i-EMS
Next-Generation AI-Driven Energy Management (iEMS)
Moving beyond traditional monitoring (CEMS), our iEMS leverages advanced AI algorithms to predict power generation and System Marginal Price (SMP). This enables data-driven automation for revenue improvement and long-term equipment optimization.
1. Overview
iEMS is designed to move energy operation from simple monitoring to intelligent prediction, automated scheduling, anomaly anticipation, and strategic decision support. By combining generation forecasting, SMP analysis, SOC-based control, and asset-health monitoring, operators can make faster and more profitable decisions across multiple sites.
2. Core Strategic Benefits
Profit Maximization
- Reduces generation forecast error to below 8%
- Secures settlement incentives through better forecast accuracy
- Improves annual revenue by approximately 10~20%
Asset Optimization
- Extends battery life by 10~20%
- Reduces maintenance cost by 30~50%
- Supports proactive preventive maintenance cycles
Operational Automation
- Minimizes labor cost through daily automated scheduling
- Supports real-time correction and operational adjustment
- Improves repeatability of site operations
Future Scalability
- Built on microservice architecture
- Supports future VPP (Virtual Power Plant) integration
- Accumulates long-term data assets for AI enhancement
Integrated Dashboard
관리 시스템
(IEMS)
홈 대시보드
실시간 운영 현황 및 AI 예측 요약
AI 예측 바 차트
발전량 예측
Forecast vs. Actual 비교 분석
예측 vs 실측 바 차트
SMP 예측
48시간 예측 라인 및 신뢰구간
48h 예측 라인 / 신뢰구간 밴드
AI 충방전 스케줄
SOC 및 SMP 기반 자동 스케줄 생성
Charging / Discharging Timeline
Smart Operations & Predictive Intelligence
High-Precision Analytics
- Uses an ensemble of LSTM, XGB, and Prophet models
- Forecasts daily and next-day generation with target MAPE ≤ 8.0%
- Analyzes real-time SMP fluctuations and SOC conditions
- Automatically generates profitable charging-discharging timelines
AI Health Scoring
- Monitors BMS rack voltage deviation and PCS temperature trends
- Calculates risk score on a 0–100 scale
- Detects early failure signs before breakdown occurs
- Predicts battery replacement timing using SOH tracking
Multi-Site Visibility
- Provides intuitive UI for multiple plant operation management
- Compares Forecast vs. Actual in real time
- Supports rapid operational decision-making
- Unifies monitoring, analytics, and report output in one environment
Customizable Strategy: One-Click AI Presets
Peak SMP 중심 전략
- Peak SMP 시간대 방전에 집중하여 단기 수익 극대화
- 공격적 수익 확보와 빠른 ROI에 유리
배터리 보호 우선 전략
- 보수적인 DoD 운영으로 배터리 수명 보존
- SOH 저하와 유지보수 리스크 최소화에 적합
수익성과 수명 균형
- 수익성과 장비 수명을 동시에 고려한 최적 중간안
- 표준 운영 환경에서 지속가능한 고효율 성능 제공
전략 적용 전 비교 시뮬레이션
- Aggressive / Standard / Conservative 시나리오 비교
- 예상 수익 차이를 사전 시각화하여 전략 선택 지원
Operational Value
iEMS combines AI forecasting, SMP-driven optimization, predictive maintenance, and scenario-based scheduling to help operators improve profitability while maintaining long-term system reliability and asset health.
Next-Generation AI-Driven Energy Management (iEMS)
Moving beyond traditional monitoring (CEMS), our iEMS leverages advanced AI algorithms to predict power generation and System Marginal Price (SMP). This enables data-driven automation for revenue improvement and long-term equipment optimization.
1. Overview
iEMS is designed to move energy operation from simple monitoring to intelligent prediction, automated scheduling, anomaly anticipation, and strategic decision support. By combining generation forecasting, SMP analysis, SOC-based control, and asset-health monitoring, operators can make faster and more profitable decisions across multiple sites.
2. Core Strategic Benefits
Profit Maximization
- Reduces generation forecast error to below 8%
- Secures settlement incentives through better forecast accuracy
- Improves annual revenue by approximately 10~20%
Asset Optimization
- Extends battery life by 10~20%
- Reduces maintenance cost by 30~50%
- Supports proactive preventive maintenance cycles
Operational Automation
- Minimizes labor cost through daily automated scheduling
- Supports real-time correction and operational adjustment
- Improves repeatability of site operations
Future Scalability
- Built on microservice architecture
- Supports future VPP (Virtual Power Plant) integration
- Accumulates long-term data assets for AI enhancement
Integrated Dashboard
관리 시스템
(IEMS)
홈 대시보드
실시간 운영 현황 및 AI 예측 요약
AI 예측 바 차트
발전량 예측
Forecast vs. Actual 비교 분석
예측 vs 실측 바 차트
SMP 예측
48시간 예측 라인 및 신뢰구간
48h 예측 라인 / 신뢰구간 밴드
AI 충방전 스케줄
SOC 및 SMP 기반 자동 스케줄 생성
Charging / Discharging Timeline
Smart Operations & Predictive Intelligence
High-Precision Analytics
- Uses an ensemble of LSTM, XGB, and Prophet models
- Forecasts daily and next-day generation with target MAPE ≤ 8.0%
- Analyzes real-time SMP fluctuations and SOC conditions
- Automatically generates profitable charging-discharging timelines
AI Health Scoring
- Monitors BMS rack voltage deviation and PCS temperature trends
- Calculates risk score on a 0–100 scale
- Detects early failure signs before breakdown occurs
- Predicts battery replacement timing using SOH tracking
Multi-Site Visibility
- Provides intuitive UI for multiple plant operation management
- Compares Forecast vs. Actual in real time
- Supports rapid operational decision-making
- Unifies monitoring, analytics, and report output in one environment
Customizable Strategy: One-Click AI Presets
Peak SMP 중심 전략
- Peak SMP 시간대 방전에 집중하여 단기 수익 극대화
- 공격적 수익 확보와 빠른 ROI에 유리
배터리 보호 우선 전략
- 보수적인 DoD 운영으로 배터리 수명 보존
- SOH 저하와 유지보수 리스크 최소화에 적합
수익성과 수명 균형
- 수익성과 장비 수명을 동시에 고려한 최적 중간안
- 표준 운영 환경에서 지속가능한 고효율 성능 제공
전략 적용 전 비교 시뮬레이션
- Aggressive / Standard / Conservative 시나리오 비교
- 예상 수익 차이를 사전 시각화하여 전략 선택 지원
Operational Value
iEMS combines AI forecasting, SMP-driven optimization, predictive maintenance, and scenario-based scheduling to help operators improve profitability while maintaining long-term system reliability and asset health.