Optimized Internet of Things-Based Intelligent Energy Management System for Enhanced Renewable Generation through Advanced Forecasting and Load Strategies

Abstract

An Internet of Things (IoT)-based intelligent energy management system enables monitoring and control of renewable energy generation. It lowers waste and increases energy efficiency via the use of sophisticated forecasting and load optimization techniques. However, challenges such as data inconsistency processing and integration of diverse energy sources still need to be addressed. In this paper, an optimized IoT-based intelligent energy management system for enhanced renewable generation through advanced forecasting and load strategies (IOT-IEMS-RGFLS-GPTPINN) is proposed. Firstly, the input data is gathered from the National Solar Radiation Database. Then the input data is preprocessed using an adaptive higher-order singular value decomposition clutter filter (AHOSVDC) for normalization. The preprocessed data is then fed into the prediction segment by using generative pretrained physics-informed neural networks (GPT-PINN) to predict the accurately forecast renewable energy generation and load demand. The battlefield optimization algorithm (BFOA) is used for enhancing GPT-PINN parameters. The proposed IOT-IEMS-RGFLS-GPTPINN technique is executed in Python. The proposed method's performance was evaluated using performance indicators such as mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE). The IOT-IEMS-RGFLS-GPTPINN achieves an MAE of 0.011%, outperforming the other existing methods SAT-IRES-IOT for 0.052%, RA-DSEM-PSO for 0.034%, and EDSM-SIMS-LEO for 0.063%. This includes existing methods such as Enhanced demand-side management for solar-based isolated microgrid systems: Load prioritization and energy optimization (EDSM-SIMS-LEO), Recent advancement in demand-side energy management systems for optimal energy utilization (RA-DSEM-PSO), and Smart agriculture technology: An integrated framework of renewable energy resources, IoT-based energy management, and precision robotics (SAT-IRES-IOT).