A Simple Moving Average-Based Predictive IoT Architecture for Energy Consumption in Smart Cities

Efficient energy management systems which monitor and forecast along with optimal consumption management are fundamental requirements for smart city development. The implementation of Internet of Things (IoT) allows real-time data collection that leads to predictive energy management prospects. This work develops an energy consumption forecast system through a lightweight IoT infrastructure which utilizes Simple Moving Average (SMA) predictive methodology. Using real-time node data from households and industries the SMA approach delivers energy consumption predictions suitable for constrained resource conditions. A model evaluation based on Mean Absolute Percentage Error and R2 scores shows the model maintains reliable accuracy despite implementation across varying consumption ranges and low computational expenses. The presented study demonstrates how SMA technology helps address smart city energy management needs and delivers an implementation plan for optimizing urban energy systems sustainably.