Short-term load forecasting based on deep learning model

被引：0
|
作者：
Kim D. [1 ,2 ]
Jin-Jo H. [1 ,2 ]
Park J.-B. [1 ,2 ]
Roh J.H. [1 ,2 ]
Kim M.S. [1 ,2 ]
机构：
[1] Dept. of Electrical and Electrical Engineering, Konkuk Univerity
[2] Dept. of Electrical and Electrical Engineering, Konkuk Univerity
来源：
Transactions of the Korean Institute of Electrical Engineers | 2019年 / 68卷 / 09期
关键词：
CNN; Deep Learning; LSTM; Short-Term Load Forecasting;
D O I：
10.5370/KIEE.2019.68.9.1094
中图分类号：
学科分类号：
摘要：
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. Copyright © The Korean Institute of Electrical Engineers
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收藏
页码：1094 / 1099
页数：5
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