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:

Comprehensive preference learning and feature validity for designing energy-efficient residential buildings using machine learning paradigms
Wei Gao, Jalal Alsarraf, Hossein Moayedi, et al.
Applied Soft Computing (2019) Vol. 84, pp. 105748-105748
Closed Access | Times Cited: 105

Showing 1-25 of 105 citing articles:

Machine learning-based approach: global trends, research directions, and regulatory standpoints
Raffaele Pugliese, Stefano Regondi, Riccardo Marini
Data Science and Management (2021) Vol. 4, pp. 19-29
Open Access | Times Cited: 222
Building energy prediction using artificial neural networks: A literature survey
Chujie Lu, Sihui Li, Zhengjun Lu
Energy and Buildings (2021) Vol. 262, pp. 111718-111718
Closed Access | Times Cited: 146
Predicting the settlement of geosynthetic-reinforced soil foundations using evolutionary artificial intelligence technique
Muhammad Nouman Amjad Raja, Sanjay Kumar Shukla
Geotextiles and Geomembranes (2021) Vol. 49, Iss. 5, pp. 1280-1293
Open Access | Times Cited: 125
Buildings' energy consumption prediction models based on buildings’ characteristics: Research trends, taxonomy, and performance measures
Amal A. Al-Shargabi, Abdulbasit Almhafdy, Dina M. Ibrahim, et al.
Journal of Building Engineering (2022) Vol. 54, pp. 104577-104577
Closed Access | Times Cited: 76
Automated machine learning-based framework of heating and cooling load prediction for quick residential building design
Chujie Lu, Sihui Li, Santhan Reddy Penaka, et al.
Energy (2023) Vol. 274, pp. 127334-127334
Closed Access | Times Cited: 44
Exploring the Benefits and Limitations of Digital Twin Technology in Building Energy
Faham Tahmasebinia, Lin Lin, Shuo Wu, et al.
Applied Sciences (2023) Vol. 13, Iss. 15, pp. 8814-8814
Open Access | Times Cited: 41
An intelligent approach for predicting the strength of geosynthetic-reinforced subgrade soil
Muhammad Nouman Amjad Raja, Sanjay Kumar Shukla, Muhammad Umer Arif Khan
International Journal of Pavement Engineering (2021) Vol. 23, Iss. 10, pp. 3505-3521
Open Access | Times Cited: 85
Time works well: Dynamic time warping based on time weighting for time series data mining
Hailin Li
Information Sciences (2020) Vol. 547, pp. 592-608
Closed Access | Times Cited: 83
Data-driven predictive models for residential building energy use based on the segregation of heating and cooling days
Ehsan Kamel, Shaya Sheikh, Xueqing Huang
Energy (2020) Vol. 206, pp. 118045-118045
Closed Access | Times Cited: 74
Multivariate adaptive regression splines model for reinforced soil foundations
Muhammad Nouman Amjad Raja, Sanjay Kumar Shukla
Geosynthetics International (2020) Vol. 28, Iss. 4, pp. 368-390
Closed Access | Times Cited: 70
Data mining with 12 machine learning algorithms for predict costs and carbon dioxide emission in integrated energy-water optimization model in buildings
Majid Emami Javanmard, S.F. Ghaderi, Mohamad Hoseinzadeh
Energy Conversion and Management (2021) Vol. 238, pp. 114153-114153
Closed Access | Times Cited: 63
Predicting and validating the load-settlement behavior of large-scale geosynthetic-reinforced soil abutments using hybrid intelligent modeling
Muhammad Nouman Amjad Raja, Syed Taseer Abbas Jaffar, Abidhan Bardhan, et al.
Journal of Rock Mechanics and Geotechnical Engineering (2022) Vol. 15, Iss. 3, pp. 773-788
Open Access | Times Cited: 42
Regression tree ensemble learning-based prediction of the heating and cooling loads of residential buildings
Nikhil Pachauri, Chang Wook Ahn
Building Simulation (2022) Vol. 15, Iss. 11, pp. 2003-2017
Closed Access | Times Cited: 37
Smart prediction of liquefaction-induced lateral spreading
Muhammad Nouman Amjad Raja, Tarek Abdoun, Waleed El-Sekelly
Journal of Rock Mechanics and Geotechnical Engineering (2023) Vol. 16, Iss. 6, pp. 2310-2325
Open Access | Times Cited: 32
Dynamic adaptive encoder-decoder deep learning networks for multivariate time series forecasting of building energy consumption
Jing Guo, Penghui Lin, Limao Zhang, et al.
Applied Energy (2023) Vol. 350, pp. 121803-121803
Closed Access | Times Cited: 31
Novel Neural Network Optimized by Electrostatic Discharge Algorithm for Modification of Buildings Energy Performance
Arash Mohammadi Fallah, Ehsan Ghafourian, Ladan Shahzamani Sichani, et al.
Sustainability (2023) Vol. 15, Iss. 4, pp. 2884-2884
Open Access | Times Cited: 26
A comparative analysis of machine learning and statistical methods for evaluating building performance: A systematic review and future benchmarking framework
Abdulrahim Ali, Raja Jayaraman, Elie Azar, et al.
Building and Environment (2024) Vol. 252, pp. 111268-111268
Closed Access | Times Cited: 13
Utilizing machine learning and CMIP6 projections for short-term agricultural drought monitoring in central Europe (1900–2100)
Safwan Mohammed, Sana Arshad, Firas Alsilibe, et al.
Journal of Hydrology (2024) Vol. 633, pp. 130968-130968
Open Access | Times Cited: 12
Ranking building design and operation parameters for residential heating demand forecasting with machine learning
Milagros Álvarez-Sanz, Felicia Agatha Satriya, Jon Terés-Zubiaga, et al.
Journal of Building Engineering (2024) Vol. 86, pp. 108817-108817
Open Access | Times Cited: 10
Optimizing building energy performance predictions: A comparative study of artificial intelligence models
Omer A. Alawi, Haslinda Mohamed Kamar, Zaher Mundher Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬
Journal of Building Engineering (2024) Vol. 88, pp. 109247-109247
Closed Access | Times Cited: 8
Enhancing Pan evaporation predictions: Accuracy and uncertainty in hybrid machine learning models
Khabat Khosravi, Aitazaz A. Farooque, Seyed Amir Naghibi, et al.
Ecological Informatics (2024) Vol. 85, pp. 102933-102933
Open Access | Times Cited: 8
Double-Target Based Neural Networks in Predicting Energy Consumption in Residential Buildings
Hossein Moayedi, Amir Mosavi
Energies (2021) Vol. 14, Iss. 5, pp. 1331-1331
Open Access | Times Cited: 42
Prediction and optimization of heating and cooling loads for low energy buildings in Morocco: An application of hybrid machine learning methods
N. Abdou, Y. El Mghouchi, Kaoutar Jraida, et al.
Journal of Building Engineering (2022) Vol. 61, pp. 105332-105332
Closed Access | Times Cited: 34
An ensemble learning-based framework for assessing the energy flexibility of residential buildings with multicomponent energy systems
Adamantios Bampoulas, Fabiano Pallonetto, Eleni Mangina, et al.
Applied Energy (2022) Vol. 315, pp. 118947-118947
Open Access | Times Cited: 29
Study on occupant behaviour using air conditioning of high-rise residential buildings in hot summer and cold winter zone in China
Jiaojiao Duan, Nianping Li, Jinqing Peng, et al.
Energy and Buildings (2022) Vol. 276, pp. 112498-112498
Closed Access | Times Cited: 29
Page 1 - Next Page

Scroll to top