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Temperature Forecasting: A Comparison between Parametric and Non-Parametric Models |
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PP: 1099-1108 |
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doi:10.18576/amis/120604
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Author(s) |
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Alaa Sheta,
Ajay Katangur,
Abdelkarim Baareh,
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Abstract |
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| The development of accurate temperature prediction models is essential for not only human life but also
for agricultural, animal life, tourism, and many others. Power consumption and achieving energy efficiency in buildings
also depends on temperature. Although modeling-based regression is one of the most popular approaches, it still suffers
from many difficulties related to the number of available measurements, the order of the model and the non-linearity
of the data. In this paper, we provide a comparison between parametric and non-parametric models for temperature
forecasting. We propose three-model structures to estimate the temperature in Mumbai, the business capital of India.
They are parametric (i.e. Linear Regression (LR), Multi-gene Genetic Programming (MG-GP)) and non-parametric (i.e.
Artificial Neural Networks (ANN)) models. These models are tested on data collected in Mumbai for the year of 2009.
The results show that multi-gene GP model performs relatively well in predicting the temperature with a high degree of
accuracy compared to the LR and ANN techniques. |
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