Compression Spatial Interpolation Models in Estimation the Heating and Cooling Degree Days in Iran
IntroductionNowadays with regard to the patterns of the energy consumption, identify the factors and components that affect it, has been the most important approach of the geosciences issue. The cooling and heating degree days (CDD & HDD) are the Indices for potential of energy consumption in any region. Therefore identify Irans different region from scope of this climatic indices can be very useful in planning of energy consumption. Spatial interpolation methods are frequently used to estimate values of climatic data in locations where they are not measured. However, very little research has been investigated the relative performance of different interpolation methods in data of CDD & HDD in Iran. Actually, it has importantly practical significance to as far as possibly improve the accuracy of interpolation results for climatic data, especially in Iran that have inadequate number of meteorological station. Therefore the main objective of this paper is comparison of three spatial interpolation methods for estimating CDD and HDD as a climatic variable in not measured location.
MethodThis paper focuses on determination the relative performance of 3 different spatial interpolation methods for estimating CDD and HDD of Iran and then select optimum method for estimation this variable as a climatic index of potential of energy consumption. HDD and CDD provides a simple metric for quantifying the amount of heating and cooling that buildings in a particular location need over a certain period (e.g. a particular month or year) it calculated over a period of time (typically a month) by adding up the differences between each day's mean daily temperature to base temperature (65 and 70˚F For HDD and CDD, respectively). The mean seasonal observed data of cooling and heating needs, are collected from 30 meteorological stations for the period 1965- 2005. Inverse distance weighting (IDW), ordinary kriging (OK) with different Semivariogram and tension Spiline, are selected as interpolated methods. Moreover, cross-validation (CV) is used to evaluate the performance of different spatial interpolation methods. 4 statistical criteria named: Mean Absolute Error (MAE), Mean Bias Error (MBE), Root Mean Square Error (RMSE), an acceptability index (d-Willmote) and finally coefficient of determine (R2), are used to analyze and compare the performance of these interpolation methods.
Results And DiscussionThe results indicated that all models have taken overestimate error to prediction of HDD and underestimate error to prediction CDD. Also we found that the average of observed data is 345.5 for HDD and 157 for CDD and the average of estimated data from IDW, Kriging and Spiline for HDD and CDD is 363, 363, 346 and 138,142, 148, respectively. And can dedicate the best model for estimating the general and total attitude such as average is tension Spiline. But for prediction of spatial detail and spatial construction that is most important in this paper, the Krigin model with the spherical Semivariogram has minimum MAE, MBE, RMSE (89, 17, 128) for HDD and (69, -15, 90) for CDD and is considered as the optimum model for interpolating this climatic variable.
ConclusionThis paper results indicated that the Kriging model with the spherical Semivariogram is more appropriate to estimate the CDD and HDD. The output of this paper can help decision maker to have more clear identification of the climatologically potential for the energy consumption in Iran. And could be useful for climate adapted architecting.
Journal of Climate Research, Volume:4 Issue: 13, 2016
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