Energy Efficiency through the Implementation of an AI Model to Predict Room Occupancy Based on Thermal Comfort Parameters

Shahira Assem Abdel-Razek, Hanaa Salem Marie, Ali Alshehri and Omar M. Elzeki
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Shahira Assem Abdel-Razek: Department of Architectural Engineering, Faculty of Engineering, Delta University for Science and Technology, Gamasa 35712, Egypt
Hanaa Salem Marie: Department of Electronics and Communications Engineering, Faculty of Engineering, Delta University for Science and Technology, Gamasa 35712, Egypt
Ali Alshehri: Department of Computer Science, University of Tabuk, Tabuk 47512, Saudi Arabia
Omar M. Elzeki: Faculty of Computers and Information, Mansoura University, Mansoura 35516, Egypt

Sustainability, 2022, vol. 14, issue 13, 1-25

Abstract: Room occupancy prediction based on indoor environmental quality may be the breakthrough to ensure energy efficiency and establish an interior ambience tailored to each user. Identifying whether temperature, humidity, lighting, and CO 2 levels may be used as efficient predictors of room occupancy accuracy is needed to help designers better utilize the readings and data collected in order to improve interior design, in an effort to better suit users. It also aims to help in energy efficiency and saving in an ever-increasing energy crisis and dangerous levels of climate change. This paper evaluated the accuracy of room occupancy recognition using a dataset with diverse amounts of light, CO 2 , and humidity. As classification algorithms, K-nearest neighbors (KNN), hybrid Adam optimizer–artificial neural network–back-propagation network (AO–ANN (BP)), and decision trees (DT) were used. Furthermore, this research is based on machine learning interpretability methodologies. Shapley additive explanations (SHAP) improve interpretability by estimating the significance values for each feature for classifiers applied. The results indicate that the KNN performs better than the DT and AO-ANN (BP) classification models have 99.5%. Though the two classifiers are designed to evaluate variations in interpretations, we must ensure that they have accurate detection. The results show that SHAP provides successful implementation following these metrics, with differences detected amongst classifier models that support the assumption that model complexity plays a significant role when predictability is taken into account.

Keywords: smart cities; smart buildings; sustainability; sustainable development goals; healthy cities; energy efficiency; room occupancy; thermal comfort; user-centered design; artificial intelligence (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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