Simplified model for the short-term forecasting of heat loads in buildings

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eguizabal_simplified_2022.pdf (1.25 MB)
Fecha
2022-10-25
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Elsevier Ltd
google-scholar
Resumen
A data-driven model is used to predict one-hour ahead heat loads based on present and recent history of weather and heat loads. A computationally inexpensive method is built to deliver load forecasting based on existing data quality and resolution from smart meters. Optimal model formulation is discussed and optimized at 4-hour historical values. The model is trained and tested against synthetic data from a building energy simulation, resulting in absolute error <4% and R2 values in the range of 0.92 to 0.94.
Palabras clave
Building
Heating demand
Lagged value
Regression model
Descripción
Materias
Cita
Eguizabal, M., Garay-Martinez, R., & Flores-Abascal, I. (2022). Simplified model for the short-term forecasting of heat loads in buildings. Energy Reports, 8, 79-85. https://doi.org/10.1016/J.EGYR.2022.10.224
URI
https://hdl.handle.net/20.500.14454/3267
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