AI
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
Home Dashboard
Real-time operation status and AI forecast summary
AI Forecast Bar Chart
Generation Forecast
Forecast vs. Actual comparison analysis
Forecast vs. Actual Bar Chart
SMP Forecast
48-hour forecast line and confidence band
48h Forecast Line / Confidence Band
AI Charge / Discharge Schedule
Automatic scheduling based on SOC and SMP
Charging / Discharging Timeline
Smart Operations & Predictive Intelligence
1. AI-Powered Forecasting & Scheduling
- 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
2. Predictive Maintenance & Anomaly Detection
- 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
3. Integrated Dashboard
- 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
1. Revenue-First Mode
- Focuses discharge on peak SMP periods to maximize short-term revenue
- Effective for aggressive profit capture and faster ROI
2. Equipment-First Mode
- Uses conservative DoD operation to protect battery life
- Suitable for minimizing SOH degradation and maintenance risk
3. Balanced Mode (AI Recommended)
- Balances profitability and equipment lifespan
- Provides sustainable high-efficiency performance in standard operation
4. Profit Simulation
- Compares Aggressive / Standard / Conservative scenarios
- Visualizes expected profit differences before strategy execution
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.