failure in the AIP solution refers to the likelihood that an asset will fail within a specified time frame. This metric is a crucial component of the AIP’s predictive analytics, helping to assess the risk associated with each asset. The probability of failure is calculated using various data inputs, including health scores, historical performance data, and condition assessments.

The AIP solution uses these probabilities, along with the consequences of failure (both technical and financial), to identify critical areas for investment. This information is integrated into the Risk Matrix, aggregating assets based on their health scores, probabilities of failure, and consequences. By analyzing these factors, the AIP solution helps users make informed decisions about where to allocate resources to minimize risks and optimize investment outcomes.

Regression budgeting

Regression budgeting in the AIP solution involves predicting future budget investments based on historical data and derived data such as the health index. This approach calculates the budget needed for investment over time for each selected asset or cluster. The engine not only predicts the required budget but also provides results such as the predicted cost of each asset and the impact of asset failures on the system.
The data science algorithms used for regression budgeting include multivariate algorithms. These algorithms enable the creation of simulations that show how changes in asset elements affect budgeting. By analyzing historical budget data and asset health indexes, regression budgeting helps forecast financial requirements for asset maintenance and replacement, ensuring informed and optimized investment decisions.

Maintenance Decision Intelligence

The Maintenance Decision Intelligence (MDI) feature in the AIP solution correlates with Asset Performance Management (APM) and Asset Investment Planning (AIP). This correlation enables investment managers to identify assets that require replacement or maintenance based on their current conditions and maintenance requirements. The MDI feature helps prioritize assets, ensuring cost-effective investments by providing insights into which assets need attention and when. It supports informed decision-making by highlighting the maintenance needs and conditions of assets, optimizing investment strategies, and ensuring efficient allocation of resources.

Conclusion

Predictive analytics is transforming asset investment planning in the utility sector by enabling decisions based on actual data. By leveraging AIP solution, utilities can shift from reactive to proactive strategies, optimizing asset health management and investment allocation. Features such as Risk Matrices, Maintenance Decision Intelligence, and regression budgeting empower utilities to minimize risks, prioritize critical assets, and achieve better financial outcomes. This approach not only ensures compliance and infrastructure flexibility but also helps utilities achieve long-term efficiency in their operations.

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This article explains on how to improve the process of knowledge transfer by integrating it into the application itself.

Knowledge Transfer Challenges

From the very beginning of software production, knowledge transfer to end-users has always been a challenge. The more complex the software, the more difficult it is to transfer knowledge to end-users.

For decades, the usual way of transferring knowledge has been to provide user manuals and adequate classroom-like training to end-users. This was tedious, labor-intensive, and time-consuming for both the end-users and the software manufacturer.

Knowledge Transfer Solution

IPS® decided to change this process from the root. Instead of treating it as a one-time event, we recognized that knowledge transfer is a continuous, two-way process.

The conclusion was to provide specific knowledge transfer for each software segment and each user individually. Knowledge transfer is integrated into the software itself and made available when and where it’s needed, allowing the end-users to learn on-demand at their own pace while also allowing them to send feedback.

IPS® Learning as a Knowledge Management Platform

IPS® Learning platform has been developed to be highly configurable and adapt to special use cases.

IPS® Learning provided full integration with all IPS® modules, allowing various access points to knowledge throughout the application. Now, every module of IPS® software has a direct link to relevant tutorials, training, videos, or a quick quiz. Also, relevant courses are available right on the spot. This approach allows users to access knowledge more casually, almost game-like, with the possibility to set their own pace, dedicating their attention and time to learning what they need the most.

Another very important goal for IPS® is the certification of employees who are using the software. IPS® Learning, as a Knowledge Management Platform, made this process straightforward. This platform enables the creation of courses and assignments for end-users, inviting them to access the student’s page by email. At this central point, they can educate themselves and take certain courses. After successfully passing the course, users are given a certificate that gives them the competence to work on certain parts of IPS® software. Learning progress can be monitored in real time, providing insight into how well users adopt new knowledge.

Besides that, the IPS® Learning platform provides detailed analytics, allowing the identification of weak spots in user knowledge and the implementation of corrective measures.

Conclusion

All sides have benefited by setting up a new way of distributing knowledge. Now, it’s easier to transfer knowledge to a large group of users with the possibility to track progress on an individual level. The feedback from the IPS® Learning platform makes it easy to find weak spots where users have the most difficulties so adequate actions can be taken.

Considering everything previously said, we can say that a new era of software learning has arrived, and with IPS® Learning, companies are well-equipped to meet the challenges.

