@Article{ee.2022.020118,
AUTHOR = {Yunlei Zhang, Ruifeng Cao, Danhuang Dong, Sha Peng, Ruoyun Du, Xiaomin Xu},
TITLE = {Deep Learning Network for Energy Storage Scheduling in Power Market Environment Short-Term Load Forecasting Model},
JOURNAL = {Energy Engineering},
VOLUME = {119},
YEAR = {2022},
NUMBER = {5},
PAGES = {1829--1841},
URL = {http://www.techscience.com/energy/v119n5/48939},
ISSN = {1546-0118},
ABSTRACT = {In the electricity market, fluctuations in real-time prices are unstable, and changes in short-term load are
determined by many factors. By studying the timing of charging and discharging, as well as the economic benefits
of energy storage in the process of participating in the power market, this paper takes energy storage scheduling as
merely one factor affecting short-term power load, which affects short-term load time series along with time-of-use
price, holidays, and temperature. A deep learning network is used to predict the short-term load, a convolutional
neural network (CNN) is used to extract the features, and a long short-term memory (LSTM) network is used to
learn the temporal characteristics of the load value, which can effectively improve prediction accuracy. Taking the
load data of a certain region as an example, the CNN-LSTM prediction model is compared with the single LSTM
prediction model. The experimental results show that the CNN-LSTM deep learning network with the participation
of energy storage in dispatching can have high prediction accuracy for short-term power load forecasting.},
DOI = {10.32604/ee.2022.020118}
}