Forecasting electricity consumption based on machine learning to improve performance: A case study for the organization of petroleum exporting countries (OPEC)-Reference-Cited by-同舟云学术

Forecasting electricity consumption based on machine learning to improve performance: A case study for the organization of petroleum exporting countries (OPEC)

Published:2020-09 Issue: Volume:86 Page:106737
ISSN:0045-7906
Container-title:Computers & Electrical Engineering
language:en
Short-container-title:Computers & Electrical Engineering

Author:

Khan Abdullah,Chiroma Haruna,Imran Muhammad,khan Asfandyar,Bangash Javed Iqbal,Asim Muhammad,Hamza Mukhtar F.,Aljuaid Hanan

Publisher

Elsevier BV

Subject

Electrical and Electronic Engineering,General Computer Science,Control and Systems Engineering

Reference30 articles.

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

2. Going Green: transmission Grids as Enablers of the Transition to a Low-Carbon European Economy;Henry;Power and Energy Magazine, IEEE,2014

3. Electricity Consumption and Economic Growth: a New Relationship with Significant Consequences?;Hirsh;The Electricity Journal,2015

4. The relationship between natural gas consumption and economic growth in OPEC members;Solarin;Renewable and Sustainable Energy Reviews,2016

5. Organization of the Petroleum Exporting Countries (OPEC). (2015). Brief history. Retrieved fromhttp://www.opec.org/opec_web/en/about_us/24.htm. Accessed, 12 October , 2012.

Cited by 27 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 discrete time-varying grey Fourier model with fractional order terms for electricity consumption forecast;Energy;2024-06

2. Data Science and Machine Learning Applications in Technology and Energy Management;2024 20th CSI International Symposium on Artificial Intelligence and Signal Processing (AISP);2024-02-21

3. Modeling and forecasting electricity consumption amid the COVID-19 pandemic: Machine learning vs. nonlinear econometric time series models;Sustainable Cities and Society;2023-11

4. Bayesian-Optimization-Based Long Short-Term Memory (LSTM) Super Learner Approach for Modeling Long-Term Electricity Consumption;Sustainability;2023-09-07

5. Machine Learning and Bagging to Predict Midterm Electricity Consumption in Saudi Arabia;Applied System Innovation;2023-07-10