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This year’s IPS User Meeting gathered energy professionals worldwide, providing a platform for sharing insights, technologies, and best practices.

Insights and Innovations: IPS User Meeting 2024

This year’s IPS User Meeting gathered energy professionals in Las Vegas, providing a platform for sharing insights, technologies, and best practices.

Networking Opportunities

One of the primary benefits of attending the IPS User Meeting was the networking opportunity. From first-time attendees to seasoned veterans, the event fostered conversations among users at all levels. Seeing individuals connect over shared experiences, discuss challenges, and brainstorm solutions was inspiring. Our first-ever networking session on Monday was well attended, and we received a visit from a true music legend and Vegas entertainer, Elvis! This relaxed and informal gathering allowed everyone to introduce themselves to other customers and our staff.

Randy Norman, IPS-ENERGY USA with ELVIS!

Presentations

The IPS User Meeting featured expert presentations from our customers who shared their experiences with IPS and how they solve real-world problems with Our Solutions.
Day one of the User Meeting featured an exciting look at IPS’s 20th anniversary. From our beginnings to our future, Dr. Zeljko Schriener, CEO and founder of IPS Intelligent Process Solutions, presented our focus on innovation and providing tools to move our customers into the Digital Future.

Big Change in Power

Our special guest speaker, Chris Root, CIGRE USNC President, covered the topic, “Big Change in Power,” from the history of CIGRE to the current changes the Electric Industry must face. He shared the limitations of renewals in the Northeastern region of the USA and insights on battery storage, renewal integration, and renewal integration challenges. This informative presentation covered the power of renewal generation and how peaks in areas pose the greatest challenge to the renewal mandates during times of “Dark Calms,” which are periods of limited or no solar generation lasting for days and up to two weeks in winter.

Customer Presentations

Our customer presentations showed the IPS® worldwide market with presentations from GSE, who shared the implementation of IPS® Work and Asset Management. Distributie Oltenia, a distribution company based in Craiova, Romania, shared their company’s maintenance and investment planning implementation. Transco, who is responsible for planning, development, and O&M of the energy and water transmission networks in Abu Dhabi and Northern Emirates, shared their background and challenges to ensuring end-users have the correct information at the right time and aligning technical solutions and processes with industry standards, to which IPS is part of their overall solution. Finally, DEWA, Dubai Electricity & Water Authority shared the Distribution Power Asset Management and their Asset Health Center project and their vision for the challenges and the steps to digitize their future.
User Meeting presentations included Technical Innovations in IPS® Solutions, updated IPS®LUNA, the IPS® Advanced Scheduling Module, and IPS®NMM. Notably, a presentation on the future of IPS technology provided insight into where the industry is heading and how IPS users can prepare for upcoming changes.

Day Two

Day two of the User meeting covered a wide array of topics relevant to IPS users. Speakers included customer representatives from Entergy, more than a hundred-year-old electrical utility in the United States, on their implementation of IPS®OMS. Attendees also enjoyed a presentation from Siemens Energy on their SIEAERO, a digital inspection service for overhead power lines. Combining various sensor processes into a fully integrated solution for overhead line inspections, this technology can be communicated from SIEAERO into IPS® for Asset Management, Asset Investment Planning, and the like. From the IPS® team and our partners Megger, there was a lively discussion on news on our collaboration, technical updates, and the vision for the future. Moving into a deeper level of our cooperation, the Transformer Intelligence Center was presented. This new venture at IPS opens the possibilities for utilities of all sizes to utilize IPS® Fleet Management services.

Attendees at the 2024 IPS User Meeting

User Success Stories

Perhaps the most inspiring segment of the IPS User Meeting was sharing user success stories. Participants had the opportunity to present real-world examples of how they overcame challenges and achieved significant results through the effective use of IPS resources. These case studies highlighted the platform’s versatility and illustrated its user community’s creativity and determination. Read more about user success stories on our website.

User Group Customer Discussion

Conclusion

The IPS User Meeting was an enlightening experience that underscored the power of collaboration and community in driving innovation. With a wealth of knowledge shared, connections made, and a renewed commitment to excellence, participants left inspired and equipped to tackle future challenges. If you missed this year’s meeting, consider attending in the future—an opportunity you won’t want to miss! Keep up with IPS on our website, or schedule a demo today!

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In the last week of August, IPS team participated in the CIGRE Technical Exhibition, the leading global event for power systems experts. The CIGRE session took place between 25 to 30 August 2024 at Palais des congrès in Paris. At the IPS booth, we had the opportunity to welcome many existing customers and meet new interesting people and companies.

