AI-Driven Energy Planning: Planning Power Use with Artificial Intelligence
Traditional home energy management relies on residents’ memory and habits. Smart Longevity IoT changes this with ’AI-Driven Energy Planning’ — analysing data from sensors throughout the home to create an ’Energy Blueprint’: an optimal energy usage plan tailored specifically to that household.
The AI learns from each member’s ’Household Behaviour Profile’ — wake times, cooking schedules, and peak appliance usage periods — then creates an ’Optimised Daily Energy Plan’ allocating power to each activity without compromising comfort.
Multi-Appliance Scheduling: Smart Scheduling Across All Devices
The ’Appliance Priority Matrix’ is the backbone of energy planning. The system categorises devices into three levels: ’Must-On’ — essential appliances that must run on schedule, such as bedroom AC overnight; ’Flexible-On’ — devices whose timing can shift, such as washing machines; and ’Standby-Cut’ — appliances whose standby power is cut after 15 minutes of inactivity.
’Cascade Scheduling’ prevents high-consumption appliances from running simultaneously by having AI calculate ’Peak Demand Windows’ and distribute load evenly throughout the day — reducing both electricity costs and appliance wear from repeated inrush currents.
Demand Forecasting: Predicting Energy Requirements
’Short-Term Demand Forecasting’ predicts energy use over the next 24–72 hours using three data layers: 90 days of historical behaviour, weather data from a Weather API, and ’Calendar Integration’ — pulling events like holidays when more occupants will be home, increasing energy demand above normal.
The ’Seasonal Adjustment Module’ recalibrates forecasting parameters by season. In Bangkok, summer (March–May) with temperatures reaching 36–40°C drives AC load 40–60% higher than in cooler months. The system automatically adjusts the monthly ’Energy Budget’ based on these forecasts.
Renewable Integration Planning: Coordinating with Renewable Energy
Homes with Solar panels need ’Energy Flow Orchestration’ — coordinating power flow from three sources: solar panels, battery storage, and the utility grid. Smart Longevity IoT calculates ’Solar Yield Forecasts’ using irradiance and cloud data to plan whether to store or immediately use each unit of energy.
’Self-Consumption Maximisation’ aims to use self-generated energy as much as possible. The system shifts heavy loads to run during peak solar hours at midday while charging batteries for evening and overnight use. A Bangkok home with a 5 kWp system can self-consume 70–85% of what it produces.
Household Budget Integration
The ’Monthly Energy Budget’ feature lets users set a monthly electricity target — for example, not exceeding 3,000 THB. AI then calculates a ’Daily Energy Allowance’ and alerts when the target is approaching, recommending the least disruptive savings measures.
HappySmart designs Smart Longevity IoT to be accessible to all users regardless of energy knowledge — through a Thai-language Dashboard app that summarises data clearly, with ’Action Cards’ recommending what to do today to save on this month’s electricity bill.