An AI-Driven Framework for Real-Time Energy Consumption Monitoring, Demand Forecasting, and Optimization Using Deep Sequence Models

Abstract: Rising electricity costs, grid stress, and the global imperative to reduce carbon emissions have intensified the need for intelligent systems that can monitor and curtail energy waste at the building and household level. Conventional metering reports cumulative consumption after the fact, offering little insight into when, where, or why energy is wasted, and provides no forward-looking guidance for reducing demand. This paper presents an artificial-intelligence framework that ingests fine-grained consumption telemetry, forecasts short-term demand, detects anomalous usage, and recommends actionable optimization measures in real time. The system streams data from smart meters and appliance- level sensors through an edge gateway into a time-series store, applies a deep sequence model for load forecasting, flags deviations through an anomaly detector, and surfaces savings recommendations on an interactive dashboard. A Python back end performs model training and inference, while a Node.js layer delivers live visualization over web sockets. Evaluated against linear-regression and ARIMA baselines, the proposed deep model reduced forecasting error to a mean absolute percentage error of roughly 6.4%, and the optimization layer identified consumption reductions of up to 18% in evaluation scenarios. The principal contributions are an end-to-end streaming monitoring pipeline, an accurate deep forecasting and anomaly-detection layer, and an interpretable recommendation engine that links prediction to concrete energy-saving actions.

Keywords: Energy monitoring; demand forecasting; deep learning; LSTM; anomaly detection; smart metering; energy optimization; time-series analysis.