Visitors to our booth had the chance to see wide range of IPS®ENERGY tools for asset and network data management, enhanced by advanced analytics, mobile workforce management, investment planning, and more. IPS team thanks everyone who visited, we are happy for all our gripping conversations.

If you missed us at the event, you can always contact us or book a free demo of our products.

We are already looking forward to the next CIGRE Paris Session in 2026!

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“Accessible, standardized power outage data can improve response times and save lives when combined with other emergency response systems.” Victor Hoerst

Unplanned Power Outage Management

As the frequency of severe weather events and other causes of power outages continues to rise, various governmental programs are being established worldwide. In the United States, the Outage Data Initiative Nationwide (ODIN) is a prime example, advocating for real-time, standardized, and transparent voluntary sharing of power outage data. This underscores the crucial role of Outage Management details in our infrastructure and daily lives. Outages can occur at all levels of the power grid, whether planned or unplanned, including distribution, transmission, and supply. Let’s delve into the challenges with unplanned outages and how powerful outage management software can significantly improve response times, thereby minimizing the impact of outages and ensuring the safety and well-being of all involved.

Stresses of Unplanned Outages

Power outages cause disruptions in communities and can even lead to loss of revenue and, in extreme cases, loss of life. To get a sense of that cost, according to ITIC, Information Technology Intelligence Consulting reports:

Hourly downtime costs continue to rise for all businesses, with 86% of firms stating that one hour of downtime costs $300,000 or more.
One-third of organizations report that the cost of a single hour of downtime can reach $1 million to over $5 million.
Downtime can be equally devastating to small and mid-sized businesses, potentially leading to significant monetary losses, damage to reputation, and litigation.
85% of corporations now require a minimum of “four nines” of uptime (99.99%) for mission-critical hardware, operating systems, and main line of business applications.
High reliability, availability, and strong security are imperative for conducting business in today’s interconnected networks.

This type of data can keep power system stakeholders up at night. While these statistics are taken from the business sector, not the utility sector, it is easy to see how revenue loss is just one example of why having a reliable and efficient system to manage outages is crucial to power utilities. Customer satisfaction is a key performance indicator. Many power companies have implemented various outage management systems to respond quickly and effectively to power outages.

Management of Unplanned Outages with Legacy Systems

While critical and time-tested for any city or region, if the current systems are manual power restoration processes such as spreadsheets and a phone list hanging on the wall, there will be significant disruptions in operations and severe consequences. This inefficient process can break down when the team usually in charge has a vital team member on vacation. There are many other ways manual processes can break down; this article will delve into these issues, highlighting the need for prompt maintenance plans and proactive measures to mitigate such risks.

Advantages of Automated Processes in Outage Management

With the increasing complexity of today’s grid, manual outage management processes still need to be improved to meet organizations’ needs. Automated approval processes have become essential to streamline decision-making and response times during an outage. Automating approval workflows eliminates specific redundant tasks, reduces the time required to review and approve outage-related requests, and facilitates seamless stakeholder communication. In this context, the benefits of automated approval processes are significant, and they can provide cost reductions to organizations that use them efficiently. Benefits of automated processes include:

Time Reduction
- Automated approval processes reduce downtime during an outage by streamlining decision-making and response times
- Expedite decision-making by automatically routing outage-related requests for approval
- Ensure critical actions, such as procuring replacement parts and authorizing emergency repairs, are initiated promptly
- Eliminate redundant tasks and manual interventions, reducing the time required to review and approve outage-related requests
- Enable faster issue resolution and quicker power restoration to affected areas
Streamlined Communication
- Facilitate seamless communication between stakeholders involved in outage management
- Real-time notification and status updates keep all parties informed about the progress of outage response efforts
- Enable coordinated actions and faster problem resolution
Enforcement of Compliance
- Enforce compliance with outage management protocols and regulatory requirements
- Reduce the risk of non-compliance-related delays by obtaining all necessary approvals before implementing changes or taking corrective actions
Data for Improvement
- Capture valuable data and metrics for outage response times, approval cycles, and resource utilization
- Analysis of data provides insights into areas for improvement
- Enable organizations to refine their outage management procedures and optimize response strategies for future incidents
Fault clearance process tools
- – Streamline fault detection, diagnosis, and resolution processes
- – Enable early detection of outages
- – Facilitate rapid diagnosis of issues
- – Automate certain aspects of outage management
- – Provide valuable data insights for improvement
- – Optimize workflows for more efficient outage management
- – Contribute to continuous improvement in outage management

Reduction of Resource Pressure with IPS^®OMS

The IPS^®OMS Unplanned Outage Management is not just another solution for power companies to manage outages. Its unique design sets it apart, empowering operators to swiftly pinpoint the root cause of an outage, dispatch maintenance crews, and restore power to affected customers with unmatched speed. Real-time data collection gives operators a holistic view of the outage. Its automated processes streamline outage requests, maintenance crew dispatch, and repair progress tracking, instilling confidence in the system’s efficiency.

