Locational Marginal Price Forecasting Using Convolutional Long-Short Term Memory-Based Generative Adversarial Network

Zhongxia Zhang, Meng Wu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish (US)
Title of host publication2021 IEEE Power and Energy Society General Meeting, PESGM 2021
PublisherIEEE Computer Society
ISBN (Electronic)9781665405072
DOIs
StatePublished - 2021
Externally publishedYes
Event2021 IEEE Power and Energy Society General Meeting, PESGM 2021 - Washington, United States
Duration: Jul 26 2021Jul 29 2021

Publication series

NameIEEE Power and Energy Society General Meeting
Volume2021-July
ISSN (Print)1944-9925
ISSN (Electronic)1944-9933

Conference

Conference2021 IEEE Power and Energy Society General Meeting, PESGM 2021
Country/TerritoryUnited States
CityWashington
Period7/26/217/29/21

ASJC Scopus subject areas

  • Energy Engineering and Power Technology
  • Nuclear Energy and Engineering
  • Renewable Energy, Sustainability and the Environment
  • Electrical and Electronic Engineering

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