Energy Usage Prediction for Smart Home with Regression Based Ensemble Model
| Autor: | Norziana Jamil, Mohammad Shamsul Hoque, Nowshad Amin, Sharul Azim Saharudin |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Electric power distribution
Ensemble forecasting Computer science business.industry 020206 networking & telecommunications 02 engineering and technology computer.software_genre law.invention Random forest law Home automation Ventilation (architecture) 0202 electrical engineering electronic engineering information engineering Unsupervised learning 020201 artificial intelligence & image processing Electricity Data mining business Cluster analysis computer |
| Zdroj: | 2020 8th International Conference on Information Technology and Multimedia (ICIMU). |
| DOI: | 10.1109/icimu49871.2020.9243578 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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