aspg-28-4501-1792223416 20261017075016 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group Journal of Artificial Intelligence and Metaheuristics JAIM 2833-5597 2027 12 1 M. El-Said Electrical Engineering Department, Faculty of Engineering, Mansoura University, Mansoura 35516, Egypt With the growing complexity of energy consumption patterns, accurate forecasting is required to maximize the benefits of demand-side management, which relies on precise forecasting and optimization. To respond to this challenge, this research suggests a new forecasting method by integrating the Al-Biruni Earth Radius (BER) optimization algorithm with the Long Short-Term Memory (LSTM) network within an Internet of Things (IoT)-based framework. The BER-optimized LSTM model processes real-time IoT sensor data, such as energy usage patterns and weather conditions, to enhance energy usage predictions. Time-series clustering addresses data uncertainty and noise, providing a probabilistic load forecast at both the household and aggregated levels. Extensive evaluation of the model’s performance demonstrates substantial predictive accuracy, achieving a root mean squared error (RMSE) of 0.007146, surpassing conventional forecasting methods. These findings corroborate that BER optimization, coupled with the use of LSTM networks, provides a suitable tool for smart home energy forecasting, which, in turn, can facilitate improved energy management, demand-side optimization, and innovative grid applications in the evolving energy market. 2027 55 65 4501 https://creativecommons.org/licenses/by/4.0/ 10.54216/JAIM.120105 https://americaspg.com/journal/28/article/4501 https://americaspg.com/storage/articles/manuscripts/01M3AH99K5PFAY92RZK98ET324.pdf