Abstract
Domestic hot water heat use prediction modelling is an important instrument for increasing energy efficiency in many buildings. This article addressed hourly domestic hot water heat use prediction, using a Norwegian hotel as a case study. Since the information available for buildings may vary, two widespread situations with different input variables were studied. For the first situation, the prediction is based only on data obtained from historical measured domestic hot water heat use. For the second situation, additional variables that affect domestic hot water heat use were applied. These variables were determined using the Wrapper approach. The Wrapper approach showed that factors related to the guests presence have the most significant influence on the domestic hot water heat use in the hotel. Nevertheless, daily data about the number of guests booked at the hotel did not appear to be informative enough for precise hourly modelling. Therefore, to improve the accuracy of the prediction, it was proposed to use an artificial variable. This artificial variable explained the hourly intensity of the guests domestic hot water use. In order to select the best model for the domestic hot water heat use prediction, ten advanced time series and machine learning techniques were tested based on the criteria of models adequacy. For both considered situations, the Prophet model showed the best results with R2 equal to 0.76 for the first situation, and 0.83 the second situation.
Highlights
Buildings are one of the largest categories of energy consumers in the European Union (EU) [1]
Historical data about hourly domestic hot water (DHW) heat use are available for building owners for many types of non-residential buildings in Norway, including hotels
The variation of DHW heat use in different periods of time should be taken into account
Summary
Buildings are one of the largest categories of energy consumers in the European Union (EU) [1]. Most building constructions have complex technical systems, to realize a comfortable living condition for people. Among these systems, the domestic hot water (DHW) system is an integrated component of every building. DHW systems are significant consumers of energy. DHW systems typically consume 25–35% of the total energy use [4]. Substantial opportunities for energy savings in buildings can be achieved by improving the performance of DHW systems [6]. Raida, Modeling EM structures in the neural network toolbox of MATLAB, IEEE Antennas Propag. [49] M.H. Beale, M.T. Hagan, H.B. Demuth, Neural network toolbox, User’s Guide, MathWorks, 2 (2010) 77-81
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