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Eguizabal, Markel; Garay-Martinez, Roberto; Flores-Abascal, Iván
Energy reports, December 2022, 2022-12-00, 2022-12-01, Letnik: 8Journal Article
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.
