ANNS_AC_1

Version 1.0 (105 MB) by Amr Sadek

This application leverages machine learning models to validate air conditioner energy efficiency tests.

5 Downloads

Updated 1 Jun 2025

This application utilizes machine learning models to validate air conditioner energy efficiency tests conducted in compliance with ISO 5151. The models analyze key performance metrics, including input power, electrical current, total cooling capacity, and the energy efficiency ratio. By leveraging advanced data-driven techniques, the application enhances accuracy in assessing AC performance, ensuring reliable validation of test results.

Cite As

Amr Sadek (2025). ANNS_AC_1 (https://www.mathworks.com/matlabcentral/fileexchange/181225-anns_ac_1), MATLAB Central File Exchange. Retrieved July 19, 2025.

Sadek, A. M., et al. “Machine Learning Models for Validating the Self-Declaration Conformity Assessment: Risk Evaluation.” Machine Learning and Soft Computing, Springer Nature Singapore, 2025, pp. 231–42, https://doi.org/10.1007/978-981-96-6400-9_17.

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AlMutiri, Yousef M., et al. “Modeling the Air Conditioner Performance Tests Using Artificial Neural Network Simulator (ANNS-AC).” Artificial Intelligence Applications and Innovations, Springer Nature Switzerland, 2024, pp. 125–38, https://doi.org/10.1007/978-3-031-63223-5_10.

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MATLAB Release Compatibility

Created with R2025a

Compatible with any release

Platform Compatibility

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Version	Published	Release Notes
1.0	1 Jun 2025		Download

MLA	Sadek, A. M., et al. “Machine Learning Models for Validating the Self-Declaration Conformity Assessment: Risk Evaluation.” Machine Learning and Soft Computing, Springer Nature Singapore, 2025, pp. 231–42, https://doi.org/10.1007/978-981-96-6400-9_17.
APA	Sadek, A. M., AlRashidi, M. S., AlMutiri, Y. M., AlQahtani, A. R. M., & AlQahtani, T. S. (2025). Machine Learning Models for Validating the Self-declaration Conformity Assessment: Risk Evaluation. In Machine Learning and Soft Computing (pp. 231–242). Springer Nature Singapore. Retrieved from http://dx.doi.org/10.1007/978-981-96-6400-9_17
BibTeX	@inbook{Sadek_2025, title={Machine Learning Models for Validating the Self-declaration Conformity Assessment: Risk Evaluation}, ISBN={9789819664009}, ISSN={1865-0937}, url={http://dx.doi.org/10.1007/978-981-96-6400-9_17}, DOI={10.1007/978-981-96-6400-9_17}, booktitle={Machine Learning and Soft Computing}, publisher={Springer Nature Singapore}, author={Sadek, A. M. and AlRashidi, Mohammed S. and AlMutiri, Yousef M. and AlQahtani, AbdulRahman M. and AlQahtani, Turki S.}, year={2025}, pages={231–242} }

MLA	AlMutiri, Yousef M., et al. “Modeling the Air Conditioner Performance Tests Using Artificial Neural Network Simulator (ANNS-AC).” Artificial Intelligence Applications and Innovations, Springer Nature Switzerland, 2024, pp. 125–38, https://doi.org/10.1007/978-3-031-63223-5_10.
APA	AlMutiri, Y. M., AlRashidi, M. S., AlQahtani, A. R. M., Alqahtani, T. S., & Sadek, A. M. (2024). Modeling the Air Conditioner Performance Tests Using Artificial Neural Network Simulator (ANNS-AC). In Artificial Intelligence Applications and Innovations (pp. 125–138). Springer Nature Switzerland. Retrieved from http://dx.doi.org/10.1007/978-3-031-63223-5_10
BibTeX	@inbook{AlMutiri_2024, title={Modeling the Air Conditioner Performance Tests Using Artificial Neural Network Simulator (ANNS-AC)}, ISBN={9783031632235}, ISSN={1868-422X}, url={http://dx.doi.org/10.1007/978-3-031-63223-5_10}, DOI={10.1007/978-3-031-63223-5_10}, booktitle={Artificial Intelligence Applications and Innovations}, publisher={Springer Nature Switzerland}, author={AlMutiri, Yousef M. and AlRashidi, Mohammed S. and AlQahtani, AbdulRahman M. and Alqahtani, Turki S. and Sadek, A. M.}, year={2024}, pages={125–138} }