Conventional energy management systems react to what has already happened. Predictive Energy Forecasting uses AI to look 24–48 hours ahead, preparing the home before energy demand peaks occur.
Four Data Sources AI Uses for Forecasting
Source 1: Weather Forecast Integration — Thai Meteorological Department APIs and commercial weather services provide 48-hour temperature, humidity, and UV index forecasts. When AI predicts tomorrow’s temperature will exceed 35°C, it commands Pre-Cooling before 07:00 before electricity enters On-Peak pricing.
Source 2: Occupancy Prediction — Integration with occupants’ digital calendars identifies Work-From-Home days, office days, holidays, and appointments in advance, allowing the system to adjust energy modes proactively rather than waiting for motion detection.
Source 3: Historical Usage Pattern — Machine Learning analyzes 6–12 months of energy usage history, building Baseline load profiles for each hour, day, and season. Well-trained ML models achieve 85–92% accuracy in predicting residential HVAC load.
Source 4: Real-Time Price Signal — For systems connected to MEA or PEA Smart Meters, real-time electricity price signals enable automatic adjustment of BESS Discharge and Load Shifting strategies based on actual current pricing.
HVAC Pre-Conditioning Strategy
One of the highest-value applications is HVAC Pre-Conditioning: when AI predicts occupancy in 60 minutes and high external temperatures, the system begins cooling during Off-Peak hours, reaching target temperature at cheap electricity rates, then reduces output when On-Peak begins — relying on stored thermal mass. This approach saves 20–35% of On-Peak HVAC electricity costs.
Energy Budget Control
The AI system sets a Monthly Energy Budget in Baht and forecasts day-by-day whether spending is on track. If over-budget trajectory is detected, it automatically adjusts Temperature Setpoints by 0.5–1°C upward or shifts Non-Critical loads, notifying homeowners via LINE OA if manual action is needed.