IoT & ML-based Energy Management Solution

IOT FOR ENERGY MANAGEMENT

IoT & ML-Based Energy Management Solution

The team developed software for IoT energy management, specifically tailored to monitor wind turbines and manage energy production.

#IoT

#Energy

#ML

Client*

A leading business in the renewable energy industry for over 20 years, specializing in wind energy and overseeing a vast network of wind turbines across multiple regions.

*We cannot provide any information about the client or specifics of the case study due to non-disclosure agreement (NDA) restrictions.

Project in numbers

duration

Ongoing project

team

14 specialists

The team involved in the project

industry

Energy

solution

Data analytics

technologies

JavaScript, React, Redux, Python, FastAPI, Apache Spark, Kubernetes, Docker, AWS, PostgreSQL, Grafana

Services

1 x Business analyst

1 x Project manager

1 x Solution architect

3 x Back-end developers

1 x Front-end developer

1 x Embedded developer

1 x ML developer

1 x DE developer

1 x DevOps Specialist

2 x QA engineers

1 x Stakeholder’s SME

Challenge

Build an IoT energy management solution empowered with ML algorithms for real-time monitoring and predictive analysis of wind turbine performance. The main goal is to prevent system malfunctions that could cause power outages and costly repairs.

Solution & functionality

The team came up with an IoT & ML-driven energy management software solution that predicts energy production. An advanced platform provides real-time updates on the status of each wind turbine based on the information accumulated from meteorological sensors and turbines.

Programmable logic controllers (PLC)

We utilized programmable logic controllers (PLCs) to gather data from sensors placed across the wind turbines. They monitor various operational metrics, like wind speed, turbine rotation speed, temperature, vibration, and torque, process the data and provide a precise overview of the wind turbine’s current performance, identify faults, and energy production efficiency. Additionally, system detects deviations, like an unexpected temperature rise or increased vibration — to prevent damage, it triggers alarms or shuts down the turbine. Such timely maintenance and malfunction prevention ensures balanced energy production and extends equipment lifespan.

Data visualization

To visualize data, our project team chose Grafana dashboards. We created customized actionable charts for IoT energy management displaying data like daily power output, turbine locations, weather patterns, and predicting future trends. Thanks to these visualizations operational managers have access to a real-time overview of turbine performance, while maintenance teams can quickly address turbine issues.

Data lake

The client needed a robust data lake, as they operate wind turbines across various regions. Our developers created a central repository to collect and store data from all turbines, regardless of their location, including structured, unstructured, and semi-structured data such as logs, sensor readings, and images. Data is collected from the PLCs and then stored and processed using AWS IoT Core and Lambda functions. Large datasets can be processed simultaneously, which greatly supports predictive maintenance and accelerates analysis and reporting.

Error prediction

Leveraging data science and MLOps, we developed a predictive model that evaluates various factors influencing turbine health, such as vibration and temperature levels, and performance metrics. This model continually learns from incoming data and enables the operational managers to detect warning signs of failures early. Upon identifying them, the energy management control system sends alerts to the maintenance teams so that they proactively address the issues before they cause breakdowns.

Analytical reports

The energy management system can generate analytical reports based on the historical and real-time data to provide insights into wind turbine performance. This data helps identify well-operating turbines and those needing maintenance. Also, by analyzing performance trends and external factors like weather, the system suggests ways to optimize energy consumption, determine ideal times for energy harvesting, manage storage, reduce costs, and streamline maintenance.

Results and business value

The team successfully implemented IoT in energy management, providing the client with a scalable energy management control system.

up to 6%

increase in energy production

18%

reduction in maintenance and repair costs

26

critical threats prevented

Benefits for client

The solution helps prevent system malfunctions that could cause power outages and costly repairs. As a result, the client achieved 18% reduction in maintenance and repair costs and up to 6% increase in energy production.

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    Bus fleet management software: 95% accuracy in real-time bus fleet tracking

    FLEET MANAGEMENT SYSTEM

    Bus Fleet Management Software: 95% Accuracy in Real-Time Bus Fleet Tracking

    The team created an interactive dashboard for a bus fleet management software, improving the accuracy of bus timetables and passenger counts.

    #IoT

    #Logistics

    #DataManagement

    Client*

    A leading bus operator distinguished for its extensive array of transportation services, particularly in Europe.

    *We cannot provide any information about the client or specifics of the case study due to non-disclosure agreement (NDA) restrictions.

    Project in numbers

    duration

    14 months

    team

    5 specialists

    The team involved in the project

    industry

    Logistics

    solution

    GPS tracking software

    technologies

    Python, Flask, Pandas, Azure SQL, Microsoft Azure, Power BI, Apache, Nginx, Prometheus, Grafana

    1 x Full-stack developer

    1 x BI developer

    1 x Data analyst

    1 x Project manager

    1 x QA engineer

    Challenge

    Improve the overall performance and user experience of the bus fleet management software

    Objectives

    Implement tracking of bus movement and resource allocation

    Update the dashboard to achieve data accuracy

    Make the user interface more intuitive

    Solution & functionality

    The team developed an advanced interactive dashboard system for the bus fleet GPS tracking with a user-friendly interface and focus on precise real-time data tracking.

    Interactive dashboard with GPS tracking and alerts

    Our team created a dynamic dashboard using data from IoT sensors, integrated with an Azure SQL database. Due to their ability to process and manage large datasets, the dashboard allows real-time tracking of buses and sending timely updates on bus positions, arrival times, any delays or schedule deviations, as well as passenger numbers.