The IPS^®OMS Unplanned Outage Management module is effective for managing unplanned outages. However, the transmission company must address redundancy, maintenance, and protection issues to prevent transmission failures, improve reliability, and reduce the frequency and duration of outages.

The IPS^®OMS is a solution-designed tool to report, manage, and track unplanned outages. The system’s automated processes help operators manage outage requests, dispatch maintenance crews, and track repair progress. Additionally, the system’s fault clearance process tools and forced outage reporting features help maintenance crews identify and manage the outage’s root cause, ensuring the system is restored safely and efficiently.

Want to experience the optimized and brighter future with IPS^®OMS Unplanned Outage tools?

Schedule a Demo Today!

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Effective Workforce Management

Every business has different priorities, but most businesses, regardless of size or industry, generally have a desire to be more efficient and productive than their competitors. Uncertainty and unpredictable times often lead to staff shortages, inefficient production, higher turnover and higher costs. Many companies also lack planning and efficient work organization. Effective workforce management should help companies strategically allocate staff and resources, track attendance, and comply with ever-changing workplace laws and regulations so that the main goal is to optimize productivity and reduce risk.

With the advanced mobile platform IPS®MobApp, workforce management becomes easier. Together with the natively integrated IPS®LUNA and the IPS® Advanced Scheduling Module (IPS®ASM), our solution ensures that all resources are in the right place at the right time. The best part is that a modular structure distinguishes the IPS®SYSTEMS. Customers can add individual module groups according to functional requirements and implement the system step by step – starting with the basics and implementing the full functionality step by step.

Our state-of-the-art mobile solution, IPS®MobApp, with online and offline capability and dynamically changing work tasks and action lists based on field data collection and instant asset analysis, provides superior predictive maintenance and speech recognition capabilities. Ma- chine learning (IPS®LUNA), asset nameplate and condition photo recognition and analysis are just some of the features already built in.

IPS®ASM is a web-based application designed to simplify work scheduling and dynamically plan and assign work based on resource availability. Master the optimization of time and costs, reduce the number of outages throughout the year and increase the use of available human, material, and inventory resources. It is a flexible application with a clear focus on safety that can be used as a stand-alone application or in conjunction with other ERP systems.

IPS®ASM is an automated process that makes suggestions and recommendations considering the condition of assets, dynamic cycles with priority calculations, budget constraints, outages, etc.

How does Workforce Management work in IPS®?

An essential component of workforce management is planning. With the integrated planning wizard, short- and long-term plans can be created in minutes. With advanced filtering, users can select asset groups, apply action templates and let the system do the work. The planning wizard is a tool that organizes all activities and defines execution dates with predefined workforce cycles and tolerances. Assets are linked to action templates and a workforce schedule is created. Workforce Management can be extended with the IPS® Asset Investment Planning solution.

Once the plan has been created, triggers in IPS®Smart- GridDI are used to create work orders and trigger notifications. These triggers can trigger different types of follow-up actions, such as email notifications, web notifications or the generation of a work order action. Save valuable resources through effective work planning and control.

Performing workforce management is the last step where the field worker gets the job done. With the advanced IPS®MobApp WFM, all data is synchronized directly with the central database. The field workers are always up to date on work orders and tasks. In addition, an overview of the history of workforce tasks and tasks related to the location of the facilities is available.

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Managing planned outages can be a challenging task. In this article, we will discuss how, with the help of OMS, you can streamline your outage planning and management processes, making your job easier, more efficient, and less stressful.

OMS is user-centric and makes your life easier, not harder. The system is designed to use your existing processes and terminology, creating an efficient solution to manage and communicate all aspects of a planned outage.

What are the challenges of a Planned Outage?

A planned outage has four significant challenges: coordination, communication, compliance, and cost. Within the coordination challenge lies the internal coordination of operations, maintenance, and engineering and external coordination with, for example, governmental agencies. All of these stakeholders have their priorities and schedules. Another vital factor to consider during coordination is minimizing customer time impact.

