| Title |
Short-Term Load Forecasting Based on Deep Learning Model |
| Authors |
김도현(Dohyun Kim) ; 조호진(Ho Jin-Jo) ; 김명수(Myung Su Kim) ; 노재형(Jae Hyung Roh) ; 박종배(Jong-Bae Park) |
| DOI |
https://doi.org/10.5370/KIEE.2019.68.9.1094 |
| Keywords |
Deep Learning; Short-Term Load Forecasting; CNN; LSTM |
| Abstract |
This paper presents a Short-Term Long-short term memory Convolutional neural network(STLC) Model that is combined with Convolutional Neural Network(CNN) and Long-Short Term Memory(LSTM). CNN model predicts load pattern using past load profile, LSTM model forecasts load variation depending on temperature and time index. STLC model’s output is hourly load data to combine two model’s outputs. The input parameters of STLC model are composed of time index, weighted weather data, past load data. Weights are calculated based on electricity consumption by main region in South Korea and reflects in the weather data. STLC model is trained with data from 2013 through 2017 and is verified with data from 2018. The STLC model forecasts 1-day hourly load data. Simulation results obtained show the comparison of actual and forecasted load data and also compare with other methods in MAPE(Mean Absolute Percentage Error) to prove accuracy of the proposed model. |