Forecasting day-ahead electricity prices using a new integrated model-Reference-Cited by-同舟云学术

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Forecasting day-ahead electricity prices using a new integrated model

Published:2019-02 Issue: Volume:105 Page:541-548
ISSN:0142-0615
Container-title:International Journal of Electrical Power & Energy Systems
language:en
Short-container-title:International Journal of Electrical Power & Energy Systems

Author:

Zhang Jin-Liang,Zhang Yue-Jun,Li De-Zhi,Tan Zhong-Fu,Ji Jian-Fei

Funder

National Natural Science Foundation of China

Science and Technology Project of State Grid Corporation of China

National Program for Support of Top-notch Young Professionals

Changjiang Scholars Program of the Ministry of Education of China

Hunan Youth Talent Program, China Scholarship Council

Fundamental Research Funds for the Central Universities

Publisher

Elsevier BV

Subject

Electrical and Electronic Engineering,Energy Engineering and Power Technology

Reference50 articles.

1. Electricity price forecast using Combinatorial Neural Network trained by a new stochastic search method;Abedinia;Energy Convers Manage,2015

2. Forecasting day-ahead electricity prices in Europe: the importance of considering market integration;Lago;Appl Energy,2018

3. Electricity price forecasting: a review of the state-of-the-art with a look into the future;Weron;Int J Forecast,2014

4. A hybrid forecasting model based on date-framework strategy and improved feature selection technology for short-term load forecasting;Jiang;Energy,2017

5. Forecasting spot electricity prices: deep learning approaches and empirical comparison of traditional algorithms;Lago;Appl Energy,2018

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