| Title |
A Study on the Summer Peak Load Forecasting Based on Average Temperature |
| Authors |
Rae-Jun Park ; Kyung-Bin Song |
| DOI |
http://dx.doi.org/10.5207/JIEIE.2018.32.4.024 |
| Keywords |
Summer Peak Load ; Peak Load Forecasting ; Temperature Sensitivity ; Multiple Regression Analysis |
| Abstract |
Summer peak load is strongly affected by temperature variability. For this reason, various forecast methods considering the characteristics of temperature change have been studied. However, research on how to deal with past peak loads at very high or low temperatures is insufficient. In order to solve this problem, the past summer peak loads are converted to the converted peak loads at the 30 years average temperature. In this paper, summer peak load forecasting algorithm using a multiple regression analysis is proposed that used for the converted summer peak load as a dependent variable and GDP, a population as independent variables. In the case study, the summer peak loads from 2013 to 2017 are forecasted used for a proposed algorithm that the improved average prediction accuracy was 97.24%. |