Locational marginal price forecasting in a day-ahead power market using spatiotemporal deep learning network-Reference-Cited by-同舟云学术

Locational marginal price forecasting in a day-ahead power market using spatiotemporal deep learning network

Published:2020-12 Issue: Volume:24 Page:100406
ISSN:2352-4677
Container-title:Sustainable Energy, Grids and Networks
language:en
Short-container-title:Sustainable Energy, Grids and Networks

Author:

Hong Ying-Yi,Taylar Jonathan V.,Fajardo Arnel C.

Funder

Ministry of Science and Technology, Taiwan

Publisher

Elsevier BV

Subject

Electrical and Electronic Engineering,Energy Engineering and Power Technology,Renewable Energy, Sustainability and the Environment,Control and Systems Engineering

Reference62 articles.

1. Electricity price forecasting by a hybrid model, combining wavelet transform, ARMA and kernel-based extreme learning machine methods;Yang;Appl. Energy,2017

2. Day-ahead electricity price forecasting in a grid environment;Li;IEEE Trans. Power Syst.,2007

3. Retail electricity pricing based on the value of electricity for consumers;Monfared;Sustain. Energy Grids Netw.,2019

4. Economic valuation of smart grid investments on electricity markets;De Oliveira-De Jesus;Sustain. Energy Grids Netw.,2018

5. Electricity price forecasting in deregulated markets: A review and evaluation;Aggarwal;Int. J. Electr. Power Energy Syst.,2009

Cited by 23 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. A hybrid forecasting method considering the long-term dependence of day-ahead electricity price series;Electric Power Systems Research;2024-10

2. LMP Sensitivity Calculation with Load Uncertainty by Using Combined Heuristic and Brute-force Technique;Journal of Electrical Engineering & Technology;2023-09-12

3. Deep learning-based forecasting of the automatic Frequency Reserve Restoration band price in the Iberian electricity market;Sustainable Energy, Grids and Networks;2023-09

4. Predictive Analysis of Energy Consumption and Electricity Demand Using Machine Learning Techniques;2023 International Conference on Smart Systems for applications in Electrical Sciences (ICSSES);2023-07-07

5. Day-Ahead Zonal Electricity Price Forecasting using 1D-LSTM with Neighbouring Zones Data;2023 IEEE Belgrade PowerTech;2023-06-25