Open Access Helper

OpenAlex Citation Counts

OpenAlex Citations Logo

OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Interval forecasting of electricity demand: A novel bivariate EMD-based support vector regression modeling framework
Tao Xiong, Yukun Bao, Zhongyi Hu
International Journal of Electrical Power & Energy Systems (2014) Vol. 63, pp. 353-362
Open Access | Times Cited: 95

Showing 1-25 of 95 citing articles:

Probabilistic electric load forecasting: A tutorial review
Tao Hong, Fan Shu
International Journal of Forecasting (2016) Vol. 32, Iss. 3, pp. 914-938
Closed Access | Times Cited: 1027
Forecasting methods in energy planning models
Kumar Biswajit Debnath, Monjur Mourshed
Renewable and Sustainable Energy Reviews (2018) Vol. 88, pp. 297-325
Open Access | Times Cited: 295
Multi-step ahead electricity price forecasting using a hybrid model based on two-layer decomposition technique and BP neural network optimized by firefly algorithm
Deyun Wang, Hongyuan Luo, Olivier Grunder, et al.
Applied Energy (2017) Vol. 190, pp. 390-407
Closed Access | Times Cited: 280
Short term electricity load forecasting using a hybrid model
Jinliang Zhang, Yi‐Ming Wei, Dezhi Li, et al.
Energy (2018) Vol. 158, pp. 774-781
Open Access | Times Cited: 274
An overview of energy demand forecasting methods published in 2005–2015
Iman Ghalehkhondabi, Ehsan Ardjmand, Gary R. Weckman, et al.
Energy Systems (2016) Vol. 8, Iss. 2, pp. 411-447
Closed Access | Times Cited: 195
Hybrid filter–wrapper feature selection for short-term load forecasting
Zhongyi Hu, Yukun Bao, Tao Xiong, et al.
Engineering Applications of Artificial Intelligence (2015) Vol. 40, pp. 17-27
Closed Access | Times Cited: 190
A deep learning model for short-term power load and probability density forecasting
Zhifeng Guo, Kaile Zhou, Xiaoling Zhang, et al.
Energy (2018) Vol. 160, pp. 1186-1200
Closed Access | Times Cited: 172
Short-term electricity price and load forecasting in isolated power grids based on composite neural network and gravitational search optimization algorithm
Azim Heydari, Meysam Majidi Nezhad, Elmira Pirshayan, et al.
Applied Energy (2020) Vol. 277, pp. 115503-115503
Closed Access | Times Cited: 160
Empirical mode decomposition based denoising method with support vector regression for time series prediction: A case study for electricity load forecasting
Yusuf Yaslan, Bahadır Bican
Measurement (2017) Vol. 103, pp. 52-61
Closed Access | Times Cited: 143
A novel composite electricity demand forecasting framework by data processing and optimized support vector machine
Ping Jiang, Ranran Li, Ningning Liu, et al.
Applied Energy (2020) Vol. 260, pp. 114243-114243
Closed Access | Times Cited: 114
Multi-objective algorithm for the design of prediction intervals for wind power forecasting model
Ping Jiang, Ranran Li, Hongmin Li
Applied Mathematical Modelling (2018) Vol. 67, pp. 101-122
Open Access | Times Cited: 113
Electrical load forecasting: A deep learning approach based on K-nearest neighbors
Yunxuan Dong, Xuejiao Ma, Tonglin Fu
Applied Soft Computing (2020) Vol. 99, pp. 106900-106900
Closed Access | Times Cited: 111
A combination method for interval forecasting of agricultural commodity futures prices
Tao Xiong, Chongguang Li, Yukun Bao, et al.
Knowledge-Based Systems (2015) Vol. 77, pp. 92-102
Closed Access | Times Cited: 100
Comprehensive learning particle swarm optimization based memetic algorithm for model selection in short-term load forecasting using support vector regression
Zhongyi Hu, Yukun Bao, Tao Xiong
Applied Soft Computing (2014) Vol. 25, pp. 15-25
Closed Access | Times Cited: 100
A novel hybrid deep neural network model for short‐term electricity price forecasting
Chiou‐Jye Huang, Yamin Shen, Yung‐Hsiang Chen, et al.
International Journal of Energy Research (2020) Vol. 45, Iss. 2, pp. 2511-2532
Open Access | Times Cited: 100
Interval decomposition ensemble approach for crude oil price forecasting
Shaolong Sun, Yuying Sun, Shouyang Wang, et al.
Energy Economics (2018) Vol. 76, pp. 274-287
Closed Access | Times Cited: 93
A New Hybrid Model for Short-Term Electricity Load Forecasting
Md. Rashedul Haq, Zhen Ni
IEEE Access (2019) Vol. 7, pp. 125413-125423
Open Access | Times Cited: 85
Multivariate empirical mode decomposition based hybrid model for day-ahead peak load forecasting
Yanmei Huang, Najmul Hasan, Changrui Deng, et al.
Energy (2021) Vol. 239, pp. 122245-122245
Open Access | Times Cited: 75
An interval decomposition-ensemble approach with data-characteristic-driven reconstruction for short-term load forecasting
Dongchuan Yang, Ju’e Guo, Shaolong Sun, et al.
Applied Energy (2021) Vol. 306, pp. 117992-117992
Closed Access | Times Cited: 75
Mid-term electricity demand forecasting using improved variational mode decomposition and extreme learning machine optimized by sparrow search algorithm
Tian Gao, Dongxiao Niu, Zhengsen Ji, et al.
Energy (2022) Vol. 261, pp. 125328-125328
Closed Access | Times Cited: 43
Interval forecasting of carbon price: A novel multiscale ensemble forecasting approach
Bangzhu Zhu, Chunzhuo Wan, Ping Wang
Energy Economics (2022) Vol. 115, pp. 106361-106361
Closed Access | Times Cited: 43
Improving the forecasting accuracy of interval-valued carbon price from a novel multi-scale framework with outliers detection: An improved interval-valued time series analysis mode
Piao Wang, Zhifu Tao, Jinpei Liu, et al.
Energy Economics (2023) Vol. 118, pp. 106502-106502
Closed Access | Times Cited: 27
Data analytics in the electricity sector – A quantitative and qualitative literature review
Frederik vom Scheidt, Hana Medinová, Nicole Ludwig, et al.
Energy and AI (2020) Vol. 1, pp. 100009-100009
Open Access | Times Cited: 69
A review of the decomposition methodology for extracting and identifying the fluctuation characteristics in electricity demand forecasting
Zhen Shao, Chao Fu, Shanlin Yang, et al.
Renewable and Sustainable Energy Reviews (2016) Vol. 75, pp. 123-136
Closed Access | Times Cited: 67
A novel hybrid forecasting scheme for electricity demand time series
Ranran Li, Ping Jiang, Hufang Yang, et al.
Sustainable Cities and Society (2020) Vol. 55, pp. 102036-102036
Closed Access | Times Cited: 65
Page 1 - Next Page

Scroll to top