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• W4289524077 abstract "Predicting building energy consumption needs to be anticipated to save building energy and effectively control the predictions. This study depicted the target building as a physical model to improve the learning performance in a data-scarce environment and proposed a model that uses simulation results as the input for a data-driven model. Case studies were conducted with different quantities of data. The proposed hybrid method proposed in this study showed a higher prediction accuracy showing a cvRMSE of 22.8% and an MAE of 6.1% than using the conventional data-driven method and satisfying the tolerance criteria of ASHRAE Guideline 14 in all the test cases." @default.
• W4289524077 created "2022-08-03" @default.
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• W4289524077 date "2022-08-02" @default.
• W4289524077 modified "2023-10-16" @default.
• W4289524077 title "A Physical Model-Based Data-Driven Approach to Overcome Data Scarcity and Predict Building Energy Consumption" @default.
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• W4289524077 doi "https://doi.org/10.3390/su14159464" @default.
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