Prediction of water evaporation rate for indoor swimming hall using neural networks

Abstract

The forecast of water evaporation rate is important in building and energy sectors. However, due to its stochastic nature and complexity, its forecast is rare in the literature. This paper presents a novel neural network approach to predicting water evaporation rate without occupant information for an indoor swimming hall containing five pools in Finland. Input sensitivity is analyzed and two step ahead predictions are compared. The neural networks using water evaporation rate and a binary representation form of time as inputs outperform other models. Experimental data show rapid fluctuations in water evaporation rate during operating hours although relatively stable during non-operating hours. The developed neural network model, however, is able to adapt to fluctuations and reaches good and acceptable accuracies for one- and two-step ahead predictions even for operating hours. The binary form of time simplifies learning process of neural networks. This paper demonstrates the capability of water evaporation rate forecasting without occupant information by neural networks, which might not be possible with traditional empirical models, and their positive impacts on promoting energy efficiency in various applications in general. Finally, the developed method is sufficiently general and can be extended to other systems for forecasting water evaporation rate as well.