Energies | |
A Hybrid Neural Network Model for Power Demand Forecasting | |
Ling Liu1  Youngjun Jeon2  Myoungsoo Kim3  Wonik Choi3  | |
[1] College of Computing, Georgia Institute of Technology, Atlanta, GA 30332, USA;Dawul Geoinfo Co., Seoul 08377, Korea;Department of Information and Communication Engineering, Inha University, Incheon 22212, Korea; | |
关键词: power demand forecasting; hybrid neural network; artificial neural network; LSTM; CNN; | |
DOI : 10.3390/en12050931 | |
来源: DOAJ |
【 摘 要 】
The problem of power demand forecasting for the effective planning and operation of smart grid, renewable energy and electricity market bidding systems is an open challenge. Numerous research efforts have been proposed for improving prediction performance in practical environments through statistical and artificial neural network approaches. Despite these efforts, power demand forecasting problems remain to be a grand challenge since existing methods are not sufficiently practical to be widely deployed due to their limited accuracy. To address this problem, we propose a hybrid power demand forecasting model, called (c, l)-Long Short-Term Memory (LSTM) + Convolution Neural Network (CNN). We consider the power demand as a key value, while we incorporate c different types of contextual information such as temperature, humidity and season as context values in order to preprocess datasets into bivariate sequences consisting of
【 授权许可】
Unknown