| Title |
Short-Term Load Forecasting Using Hourly Temperature Sensitivity on Summer Weekdays |
| Authors |
김정환(Jung-Hwan Kim) ; 김규한(Kyu-Han Kim) ; 이흥석(Heung-Seok Lee) ; 박준호(June Ho Park) |
| DOI |
https://doi.org/10.5370/KIEE.2019.68.9.1045 |
| Keywords |
Short-Term Load Forecasting; Hourly Temperature Sensitivity; Artificial Neural Network |
| Abstract |
Load forecasting is important to determine the market price and the supply reserve. The electric load in summer is influenced by meteorological elements, especially most affected by temperature. Therefore, the temperature directly related to the cooling loads must be precisely considered to improve the accuracy of the load forecasting. In this paper, we propose the load forecasting model for 24 hours during summer weekdays based on the artificial neural network. To improve the forecasting accuracy, we classify the weekdays into two groups of Monday and Tuesday-Friday, where electric load pattern is similar within each group. Furthermore, the hourly temperature sensitivity was calculated and used as an input variable. The simulation results show that this proposed approach can be applied to forecast the electric load in summer weekdays accurately. |