It is essential to manage communication between all the entities involved. Customers want communication; improper communication with outside agencies can lead to delays, and operations, maintenance, and engineering departments must be in the loop. Consider a scenario where the planning team overlooks the communication of vital details such as the precise time and location of the power outage or the extent of the work to be carried out. As a result, the field crew may be ill-equipped to perform their assigned duties with maximum efficiency. Leading to delays, missed deadlines, and even safety hazards.

There are several ways compliance issues can lead to problems. If permits or regulatory approvals are required for the planned outage area, delays or legal consequences may arise. Within the compliance realm, you must also consider the planned outage schedule. Not adhering to the planned outage window can lead to compliance issues and penalties.

Tying these issues together is the last of the four significant challenges. Cost impacts every planned outage issue that can occur. Failure to communicate, coordinate, and comply can result in loss of time, increased staffing, safety issues, fines, and legal consequences, to name a few. All of these issues contribute to cost!

How does OMS handle the Challenges of Planned Outages?

As previously discussed, coordinating planned outages can be challenging and requires collaboration with internal and external stakeholders. The OMS system facilitates utility coordination by providing a platform for scheduling outages, visualizing plans, and recording operational log activities. The platform also offers the infrastructure for change tracking, alerting, notification, and reporting and can seamlessly integrate with all internal and external systems. OMS streamlines the coordination process, reducing the impact on power system utilities.

OMS enables utilities to communicate with their internal teams and external stakeholders effectively; this ensures everyone is on the same page to complete the planned outages safely and quickly. The platform provides various communication channels, such as email, SMS alerts, and social media updates, to facilitate effective communication. Additionally, the system can automatically engage certain communications when specific events occur, thus enhancing time management and reducing errors.

Moreover, OMS is designed to assist in complying with the power industry’s regulatory standards and guidelines for outage management. It fulfills the requirements of regulatory bodies such as NERC, FERC, and others. The system provides a comprehensive solution to manage and track outages, work permits, and other associated activities in adherence to regulatory requirements. Furthermore, the platform is flexible and customizable, allowing utilities to adapt to changes in regulations or reporting requirements quickly. OMS provides a robust reporting system that generates reports in the required formats for regulatory compliance. Providing a centralized platform to manage outage-related activities and data, OMS is designed to help utilities maintain compliance with regulatory requirements while being adaptable to regulatory changes.

OMS and Cost Reduction

Our last challenge cost is embedded in coordination, communication, and compliance. While not separate from the other areas, the importance of a utility is so vital that it deserves its own section. There are costs related to operations, maintenance, and engineering; external costs can affect a utility through customer dissatisfaction, fines from agencies, and other losses related to bad planning. Streamlining outage planning and management processes makes your job easier, more efficient, less stressful, and cost-efficient. Here are some ways that OMS can help:

Streamlining outage planning and management processes, OMS helps utilities save time and reduce the number of manual tasks required to manage outages. This results in increased efficiency and reduced labor costs.
By providing real-time visibility into outage schedules and work activities, OMS helps utilities optimize maintenance and reduce downtime. This translates to reduced costs associated with equipment failure and repairs.
OMS provides a comprehensive solution for compliance management, reducing the risk of non-compliance penalties and associated costs. The system ensures that all necessary permits and approvals are obtained before the outage, reducing the risk of delays and legal consequences.

IPS Planned OMS 1 1024x534 Streamline Your Outage Planning and Management Processes — IPS Outage Planning

Overall, OMS is a cost-effective solution that helps power system utilities reduce costs by improving efficiency, optimizing maintenance, and minimizing downtime.

In conclusion, managing planned outages can be a complex and challenging task. Still, with the help of OMS, utilities can streamline their outage planning and management processes, making their job more accessible, more efficient, and cost-effective. By providing a centralized platform for collaboration and communication, assisting in complying with regulatory requirements, and reducing operational costs, OMS enables utilities to manage planned outages safely and quickly, with minimal impact on the power system and customers. With its user-centric design and customizable features, OMS is the ideal solution for utilities looking to improve their outage management processes and achieve greater operational efficiency.

Book a demo and learn more about managing planned outages from the experts.

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In part one of Exploring the Power of Data Lakes in Machine Learning, we discussed what data lakes are. Their benefits for storing unstructured data for machine learning, metadata management, data governance, security measures, data preprocessing, and data integration with IPS^®IDL.

