A MicroGrid System Infrastructure Implementing IoT/Big-Data Technologies for Efficient Energy Management in Buildings

Elmouatamid, Abdellatif; NaitMalek, Y.; Ouladsine, Radouane; Bakhouya, Mohamed; Kamoun, Najib El; Khaidar, Mohammed; Zine-Dine, Khalid

doi:10.1007/978-3-030-64565-6_20

Cited by 10 publications

(4 citation statements)

References 49 publications

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“…Additionally, the battery SoC could be an input for micro-grid control strategies in order to have a trade-off between energy demand/response and to select the best time to switch the system from renewable energy to the grid [30].…”

Section: Related Workmentioning

confidence: 99%

A Hybrid Approach for State-of-Charge Forecasting in Battery-Powered Electric Vehicles

et al. 2022

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Nowadays, electric vehicles (EV) are increasingly penetrating the transportation roads in most countries worldwide. Many efforts are oriented toward the deployment of the EVs infrastructures, including those dedicated to intelligent transportation and electro-mobility as well. For instance, many Moroccan organizations are collaborating to deploy charging stations in mostly all Moroccan cities. Furthermore, in Morocco, EVs are tax-free, and their users can charge for free their vehicles in any station. However, customers are still worried by the driving range of EVs. For instance, a new driving style is needed to increase the driving range of their EV, which is not easy in most cases. Therefore, the need for a companion system that helps in adopting a suitable driving style arise. The driving range depends mainly on the battery’s capacity. Hence, knowing in advance the battery’s state-of-charge (SoC) could help in computing the remaining driving range. In this paper, a battery SoC forecasting method is introduced and tested in a real case scenario on Rabat-Salé-Kénitra urban roads using a Twizy EV. Results show that this method is able to forecast the SoC up to 180 s ahead with minimal errors and low computational overhead, making it more suitable for deployment in in-vehicle embedded systems.

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Section: Related Workmentioning

confidence: 99%

A Hybrid Approach for State-of-Charge Forecasting in Battery-Powered Electric Vehicles

et al. 2022

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“…In recent times, modeling batteries through neural networks (NNs) is also been broadly executed. The application zones of NNs deliver significant accessibility to the modeling procedure [9]. It could not necessary to evaluate the model parameter independently at various SOC points.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Bacterial Foraging Optimization with Sparse Autoencoder for Energy Systems

L¹,

Vedaiyan²,

Xavier³

et al. 2023

Computer Systems Science and Engineering

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The Internet of Things (IoT) technologies has gained significant interest in the design of smart grids (SGs). The increasing amount of distributed generations, maturity of existing grid infrastructures, and demand network transformation have received maximum attention. An essential energy storing model mostly the electrical energy stored methods are developing as the diagnoses for its procedure was becoming further compelling. The dynamic electrical energy stored model using Electric Vehicles (EVs) is comparatively standard because of its excellent electrical property and flexibility however the chance of damage to its battery was there in event of overcharging or deep discharging and its mass penetration deeply influences the grids. This paper offers a new Hybridization of Bacterial foraging optimization with Sparse Autoencoder (HBFOA-SAE) model for IoT Enabled energy systems. The proposed HBFOA-SAE model majorly intends to effectually estimate the state of charge (SOC) values in the IoT based energy system. To accomplish this, the SAE technique was executed to proper determination of the SOC values in the energy systems. Next, for improving the performance of the SOC estimation process, the HBFOA is employed. In addition, the HBFOA technique is derived by the integration of the hill climbing (HC) concepts with the BFOA to improve the overall efficiency. For ensuring better outcomes for the HBFOA-SAE model, a comprehensive set of simulations were performed and the outcomes are inspected under several aspects. The experimental results reported the supremacy of the HBFOA-SAE model over the recent state of art approaches.

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“…The integration of modern information and communication technologies, such as IoT and big data technologies, into building management systems can help to improve the performance and efficiency of buildings’ services (e.g., lighting, ventilation) [ 14 ]. Wireless sensor networks, image processing, object tracking, target detection, and identification are some techniques that can be integrated into BMSs.…”

Section: Introductionmentioning

confidence: 99%

Using Stream Data Processing for Real-Time Occupancy Detection in Smart Buildings

Elkhoukhi

Bakhouya

Ouadghiri

et al. 2022

Sensors

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Controlling active and passive systems in buildings with the aim of optimizing energy efficiency and maintaining occupants’ comfort is the major task of building management systems. However, most of these systems use a predefined configuration, which usually do not match the occupants’ preferences. Therefore, occupancy detection is imperative for energy use management mainly in residential and industrial buildings. Most works related to data-driven-based occupancy detection have used batch learning techniques, which need to store first and then train the data. It is not appropriate for a non-stationary environment. Therefore, this work sheds more light on the use of non-stationary machine learning techniques. To this end, three machine learning algorithms for stream data processing are presented, tested, and evaluated in term of accuracy and resources performance (i.e., RAM, CPU), with the aim of predicting the number of occupants in smart buildings. A platform architecture that integrates IoT technologies with stream machine learning is implemented and deployed. The experimental results show the effectiveness of this approach and illustrate that the number of occupants can be predicted with an accuracy of more than 83% and without resource wasting (i.e., time of CPU use varied between 0.04s and 3.85 ⋅ 10−11 GB of RAM could be exploited per hour).

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A MicroGrid System Infrastructure Implementing IoT/Big-Data Technologies for Efficient Energy Management in Buildings

Cited by 10 publications

References 49 publications

A Hybrid Approach for State-of-Charge Forecasting in Battery-Powered Electric Vehicles

A Hybrid Approach for State-of-Charge Forecasting in Battery-Powered Electric Vehicles

Hybrid Bacterial Foraging Optimization with Sparse Autoencoder for Energy Systems

Using Stream Data Processing for Real-Time Occupancy Detection in Smart Buildings

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