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OpenAlex Citation Counts

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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:

A Smart Forecasting Approach to District Energy Management
Barış Yüce, Monjur Mourshed, Yacine Rezgui
Energies (2017) Vol. 10, Iss. 8, pp. 1073-1073
Open Access | Times Cited: 27

Showing 1-25 of 27 citing articles:

Forecasting Energy Use in Buildings Using Artificial Neural Networks: A Review
Jason Runge, Radu Zmeureanu
Energies (2019) Vol. 12, Iss. 17, pp. 3254-3254
Open Access | Times Cited: 208
A systematic review towards integrative energy management of smart grids and urban energy systems
Zhuang Zheng, Muhammad Shafique, Xiaowei Luo, et al.
Renewable and Sustainable Energy Reviews (2023) Vol. 189, pp. 114023-114023
Closed Access | Times Cited: 54
Weather forecasts for microgrid energy management: Review, discussion and recommendations
Agustín Agüera-Pérez, José-Carlos Palomares-Salas, Juan José González de la Rosa, et al.
Applied Energy (2018) Vol. 228, pp. 265-278
Closed Access | Times Cited: 144
A Comparative Analysis of Machine Learning Approaches for Short-/Long-Term Electricity Load Forecasting in Cyprus
Davut Solyalı
Sustainability (2020) Vol. 12, Iss. 9, pp. 3612-3612
Open Access | Times Cited: 111
A Review of Deep Learning Techniques for Forecasting Energy Use in Buildings
Jason Runge, Radu Zmeureanu
Energies (2021) Vol. 14, Iss. 3, pp. 608-608
Open Access | Times Cited: 75
Systematic Review of Deep Learning and Machine Learning for Building Energy
Sina Ardabili, Leila Abdolalizadeh, Csaba Makó, et al.
Frontiers in Energy Research (2022) Vol. 10
Open Access | Times Cited: 62
Methods of Forecasting Electric Energy Consumption: A Literature Review
Roman V. Klyuev, Ирбек Джабраилович Моргоев, Angelika Morgoeva, et al.
Energies (2022) Vol. 15, Iss. 23, pp. 8919-8919
Open Access | Times Cited: 62
Predictive artificial neural network models to forecast the seasonal hourly electricity consumption for a University Campus
Jihui Yuan, Craig Farnham, Chikako Azuma, et al.
Sustainable Cities and Society (2018) Vol. 42, pp. 82-92
Closed Access | Times Cited: 81
Forecasting household electric appliances consumption and peak demand based on hybrid machine learning approach
Ejaz Ul Haq, Xue Lyu, Youwei Jia, et al.
Energy Reports (2020) Vol. 6, pp. 1099-1105
Open Access | Times Cited: 54
A scoping review of deep neural networks for electric load forecasting
Nicolai Bo Vanting, Zheng Ma, Bo Nørregaard Jôrgensen
Energy Informatics (2021) Vol. 4, Iss. S2
Open Access | Times Cited: 46
Review of Smart City Energy Modeling in Southeast Asia
Md Shafiullah, R. Saidur, Binash Imteyaz, et al.
Smart Cities (2022) Vol. 6, Iss. 1, pp. 72-99
Open Access | Times Cited: 25
The Fuzzy Logic Method to Efficiently Optimize Electricity Consumption in Individual Housing
Sébastien Bissey, Sébastien Jacques, Jean-Charles Le Bunetel
Energies (2017) Vol. 10, Iss. 11, pp. 1701-1701
Open Access | Times Cited: 36
Methodology for the Quantification of the Impact of Weather Forecasts in Predictive Simulation Models
Eva Lucas Segarra, Hu Du, Germán Ramos Ruiz, et al.
Energies (2019) Vol. 12, Iss. 7, pp. 1309-1309
Open Access | Times Cited: 31
The comparison of some advanced control methods for energy optimization and comfort management in buildings
Ahmad Esmaeilzadeh, Mohammad Reza Zakerzadeh, Aghil Yousefi‐Koma
Sustainable Cities and Society (2018) Vol. 43, pp. 601-623
Closed Access | Times Cited: 28
Machine Learning Approach Electric Appliance Consumption and Peak Demand Forecasting of Residential Customers Using Smart Meter Data
Fikirte Zemene Abera, Vijayshri Khedkar
Wireless Personal Communications (2020) Vol. 111, Iss. 1, pp. 65-82
Closed Access | Times Cited: 22
Performance Evaluation of Forecasting Strategies for Electricity Consumption in Buildings
Sarah Hadri, Mehdi Najib, Mohamed Bakhouya, et al.
Energies (2021) Vol. 14, Iss. 18, pp. 5831-5831
Open Access | Times Cited: 19
Systematic review of deep learning and machine learning for building energy
Sina Ardabili, Leila Abdilalizadeh, Csaba Makó, et al.
(2022)
Open Access | Times Cited: 12
Short-term load forecasting coupled with weather profile generation methodology
Guangya Zhu, T.T. Chow, Norman C. F. Tse
Building Services Engineering Research and Technology (2017) Vol. 39, Iss. 3, pp. 310-327
Closed Access | Times Cited: 21
Assessment Framework of Provincial Carbon Emission Peak Prediction in China: An Empirical Analysis of Hebei Province
Wei Li, Lei Du
Polish Journal of Environmental Studies (2019) Vol. 28, Iss. 5, pp. 3753-3765
Open Access | Times Cited: 20
Short-Term Load Forecasting for Campus Building with Small-Scale Loads by Types Using Artificial Neural Network
Sung-Jun Baek, Sung‐Guk Yoon
(2019), pp. 1-5
Closed Access | Times Cited: 12
Smart Building and Current Technologies
Ming Hu
Springer eBooks (2020), pp. 75-91
Closed Access | Times Cited: 6
Evaluation of Different Development Possibilities of Distribution Grid State Forecasts
Jessica Hermanns, Marcel Modemann, Kamil Korotkiewicz, et al.
Energies (2020) Vol. 13, Iss. 8, pp. 1891-1891
Open Access | Times Cited: 4
Heuristics-Based Energy Demand Forecasting with Scarce Data in the Department of Huila, Colombia
Juan Cuenca, Diego Palacios-Castro, Rodolfo García-Sierra
Ingeniería e Investigación (2024) Vol. 44, Iss. 3, pp. e109551-e109551
Open Access
Neural network models for predicting urban albedo of urban surfaces with different reflection directional properties
Jihui Yuan, Yasuhiro Shimazaki, Shingo Masuko
Energy Reports (2023) Vol. 10, pp. 2850-2864
Open Access | Times Cited: 1
Implementation of Load Demand Prediction Model for a Domestic Load Center Using Different Machine Learning Algorithms—A Comparison
M. Pratapa Raju, A. Jaya Laxmi
Lecture notes in networks and systems (2022), pp. 445-467
Closed Access | Times Cited: 1
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