Part two will cover data lakes in machine learning workflows, discussing benefits, concerns, and best practices. It also covers data quality, security, preprocessing, integration, computational costs, and overfitting issues. The importance of metadata management, data governance, security, and data preprocessing are highlighted. We also discuss data integration and how IPS^®IDL reduces resource requirements.

Drawbacks and Downsides of Large-Scale Processing

It is necessary to work through the potential downsides of large-scale processing, such as increased computational costs and longer training times. Have you ever considered the possible drawbacks of large-scale processing in machine learning? Large-scale processing in machine learning can offer accuracy and efficiency benefits, but it also comes with a cost in the form of increased computational resources and longer training times. Finding the right balance between accuracy, efficiency, and costs is crucial for deploying machine learning models. However, there are ways to mitigate these issues and optimize computational resources by staying up-to-date with the latest advancements in the field. By utilizing IPS^®IDL, you benefit from the depth of a data lake with the increased speed of our lightweight database layer. To see more benefits of IPS^®SYSTEMS, schedule a Demo with us now!

Overfitting

Overfitting in machine learning can significantly impact the model’s performance when applied to new data. This can occur when the model fits the training data too closely when using large datasets. However, this issue can be effectively prevented by implementing different measures. Some of they are collecting more data, using a simplified model, implementing regularization techniques, using cross-validation techniques, and carefully selecting the features used in the model. It is essential to balance the amount of data used and the complexity of the model. Therefore, it is crucial to implement suitable measures to prevent overfitting and improve the model’s overall performance.

Ensure Interpretability and Transparency in Models

Data lakes may result in models that need more transparency and interpretability, creating concerns about bias and accountability. Trust in the model predictions comes from incorporating techniques like feature importance analysis, model explainability, data visualization, and documenting the machine-learning pipeline. The goal is to balance efficiency with interpretability and transparency for reliable and trustworthy models.

Data Lakes and Feature Engineering

Feature engineering uses domain knowledge to extract features from raw data via data mining techniques. These features improve the performance of machine learning algorithms. Data lakes are a powerful tool for feature engineering as they allow practitioners to work with raw data in its entirety, enabling them to discover and create new features that can improve the accuracy of ML models.

It’s essential to consider the trade-offs and potential risks of over-engineering features to avoid overfitting and ensure the model’s effectiveness in real-world scenarios. Yes, over-engineering features can lead to overfitting the model to the training data. Sometimes, this is because your model is too complex. Over-engineering features can cause the model to learn specific details of the training data irrelevant to the problem, leading to overfitting and poor performance on new data. To mitigate the issue of over-engineering, it is essential to use feature selection techniques to identify the most relevant features for the problem at hand. This can involve using domain knowledge, statistical techniques, or machine learning algorithms. Additionally, it is vital to use regularization techniques to prevent overfitting. To discourage complex models, add a penalty term. Ultimately, it is essential to balance the complexity of the model with the amount of data available to ensure that the model can generalize well to new data.

Including irrelevant or redundant features in a model can decrease its accuracy. Therefore, selecting and filtering the model’s features is vital. Doing so can reduce the model’s accuracy, resulting in noisy data and slowing down the training process. To avoid this, use feature selection techniques to identify the most relevant features. It’s also essential to reduce the data dimensionality and balance the model’s complexity with the available data. This will help the model generalize new data well and achieve high accuracy.

Model Bias in Feature Engineering

Feature engineering can introduce bias into the model if certain features have more weight or importance than others. Likely, this happens when the feature selection process lacks meticulousness. It can also occur if there are biases in the data itself. To address these concerns, it is essential to use techniques such as exploratory data analysis (EDA) to identify potential biases in the data and carefully select features relevant to the problem. Additionally, it is important to use techniques such as regularization to ensure that the model is not overly dependent on any individual feature, reducing the risk of bias. It is also important to evaluate the model’s performance on a diverse data set to ensure that it is not biased toward any particular subset of the data. Finally, it is important to document the entire feature engineering process, including the selection and weighting of features, to provide transparency and accountability throughout the machine learning workflow. Taking these steps makes it possible to mitigate the risk of bias introduced by feature engineering and ensure that the resulting model is fair, accurate, and reliable.

Versioned Data in Data Lakes

Versioned Data: Data lakes can maintain versioned datasets, crucial for reproducibility in machine learning experiments. This ensures that ML practitioners can trace back and replicate experiments with specific versions of input data.

It’s important to remember that versioned datasets can be demanding in terms of storage space and computing resources. Consider this to avoid costly mistakes down the line.

