Inaccurate power estimation leads to the expensive over-procurement or risky under-buying of electricity. Inefficient procurement decisions drive up blended power costs and result in avoidable grid penalties. Faclon Labs provides AI-based demand forecasting that synchronizes production schedules with historical consumption to optimize power purchase planning
Accurate short-term power demand forecasting aligned to production and maintenance plans
Reduction of manual planning errors through AI-driven automation
Optimized power mix decisions across grid, captive, and renewable sources
Measured reductions in power costs, penalties, and planning risk
Lower Blended Cost per kWh
Optimized procurement strategies and power-mix balancing significantly lower the overall unit cost of electricity
AI-Driven Predictive Automation
Transition from reactive scheduling to proactive, automated demand planning based on high-fidelity machine learning models
95% Reduction in Planning Errors
Removing human bias and calculation errors from forecasting ensures precision in power purchase agreements and bidding
Maximized Renewable Utilization
Maximize the consumption of green energy by accurately predicting solar and wind availability against plant load
Solution Highlights
Core capabilities enabling accurate and automated energy demand forecasting
Multi-Source Data Integration:
Seamlessly ingests data from ERP, EMS, and DCS systems to feed the AI forecasting engine
15-Min Block Forecasting
Provides granular power demand predictions for the next hour and day in precise 15-minute intervals
Dynamic "What-If" Analysis
Interactive tools to simulate production changes, downtimes, and varying power source availability
Renewable Generation Forecasting
Predicts solar and wind output based on localized weather data to optimize the green energy mix
IEX Bidding & Mix Optimization
Generates optimized purchase plans for IEX bidding, balancing cost, grid stability, and green targets
Historical Correlation Engine
Automatically maps historical power consumption against past production cycles to refine future accuracy
Why Faclon Labs
Key strengths behind scalable and high-accuracy energy forecasting deployments
Industry-Specific AI Models
Proven forecasting algorithms with model accuracy consistently exceeding 95%
Successful Cement Sector Deployment
Demonstrated success in complex, high-load environments like large-scale cement plants
End-to-End Implementation
Comprehensive ownership from data lake creation to delivering actionable procurement recommendations
Cybersecurity Compliant
Enterprise-grade architecture meeting the highest IT/OT security standards for critical infrastructure
Scalable Across Site Networks
Designed to centralize and forecast energy demand for multiple distributed plant locations
Proactive Customer Success
Dedicated support focused on model refinement, system upkeep, and driving user adoption
CASE STUDY - ENERGY DEMAND FORECASTING
Reducing Energy Costs via Predictive Power Demand Analytics for a Cement Leader
We implemented an AI-driven power demand forecasting system at a major cement manufacturing plant to optimize high-stakes energy procurement decisions. By analyzing historical consumption and real-time operational data, the solution provides precise load recommendations 45 minutes ahead of each power block. This proactive approach allows the plant to avoid heavy penalties and optimize grid-versus-captive power usage, shifting from reactive adjustments to data-driven energy management
Up to USD 500K Annual Savings Optimized power procurement and reduced penalties led to significant annual cost reductions
Higher Accuracy Than Manual Planning The AI model outperformed traditional spreadsheet-based estimation by identifying complex consumption patterns
95–97% Forecast Accuracy Achieved High-precision modeling ensured consistent reliability for power purchase planning across all shifts
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