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Machine Learning-Integrated IoT-Based Smart Home Energy Management System
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Author(s): Maganti Syamala (Koneru Lakshmaiah Education Foundation, India), Komala C. R. (HKBK College of Engineering, India), P. V. Pramila (Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, India), Samikshya Dash (School of Computer Science and Engineering, VIT-AP University, India), S. Meenakshi (R.M.K. Engineering College, India)and Sampath Boopathi (Mechanical Engineering, Muthayammal Engineering College, India)
Copyright: 2023
Pages: 17
Source title:
Handbook of Research on Deep Learning Techniques for Cloud-Based Industrial IoT
Source Author(s)/Editor(s): P. Swarnalatha (Department of Information Security, School of Computer Science and Engineering, Vellore Institute of Technology, India)and S. Prabu (Department Banking Technology, Pondicherry University, India)
DOI: 10.4018/978-1-6684-8098-4.ch013
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Abstract
The internet of things (IoT) is an important data source for data science technology, providing easy trends and patterns identification, enhanced automation, constant development, ease of handling multi-dimensional data, and low computational cost. Prediction in energy consumption is essential for the enhancement of sustainable cities and urban planning, as buildings are the world's largest consumer of energy due to population growth, development, and structural shifts in the economy. This study explored and exploited deep learning-based techniques in the domain of energy consumption in smart residential buildings. It found that optimal window size is an important factor in predicting prediction performance, best N window size, and model uncertainty estimation. Deep learning models for household energy consumption in smart residential buildings are an optimal model for estimation of prediction performance and uncertainty.
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