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.

Related cases

Need assistance with a software project?

Whether you're looking for expert developers or a full-service development solution, we're here to help. Get in touch!

    What happens next?

    An expert contacts you after thoroughly reviewing your requirements.

    If necessary, we provide you with a Non-Disclosure Agreement (NDA) and initiate the Discovery phase, ensuring maximum confidentiality and alignment on project objectives.

    We provide a project proposal, including estimates, scope analysis, CVs, and more.

    Meet our experts!

    Viktoryia Markevich

    Relationship manager

    Samuel Krendel

    Head of partnerships

    SAP Implementation Services: 11% Reduction in Costs and Increased Revenues

    SAP implementation services for oil and gas industry

    SAP implementation services: 11% reduction in costs and increased revenues

    By integrating SAP S/4HANA, our team has modernized outdated enterprise management systems and the client’s operational processes, as well as streamlined purchasing, inventory management, transportation, financials, and analytics.

    #ERP

    #Enterprise

    #DataManagement

    Client*

    An industrial corporation which specializes in oil and gas investigation, extraction, refining, and transportation.

    *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

    33 months

    team

    31 specialists

    The team involved in the project

    industry

    Enterprise

    solution

    SAP S/4HANA integration

    technologies

    SAP S/4HANA, SAP GUI, SAP HANA

    21 x SAP consultants

    1 x Project manager

    1 x Project architect

    2 x ABAP developers

    Challenge

    The client‘s business was rapidly expanding and faced difficulties with the legacy software. As a SAP implementation partner, our team had to address these issues so that the software could meet the growing demands of the enterprise.

    Objectives

    Rebuild legacy software

    Foster visibility and control over the value chain

    Solution & functionality

    Our team proposed deploying an SAP business suite to automate logistics, finance, HR management, reporting, and more. Utilizing SAP Activate, we managed to integrate best practices and methodologies for smooth S/4HANA implementation.

    Financial accounting module (FI)

    Our team deployed a finance module to manage transactions within the client’s businesses, covering general ledger and asset management, as well as accounts payable and receivable, financial reports, and bank accounting. All financial activities, income, and expenditures are carefully recorded in the module, so they stay compliant with global accounting standards.

    Funds management module (FM)

    Using this module, the client can utilize budget resources efficiently. The module enables controlling revenue and expenditures, tracking funds according to financial constraints, and preventing budget excesses, while simultaneously allowing managers to change releases, supplements, returns, and transfers.

    Sales and distribution module (SD)

    The module houses customer and sales data, encompassing every facet of the sales cycle. Additionally, utilizing it with Materials Management (MM) and Financial Accounting (FI) modules helps to facilitate sales transactions, oversee orders, devise pricing tactics, and evaluate sales efficacy.

    Controlling module (CO)

    As the module documents the essential data, stakeholders are enabled with efficient decision-making, supervision, and enhancement of all corporate operations. Additionally, the module compares actual data with the initial plan so that it can be adjusted for a short-term or a long-term period.

    Human resources module (HCM)

    The last not least important module was integrated to oversee and bolster the client’s workforce — staff administration, organizational oversight, time management, payroll processing, perks, and self-service functionalities for employees, so that human resources are applied to their full potential.

    Business intelligence module (BI)

    We designed this module for efficient data analysis and reporting, giving the client the possibility to get data from SAP and non-SAP platforms, then convert them into valuable insights. Functionality allows a range of possibilities, like data storage, modeling, creating reports and dashboards, as well as setting key performance indicators (KPIs).

    Materials management module (MM)

    The module helps to oversee procurement, stock, and warehouse operations along the supply value chain. This implementation ensures timely availability of materials, optimizes inventory levels, and streamlines procurement procedures.

    Results and business value

    With SAP S/4hana implementation, we replaced the obsolete system with cost-effective solutions that enabled the client to diversify workflows, regain data integrity, visibility and controllability over business processes.

    Benefits for client

    With SAP S/4HANA implementation, our team transformed the client’s operational processes, covered project administration, inventory, sales, distribution, and financial management. We improved the client’s operational workflows, facilitating smooth data transmission and interaction among various divisions, thereby enhancing coordination and teamwork throughout the organization.

    14%

    increase in total revenue

    11%

    reduction in production cost

    Related cases

    Need assistance with a software project?

    Whether you're looking for expert developers or a full-service development solution, we're here to help. Get in touch!

      What happens next?

      An expert contacts you after thoroughly reviewing your requirements.

      If necessary, we provide you with a Non-Disclosure Agreement (NDA) and initiate the Discovery phase, ensuring maximum confidentiality and alignment on project objectives.

      We provide a project proposal, including estimates, scope analysis, CVs, and more.

      Meet our experts!

      Viktoryia Markevich

      Relationship manager

      Samuel Krendel

      Head of partnerships

      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.

      Related cases

      Need assistance with a software project?

      Whether you're looking for expert developers or a full-service development solution, we're here to help. Get in touch!

        What happens next?

        An expert contacts you after thoroughly reviewing your requirements.

        If necessary, we provide you with a Non-Disclosure Agreement (NDA) and initiate the Discovery phase, ensuring maximum confidentiality and alignment on project objectives.

        We provide a project proposal, including estimates, scope analysis, CVs, and more.

        Meet our experts!

        Viktoryia Markevich

        Relationship manager

        Samuel Krendel

        Head of partnerships

        Let’s build something great together

          Let’s build something great together

            Let’s build something great together

              Let’s build something great together