Maintaining versioned data sets can require significant storage space and computing resources. As more and more data are collected and processed, the size of the datasets can snowball, making it challenging to store and maintain multiple versions of the data.

Conclusion

In conclusion, data lakes offer a flexible and scalable infrastructure for handling diverse data that supports machine learning models. However, potential challenges are associated with storing raw, unstructured data, ensuring data quality, and addressing security concerns. Preprocessing, data integration, and careful consideration of overfitting are essential to ensure accurate and reliable machine learning models. By utilizing IPS^®IDL, you can benefit from the vast amount of data available in a data lake and optimize computational resources while adding an intelligence layer to link the information. Overall, data lakes provide a significant opportunity for organizations to leverage the power of machine learning and extract valuable insights from their data.

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This article concerns data lakes and their use in machine learning. It includes topics such as the definition of data lakes, their benefits, concerns, potential drawbacks, and how they can be used in machine learning workflows. It also covers data quality, security, preprocessing, integration, computational costs, and overfitting issues.

Why is it called a data lake?

“If you think of a data mart as a store of bottled water – cleansed and packaged and structured for easy consumption – the data lake is a large body of water in a more natural state. The contents of the data lake stream in from a source to fill the lake, and various lake users can come to examine, dive in, or take samples.” James Dixon, CTO Pentaho

What is a Data Lake?

It support machine learning tools by providing a flexible, scalable infrastructure for handling diverse data. Data lakes store raw, unstructured data, allowing for flexible exploration and analysis. This is essential for feature engineering and understanding data patterns.

Problems with Raw Data Storage

How can storing raw, unstructured data in a data lake lead to data quality issues and make it difficult to find specific data? Storing unstructured data in a data lake can create problems with data quality and organization. However, there are tools and techniques available to address these challenges. For instance, metadata management tools can categorize and tag data in the data lake, making it easier to search for specific data. Additionally, data governance policies can be implemented to ensure that data is correctly defined, documented, and maintained within the data lake.

IPS^®IDL (Intelligent Data Lake) can add any type of new model and any type of model management, including utilizing vendor-specific source models. In addition, IPS^®IDL utilizes intelligent parsers to extract relevant information from vendor-specific models and provides a configurable transformation script library based on vendor-specific knowledge.

It is important to be aware that storing large amounts of sensitive data in a data lake can pose potential security risks. Strong security measures, including access controls, encryption, and monitoring, are necessary to safeguard data from unauthorized access and breaches. For IPS^® customers, IPS^® Identity Provider offers cyber security authentication and authorization. If you are interested in how IPS^® handles security, schedule a DEMO today! Finally, reviewing and updating security policies and procedures regularly is vital to stay ahead of emerging threats and vulnerabilities.

What is Data Preprocessing?

Machine learning models require well-structured and clean input data. Data lakes simplify preprocessing by enabling data extraction, transformation, and loading into suitable formats for ML algorithms.

How do preprocessing biases and errors affect ML model accuracy? It’s crucial to consider potential biases and errors that could occur during the preprocessing stage of machine learning. These issues can significantly impact the accuracy of the models. Therefore, paying attention to data preprocessing is essential to ensure that the models are unbiased and deliver accurate results.

Preprocessing is a vital step in ML and can impact model accuracy. Biases and errors can occur due to incomplete/incorrect data or algorithmic biases. Identifying and eliminating biases is crucial to ensure accurate and reliable models. Multiple techniques can be employed and validated to avoid inconsistencies and biases. In our next article, we’ll discuss model bias in machine learning and how we best prepare for the occurrence to ensure our data is fair, accurate, and reliable.

Data Integration

Data integration allows data lakes to combine different data types, such as structured, semi-structured, and unstructured data from diverse sources. This comprehensive view enhances the diversity and richness of input data for ML models.

At IPS, we can access your pre-existing data lake and add an intelligence layer to link the information. By doing so, we can reduce resource requirements by adding a lightweight database layer over the existing data lake.

In part 1, we’ve discussed what a data lake is and how it differs from a data mart. Next, we’ve highlighted the benefits of using a data lake for storing raw, unstructured data for machine learning tools. We’ve discussed the potential problems with storing raw data and how to address them with metadata management tools and data governance policies. Also, the importance of security measures for safeguarding data from unauthorized access and breaches and covering the significance of data preprocessing in machine learning and how it impacts model accuracy. Finally, we’ve touched on data integration, which allows data lakes to combine different data types from diverse sources, and how IPS security measures, including access controls, encryption, and monitoring, are necessary to safeguard data from unauthorized access and breaches. For IPS^® customers, IPS^®IDL works by adding an intelligence layer to link information and reduce resource requirements.

