Volume 8 • Issue 2 • PP: 07–13 • 2024
Smart Home Energy Management through ARIMA Model Forecasting: Leveraging Weather Data for Improved Efficiency
Open Access & Copyright
© 2024 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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This study pursues machine learning models for the task of smart homes’ energy management with the use of a dataset that combines smart meter readings and weather conditions at the same time. The assessment of the Baseline Qualification and ARIMA models is done using various criteria, such as MSE, RMSE, and others. Most telling, the best performance is shown by ARIMA, which gets the lowest MSE score, 0.0693, in this instance. They show that such a model is optimal in forecasting energy consumption dynamics, and while they could be better, weather information helps improve the accuracy of the forecasts. The conduct helps uncover priceless information, allowing for the development of new smart home operating systems with a prospect of energy efficiency enhancement as well as a sustainable environment.
Keywords
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