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- W2890188851 endingPage "726" @default.
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- W2890188851 abstract "Energy consumption in buildings is increasing because of social development and urbanization. Forecasting the energy consumption in buildings is essential for improving energy efficiency and sustainable development, and thereby reducing energy costs and environmental impact. This investigation presents a comprehensive review of machine learning (ML) techniques for forecasting energy consumption time series using actual data. Real-time data were collected from a smart grid that was installed in an experimental building and used to evaluate the efficacy and effectiveness of statistical and ML techniques. Well-known artificial intelligence techniques were used to analyze energy consumption in single and ensemble scenarios. An in-depth review and analysis of the ‘hybrid model’ that combines forecasting and optimization techniques is presented. The comprehensive comparison demonstrates that the hybrid model is more accurate than the single and ensemble models. Both the accuracy of prediction and the suitability for use of these models are considered to support users in planning energy management." @default.
- W2890188851 created "2018-09-27" @default.
- W2890188851 creator A5043651248 @default.
- W2890188851 creator A5089359026 @default.
- W2890188851 date "2018-12-01" @default.
- W2890188851 modified "2023-10-14" @default.
- W2890188851 title "Forecasting energy consumption time series using machine learning techniques based on usage patterns of residential householders" @default.
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- W2890188851 doi "https://doi.org/10.1016/j.energy.2018.09.144" @default.
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