Volume 12 • Issue 1 • PP: 55–65 • 2027
Smart Home Energy Consumption Forecasting: An Engineering Approach with IoT and Al-Biruni Earth Radius Optimized LSTM Networks
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© 2027 The Author(s). Published by ASPG. This article is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
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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.
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