kubwa in Energy Industry and Data Analysis

• Resolving innovative energy problems using AI technology following the explosive increase in global energy consumption
• Provide efficient energy-saving solutions AI-based for facility operation that utilizes energy data
• Energy efficiency by applying AI and big data technologies in the value chain stage of energy production, supply and consumption

  • 2014

    Expected to develop ICT utilization and convergence technology with the energy sector

  • 2017

    New energy-related data business expansion of IoT, AI public data opening start, automobile comprehensive information, national energy

  • 2019

    Digitalization of the fast-growing energy industry, and increased utilization of data analysis in energy production, consumption, utilization, and storage

  • 2020

    kubwa, orgnized data analysis business energy sector

  • 2021~

    Dvelope eco-friendly AI that meets increased energy efficiency through energy data analysis and the Korean Green New Deal policy

Application Case 1. Resource Management - Leak Detection

• Water pipe leak detection algorithm through AI model
• Minimize damage through proactive measures by predicting leaks in advance
• Efficiently measure leak location, leak status, and leak volume

  • 1


    AI-based intelligent leak integration
    Solution platform design,
    AI-based pipe acceleration signal abnormal detection development,
    Leak location prediction development

  • 2


    AI-based leak data prediction platform,
    AI-based leak location calculation,
    Calculation of leaks and leaks

  • 3


    AI-based leak platform and model field validation and optimization

  • 4


    Commercialization of AI-based leak detection platform and algorithm

Application Case 2. Energy Management – Motor Machine Diagnosis

• Build AI motor diagnosis predictive maintenance system
• Real-time diagnosis by predicting motor failure with an AI model through vibration sensor data


Data Exploration

Sensor data collection
vibration frequency


Data Cleansing

Vibration & current data
Check data distribution
Raw data selection
Normal/abnormal frequency confirmation
Labeling data


Feature Extraction

Feature engineering for time-based and spectrum-based


AI Model & Prediction

Selection of algorithms such as XGBoost, LigthGBM, SVM, Neural Networks, etc.
AI learning and fault diagnosis


AI Deployment

Building motor diagnosis dashboard