Advanced Time Series Load Forecasting for Distributed Energy Resources with IoT and Deep Learning

To improve operational efficiency and reliability, new load forecasting methods are required for integrating Distributed Energy Resources (DERs) into power networks. This paper introduces a new time series load forecasting method that uses Deep Learning techniques, notably Radial Basis Function Networks (RBFNs) and Internet of Things (IoT) data. The proposed approach uses real-time data from IoT sensors to identify complex patterns and variations in energy use. The RBFN design is tuned for high-dimensional input data; it has the potential to predict load demand for DERs with greater precision. With significant decreases in prediction error, the experimental findings show that the RBFN model works better than traditional forecasting techniques. This development promotes sustainability in power systems by efficiently integrating renewable energy sources and improving energy management. In distributed energy management, the results highlight the possibility of integrating deep learning methods with IoT technology to improve load forecasting capabilities. It addresses the issues of load forecasting for DER with use of IoT technology and RBFN. The enhanced model increases accuracy providing improved grid stability, energy efficiency, and real-time demand regulation.