TY - GEN
T1 - Locational Marginal Price Forecasting Using Convolutional Long-Short Term Memory-Based Generative Adversarial Network
AU - Zhang, Zhongxia
AU - Wu, Meng
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - In wholesale electricity markets, locational marginal prices (LMPs) are strongly spatio-temporal correlated. Most previous data-driven studies on LMP forecasting only leveraged temporal correlations among historical LMPs, very few of them learned the spatial correlations to improve forecasting accuracy. In this paper, a convolutional long-short term memory (CLSTM)-based generative adversarial network (GAN) is proposed to forecast LMPs from market participants' perspective. Historical LMPs of different price nodes are organized into a 3-dimensional (3D) tensor which stores the spatio-temporal correlations among LMPs. The LMP forecasting problem is formulated as a spatiotemporal sequence-to-sequence forecasting problem. The proposed approach is verified through case studies using public historical LMPs from Midcontinent Independent System Operator (MISO) and ISO-New England (ISO-NE), in comparison with other state-of-the-art LMP prediction approaches.
AB - In wholesale electricity markets, locational marginal prices (LMPs) are strongly spatio-temporal correlated. Most previous data-driven studies on LMP forecasting only leveraged temporal correlations among historical LMPs, very few of them learned the spatial correlations to improve forecasting accuracy. In this paper, a convolutional long-short term memory (CLSTM)-based generative adversarial network (GAN) is proposed to forecast LMPs from market participants' perspective. Historical LMPs of different price nodes are organized into a 3-dimensional (3D) tensor which stores the spatio-temporal correlations among LMPs. The LMP forecasting problem is formulated as a spatiotemporal sequence-to-sequence forecasting problem. The proposed approach is verified through case studies using public historical LMPs from Midcontinent Independent System Operator (MISO) and ISO-New England (ISO-NE), in comparison with other state-of-the-art LMP prediction approaches.
UR - http://www.scopus.com/inward/record.url?scp=85124136870&partnerID=8YFLogxK
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U2 - 10.1109/PESGM46819.2021.9637861
DO - 10.1109/PESGM46819.2021.9637861
M3 - Conference contribution
AN - SCOPUS:85124136870
T3 - IEEE Power and Energy Society General Meeting
BT - 2021 IEEE Power and Energy Society General Meeting, PESGM 2021
PB - IEEE Computer Society
T2 - 2021 IEEE Power and Energy Society General Meeting, PESGM 2021
Y2 - 26 July 2021 through 29 July 2021
ER -