    Enhanced data accuracy

    The system is designed to handle and analyze complex datasets, making predictions and assessments. The predictive algorithms evaluate past and current traffic data to recommend optimal routes and assess trends in passenger numbers. Through analysis of traffic patterns, weather conditions, and previous delays, the system foresees potential disruptions for planning. Altogether, these features help refine the transport schedules and bus frequencies, helping to reduce expenses and nurturing clients’ loyalty.

    Viewer and administrator roles

    The dashboard holds distinct user roles to accommodate diverse levels of engagement and operational requirements.

    Viewer role

    With the viewer role, users can access an interactive map with current bus positions, route status, and estimated arrival times in real-time, and a flexible analytics dashboard giving predictions about possible disruptions, allowing for route and scheduling adjustments and sharing reports.

    Administrator role

    Users with the administrator role have full control and supervision over the dashboard’s configurations and processes with data. They can customize the dashboard’s interface and features to evolving requirements and manage user access levels.

    Results and business value

    The new interactive dashboard for the bus fleet management software contributed to a more efficient, timely, and reliable transport service.

    2X

    faster data analysis

    30%

    reduction in operational delays

    95%

    accuracy in arrival and departure time tracking

    Benefits for client

    With implementation of a dashboard system and a user-friendly interface the bus arrival and departure times reached near-perfect accuracy, reducing wait times for passengers. Better operational performance and customer satisfaction was the ultimate goal of the client.

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      ML in banking: 5 times less fraud risk by spotting weird transactions

      Machine learning in banking
      ML in banking: 5 times less fraud risk by spotting weird transactions
      Timspark leveraged the power of machine learning in banking to keep an eye on digital transactions and catch any abnormal behavior with a new extension for the existing client’s system.
      #Fintech #Banking
      #MachineLearning
      #DataAnalytics

      Client*

      We have partnered with a major bank that has branches all over the US, providing loans, deposits, and more banking products.
      *We cannot provide any information about the client or specifics of the case study due to non-disclosure agreement (NDA) restrictions.

      Project in numbers

      duration
      19 months
      team
      13 specialists

      Team involved in the project

      industry
      Banking, Fintech
      solution
      ML-based system for fraud risk analysis and detection
      technologies

      Python, Scala, DVC, MLFlow, Comet, Apache Spark MLLib, Scikit-learn, LightGBM, XGBoost, Hyperopt, PySpark, Numpy, Pandas, Scipy, Docker, Docker Compose, Kubernetes, Jenkins

      2 x Frontend Developers
      2 x Backend Developers
      1 x Project Manager
      1 x Business Analyst
      2 x Data Engineers
      3 x ML Engineers
      1 x QA Engineer
      1 x UX/UI Designer

      Challenge

      The key American bank faced rising financial fraud threats, and traditional systems proved ineffective. We were picking the best ways to use machine learning in banking and finance against increasing fraudulent activities that endangered customer safety and the bank’s reputation.

      Related objectives

      Implement ML for fraud detection
      Upgrade the anti-money laundering system
      Increase customer safety
      Improve the bank’s reputation

      Solution & functionality

      We suggested adding an ML-powered extension to the banking system to scrutinize large data volumes and protect funds from malicious activities. It analyzes account holders’ transactions and raises alerts for any unusual, suspicious, or fraudulent behavior. With deep learning fintech algorithms, our team processed extensive data to spot irregularities signaling potential fraud risk.

      Aggregating data

      To begin, our engineers collected and unified all banking data, encompassing user identities, transaction histories, locations, payment methods, and other pertinent factors.

      Detecting anomalies

      We identified distinctive patterns like high transaction amounts or segmented transactions to avoid automated tax reporting, enabling ML algorithms to distinguish fraud from regular banking. Transactions are tagged as “good” or “bad”.
      We also accessed a vast dataset, efficiently spotting patterns and anomalies, and selected crucial features through data comparison and elimination techniques, improving fraud risk analysis and detection.

      Training the ML model

      Our ML team created algorithms to catch odd situations that slip past regular rules. This extension can predict even with less data, using smart machine-learning methods. So, our solution uses embedded representations, not typical features, to handle transactions.

      Implementing the ML model

      Once a threat’s spotted, the system shoots real-time data to the admin, who can stop or nix operations for further digging. Depending on the fraud chance, there are three outcomes:
      • If fraud odds are below 5%, the transaction gets the green light.
      • If the odds range between 6% and 70%, an extra check like an SMS code, fingerprint, or secret question is needed.
      • If the fraud chance tops 80%, the transaction’s axed, needing hands-on analysis.
      Plus, we set up good ML tools to explain models, making predictions clear and keeping things smooth for users.

      Results and business value

      Timspark’s top-notch ML extension spots fraud and takes action. Security’s solid — no breaches or financial crimes.

      x2.4 speedier in processing

      Our ML algorithms swiftly handle heaps of data, keeping up with the rapid transactions.

      99.3% accuracy of fraud detection

      Using these algorithms, we find tricky patterns that humans might miss. That means fewer mistakes and less unseen fraud.

      Less mundane tasks

      Our solution checks hundreds of thousands of payments per second, making the transaction process as painless as possible.
      The algorithms catch tiny changes fast, checking tons of payments per second. The bank gets tighter security, faster transactions, and less chance of missed fraud. It means smoother banking and peace of mind for the end customers.

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