Read part 2: Exploring the power of data lakes – machine learning workflows, best practices (part 2)

Table of Contents

Optimize Energy Investments

The energy sector is undergoing significant transformation. Utility companies are confronted with the challenge of managing aging infrastructure that requires extensive refurbishment or replacement. This is happening alongside a global shift in policy towards more efficient and sustainable energy systems. Ignoring these factors could greatly affect overall grid stability, customer satisfaction, and compliance with evolving regulatory and environmental standards.

Faced with these challenges, utilities are struggling with limited resources and a range of stakeholders, both internal and external, who have interests in various projects including new developments, refurbishments, legal regulations, and network digitalization.

Recognizing the need for data-driven decision-making in investment projects, companies are turning to AIP solutions. These solutions provide powerful and intelligent support that enables companies to plan and manage investments, optimizing benefits, costs, and risks to achieve high-value returns.

IPS has been identified as a Representative Vendor in the 2022 Gartner^® Market Guide for AIP Solutions for Energy and Utilities. Access the report to explore AIP solution recommendations and key capabilities in more detail.

AIP vs Asset Performance Management (APM)

AIP differs from APM and Enterprise Asset Management by focusing on longer-term strategic planning, specifically aimed at optimizing budgets. In contrast, APM focuses on evaluating asset health, importance, probability, and consequences of failures, ensuring accurate and measurable operational reliability of assets.

Every AIP investment and scenario is evaluated based on APM results and strategic considerations, such as budgetary constraints and regulatory requirements.

IPS AIP Process Flow Diagram v2 1024x576 What is Asset Investment Planning AIP? — Asset Investment Planning Process Flow Diagram

AIP role in digital ecosystem

An AIP solution should utilize asset data collected from systems like NMM, APM, EAM, GIS, and OMS to predict areas of need. This, coupled with budgeting opportunities, enables the identification of the most viable investment options within a predetermined timeframe, typically spanning a year or longer.

On the flip side, AIP systems serve as the primary source of information and should be seamlessly integrated into supply chain management processes (SCM) and enterprise resource planning (ERP) systems.

During periods of supply and demand shocks such as pandemics or conflicts, as well as times of inflation, it is imperative for companies to streamline and effectively execute SCM and distribution processes to mitigate risks. AIP systems play a pivotal role in providing valuable insights within such scenarios.

AIP – what to look for?

Several factors should be considered before implementing an AIP solution, such as:

The real value of existing in-house solutions and their integration (in)capability within the digital twin represented by EAM, APM and NMM.
Whether to base AIP on outdated statistical data or on actual asset risk and planning data.
Ensuring seamless integration between AIP, APM, EAM, and operational planning tools like NMM as a source for investment planning decisions.
Methods to support and enhance decision-making by optimizing the benefits, costs, and risks associated with high-value assets.

IPS^®AIP Asset Investment Planning can enhance the value of investment decisions and is a proven approach for complex business challenges. Contact us if you want to schedule an AIP demo.

Asset Investment Planning

How does predictive analytics work in asset investment planning?

Risk Matrix

Probability of Failure

Regression budgeting

Maintenance Decision Intelligence

Conclusion

Knowledge Transfer Challenges

Knowledge Transfer Solution

IPS® Learning as a Knowledge Management Platform

Conclusion

Insights and Innovations: IPS User Meeting 2024

Networking Opportunities

Presentations

Big Change in Power

Customer Presentations

Day Two

User Success Stories

Conclusion

Unplanned Power Outage Management

Stresses of Unplanned Outages

Management of Unplanned Outages with Legacy Systems

Advantages of Automated Processes in Outage Management

Reduction of Resource Pressure with IPS®OMS

Effective Workforce Management

How does Workforce Management work in IPS®?

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What are the challenges of a Planned Outage?

How does OMS handle the Challenges of Planned Outages?

OMS and Cost Reduction

Drawbacks and Downsides of Large-Scale Processing

Overfitting

Ensure Interpretability and Transparency in Models

Data Lakes and Feature Engineering

Model Bias in Feature Engineering

Versioned Data in Data Lakes

Conclusion

Why is it called a data lake?

What is a Data Lake?

Problems with Raw Data Storage

What is Data Preprocessing?

Data Integration

Optimize Energy Investments

AIP vs Asset Performance Management (APM)

AIP role in digital ecosystem

AIP – what to look for?

Reduction of Resource Pressure with IPS^®OMS