A Study of Climate Data Using Least Square Method and the Fast Fourier Transform

Abstract

Energy utilization in buildings continues to increase as the quality of life increases. Buildings are built in an environment in which the climate surrounding a building is a factor influencing the energy requirements for the building services. The higher the thermal stress due to external conditions, the higher the energy required to provide consistent building services. This paper discusses the different types of climate analyses for Subang. The climate data has been calculated using averaged hourly values per month. The least squares method and fast Fourier transform have been used to explore the data further and elucidate climatic data. The climatic data collected and presented include temperature distribution, solar radiation, relative humidity distribution, rainfall distribution, wind-speed distribution and pressure distribution were presented. The least square polynomial of degree four and ten were chosen to represent the climate data. The least square error and the norm of the residual for these two polynomials were the smallest obtained amongst the other polynomials. The coefficients of determination were also calculated. The Fast Fourier Transform (FFT) from the MATLAB toolbox was used to evaluate patterns within the climate data. The FFT shows the Fourier coefficient on the complex plane. These studies reveal the climate patterns that need to be considered for optimum energy utilization in buildings.