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Received November 13, 2009
Accepted January 10, 2010
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Heat consumption forecasting using partial least squares, artificial neural network and support vector regression techniques in district heating systems
Department of Chemical Engineering, Hanyang University, Seoul 133-791, Korea 1Department of Chemical Engineering, Seoul National University of Technology, Seoul 135-743, Korea 2Department of Civil Engineering, Hanyang University, Seoul 133-791, Korea
Korean Journal of Chemical Engineering, July 2010, 27(4), 1063-1071(9), 10.1007/s11814-010-0220-9
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Abstract
Effective management of district heating networks depends upon the correct forecasting of heat consumption during a certain period. In this work short-term forecasting for the amount of heat consumption is performed first to validate the three forecasting methods: partial least squares (PLS) method, artificial neural network (ANN), and support vector regression (SVR) method. Based on the results of short-term forecasting, one-week ahead forecasting was performed for the Suseo district heating network. Data of heat consumption and ambient temperature during January and February in 2007 and 2008 were employed as training elements. The heat consumption estimated was compared with actual one in the Suseo area to validate the forecasting models.
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Min KG, Han CH, Chang KS, Korean J. Chem. Eng., 5(2), 2437 (1999)
Park CK, Korean Operations Res. Manage. Soc., 23(2), 75 (2006)
