Application of Hybrid Model in Estimating the Daily Maximum and Minimum Values of the Environment Temperature

The daily forecast of maximum and minimum of the ambient temperature is a problem with high applicability. There have been many proposed methods to forecast these quantities [1,2], however the parameters of the model depend strongly on regional geographical locations and economic indices. Because of that, for each region, the forecast model needs to refine its parameters for better suitability This paper proposes a hybrid model consisting of an neural network MLP (Multi Layer Perception) [3,4] and a linear model to forecast the maximum and minimum of the daily environment temperature [5,6]. The input data is the daily maximum and minimum temperatures and humidity of previous days. Model inputs were further evaluated and selected using an SVD (Singular Value Decomposition) algorithm. The quality of the proposed solutions are tested on actual data (1764 days from 01/01/2010 to 31/10/2014) in Bac Ninh province

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