Smart Home Energy Optimization Using AI

Residential electricity consumption varies with occupancy, weather, appliance use, and household routines. Conventional energymonitoring often provides limited visibility into which devices consume the most electricity and when demand is highest. This paperproposes an AI-assisted smart home energy optimization framework that combines smart-meter or appliance-level measurements, Internet ofThings (IoT) communication, consumption analytics, and automated control recommendations. The system collects time-stamped energydata, validates readings, forecasts short-term demand, and identifies opportunities to shift flexible loads or reduce unnecessary consumption.A rule-based controller provides a transparent baseline, while machine-learning models may be evaluated when sufficient historical data areavailable. The design includes a dashboard for energy trends, estimated costs, alerts, and user-approved automation. Privacy, device security,reliability, and manual override are treated as core requirements. An evaluation plan is presented using prediction error, energy-usecomparisons, peak-demand measures, and system reliability. This paper describes a proposed design and does not claim measured energysavings or field-tested model accuracy