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- W3104688052 abstract "Residential sectors using energy mainly though lighting and HV AC (Heating, Ventilation and Air-Conditioning) have become a significant consumer of world energy and it is expected to grow especially with the trend of increasing smart homes. To provide an optimum, accurate and reliable electricity distribution, load prediction is a prerequisite policy and operational implementation. Smart homes with the use of various sensors create big data that gives a favorable opportunity for developing data-driven energy usage prediction models. In this paper, a novel regression-based ensemble prediction model with inbuilt automated optimization for parameters is proposed to predict the demand of electricity. The model explains the 0.998 correlation between the features and their label, and achieved root mean squared error (RMSE) and Normalized Absolute Error as low as 5.508 and 0.0508 respectively. We have also proposed a novel data-driven classification of the energy usage by unsupervised learning through clustering." @default.
- W3104688052 created "2020-11-23" @default.
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- W3104688052 date "2020-08-24" @default.
- W3104688052 modified "2023-10-16" @default.
- W3104688052 title "Energy Usage Prediction for Smart Home with Regression Based Ensemble Model" @default.
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- W3104688052 doi "https://doi.org/10.1109/icimu49871.2020.9243578" @default.
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