On the use of Deep Learning Approaches for Occupancy prediction in Energy Efficient Buildings

Elkhoukhi, Hamza; Bakhouya, Mohamed; Hanifi, Majdoulayne; Ouadghiri, Driss El

doi:10.1109/irsec48032.2019.9078164

Cited by 11 publications

(4 citation statements)

References 19 publications

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“…Deep learning-based occupancy forecasting techniques have been investigated [ 65 ]. The first two recurrent neural network (RNN) based methods, long short-term memory (LSTM) and gated recurrent unit (GRU), have been evaluated and compared in terms of accuracy and root mean square error.…”

Section: Methodsmentioning

confidence: 99%

Internet-of-Things Based Hardware-in-the-Loop Framework for Model-Predictive-Control of Smart Building Ventilation

Kharbouch

Berouine²,

Elkhoukhi³

et al. 2022

Sensors

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In this work, a Hardware-In-the-Loop (HIL) framework is introduced for the implementation and the assessment of predictive control approaches in smart buildings. The framework combines recent Internet of Things (IoT) and big data platforms together with machine-learning algorithms and MATLAB-based Model Predictive Control (MPC) programs in order to enable HIL simulations. As a case study, the MPC algorithm was deployed for control of a standalone ventilation system (VS). The objective is to maintain the indoor Carbon Dioxide (CO2) concentration at the standard comfort range while enhancing energy efficiency in the building. The proposed framework has been tested and deployed in a real-case scenario of the EEBLab test site. The MPC controller has been implemented on MATLAB/Simulink and deployed in a Raspberry Pi (RPi) hardware. Contextual data are collected using the deployed IoT/big data platform and injected into the MPC and LSTM machine learning models. Occupants’ numbers were first forecasted and then sent to the MPC to predict the optimal ventilation flow rates. The performance of the MPC control over the HIL framework has been assessed and compared to an ON/OFF strategy. Results show the usefulness of the proposed approach and its effectiveness in reducing energy consumption by approximately 16%, while maintaining good indoor air quality.

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Section: Methodsmentioning

confidence: 99%

Internet-of-Things Based Hardware-in-the-Loop Framework for Model-Predictive-Control of Smart Building Ventilation

Kharbouch

Berouine²,

Elkhoukhi³

et al. 2022

Sensors

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show abstract

“…Regarding root mean square error (RMSE), the model achieved a value of 13.31%. According to Elkhoukhi et al [ 143 ], their main objective was to evaluate the accuracy of forecasting occupant numbers using contemporary DL methods, including a RNN and LSTM. Hitimana et al [ 144 ] used a multivariate time series to predict occupancy patterns in regression forecasting.…”

Section: Data Analysis Approachmentioning

confidence: 99%

Occupancy Prediction in IoT-Enabled Smart Buildings: Technologies, Methods, and Future Directions

Khan,

Zedadra,

Guerrieri

et al. 2024

Sensors

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In today’s world, a significant amount of global energy is used in buildings. Unfortunately, a lot of this energy is wasted, because electrical appliances are not used properly or efficiently. One way to reduce this waste is by detecting, learning, and predicting when people are present in buildings. To do this, buildings need to become “smart” and “cognitive” and use modern technologies to sense when and how people are occupying the buildings. By leveraging this information, buildings can make smart decisions based on recently developed methods. In this paper, we provide a comprehensive overview of recent advancements in Internet of Things (IoT) technologies that have been designed and used for the monitoring of indoor environmental conditions within buildings. Using these technologies is crucial to gathering data about the indoor environment and determining the number and presence of occupants. Furthermore, this paper critically examines both the strengths and limitations of each technology in predicting occupant behavior. In addition, it explores different methods for processing these data and making future occupancy predictions. Moreover, we highlight some challenges, such as determining the optimal number and location of sensors and radars, and provide a detailed explanation and insights into these challenges. Furthermore, the paper explores possible future directions, including the security of occupants’ data and the promotion of energy-efficient practices such as localizing occupants and monitoring their activities within a building. With respect to other survey works on similar topics, our work aims to both cover recent sensory approaches and review methods used in the literature for estimating occupancy.

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“…Though air-side measurements are affected by the installation of external sensors (e.g., temperature, air flow, pressure, and power consumption) to identify the context driven of the system and conclude the coefficient performance for a given context, several measurements should be established on the airside to create a direct and indirect relationship between the system's operation and other external parameters such as weather conditions and occupants' activities. In fact, this is the main research key that researchers focus on to reduce the energy consumption of the AC system, depending on occupant detection and activity identification [43]. On the other hand, a relationship is established between weather conditions and the optimal operation of an AC system using virtual sensing techniques to detect faults.…”

Section: Major Common Faults In Air Conditioning Systemsmentioning

confidence: 99%

Air Conditioning Systems Fault Detection and Diagnosis-Based Sensing and Data-Driven Approaches

et al. 2023

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The air conditioning (AC) system is the primary building end-use contributor to the peak demand for energy. The energy consumed by this system has grown as fast as it has in the last few decades, not only in the residential section but also in the industry and transport sectors. Therefore, to combat energy crises, urgent actions on energy efficiency should be taken to support energy security. Consequently, the faults in AC system components increase energy consumption due to the degradation of the system’s performance and the losses in the energy conversion procedure. In this work, AC system fault detection and diagnosis (FDD) methods are investigated to propose analytic tools to identify faults and provide solutions to those problems. The analysis of existing work shows that data-driven approaches are more accurate for both soft and hard fault detection and diagnosis in AC systems. Therefore, the proposed methods are not accurate for simultaneous fault detection, while in some works, authors tested the method with several faults separately without investigating scenarios that combine more than one fault. Moreover, this study shows that integrating data-driven approaches requires deploying an optimal sensing and measurement architecture that can detect a maximum number of faults with minimally deployed sensors. The new sensing, information, and communication technologies are discussed for their integration in AC system monitoring in order to optimize system operation and detect faults.

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On the use of Deep Learning Approaches for Occupancy prediction in Energy Efficient Buildings

Cited by 11 publications

References 19 publications

Internet-of-Things Based Hardware-in-the-Loop Framework for Model-Predictive-Control of Smart Building Ventilation

Internet-of-Things Based Hardware-in-the-Loop Framework for Model-Predictive-Control of Smart Building Ventilation

Occupancy Prediction in IoT-Enabled Smart Buildings: Technologies, Methods, and Future Directions

Air Conditioning Systems Fault Detection and Diagnosis-Based Sensing and Data-Driven Approaches

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