K. Karboub, M. Tabaa, F. Monteiro, S. Dellagi, F. Moutaouakkil et al., Automated Diagnosis System for Outpatients and Inpatients with Cardiovascular Diseases, IEEE Sensors Journal, 2020.

S. Saadaoui, A. Khalil, M. Tabaa, M. Chehaitly, F. Monteiro et al., Improved many-to-one architecture based on discrete wavelet packet transform for industrial IoT applications using channel coding, Journal of Ambient Intelligence and Humanized Computing, pp.1-9, 2020.

,

B. Chegari, M. Tabaa, F. Moutaouakkil, E. Simeu, &. H. Medromi et al., Local energy selfsufficiency for passive buildings: Case study of a typical Moroccan building, Journal of Building Engineering, vol.29, pp.218-231, 2020.
URL : https://hal.archives-ouvertes.fr/hal-02650642

M. Tabaa, A. Chakir, F. Moutaouakkil, H. Medromi, and K. Alami, Non-linear management programming for gridconnected hybrid system combined to E-mobility facility, International Journal of Renewable Energy Technology

A. Chakir, M. Tabaa, F. Moutaouakkil, H. Medromi, and K. Alami, Control system for a permanent magnet wind turbine using Particle Swarm Optimization and Proportional Integral controller, International Review of Automatic Control (IREACO)

S. Saadaoui, M. Tabaa, K. Bousmar, F. Monteiro, and &. A. Dandache, DWPT vs OFDM Under a Noisy Industrial Channel, Journal of Ubiquitous Systems & Pervasive Networks, vol.14, issue.1, pp.13-17, 2021.

M. Chehaitly, M. Tabaa, F. Monteiro, and A. , Dandache « A generic, configurable and efficient architecture for first and second generation discrete wavelet packet transform with ultra-high speed and low-cost FPGA implementation, APC Journal, vol.2190, issue.1, p.20090, 2019.

K. Kaouter, T. Mohamed, S. Dellagi, and A. , Dandache and F.Moutaouakkil « Full training convolutional neural network for ECG signals classification, APC Journal, vol.2190, issue.1, p.20055

S. Saadaoui, M. Tabaa, M. Chehaitly, F. Monteiro, and A. , Dandache « Discrete wavelet packet transform-based industrial digital wireless communication systems, Information Journal MDPI, vol.10, issue.3, 2019.

M. Chehaitly, M. Tabaa, F. Monteiro, S. Saadaoui, and A. , Dandache «High throughput configurable architecture based on IDWPT/DWPT for impulse radio wireless communications, In APC Journal, vol.2123, issue.1, p.30023

A. Fentis, L. Bahatti, M. Tabaa, M. Mestari-«-;-cn172]-s, H. Saadaoui et al., Short-term nonlinear autoregressive photovoltaic power forecasting using statistical learning approaches and in-situ observations », 6th International Conference on Innovation and New Trends in Information Systems, INTIS'2017, pp.24-25, 2017.

M. Chehaitly, M. Tabaa, F. Monteiro, A. Dandache, and «. , Fast and configurable architecture dor discret and inverse discrete wavelet packet transform », Colloque GDR SoC SIP 14-16 Juin, vol.2, p.413, 2017.

, La consommation d'énergie journalière, telle que mentionnée dans la section 4, vol.5, p.0

, (121) et l'Eq. (122) nous a permis de dire que nous aurons besoin, d'une part, d'une puissance crête de 3000Wp générée par un champ photovoltaïque composé de 12 éléments de 250Wp chacun

U. Ensuite and . Parc-d'accumulateurs-d'énergie-Électrique, BAT19] composé de 4 batteries de 3000Wh chacune, connectées en série, d'un régulateur de charge avec une efficacité de 98%

Y. Lu, Industry 4.0: A survey on technologies, applications and open research issues, Journal of industrial information integration, vol.6, pp.1-10, 2017.

. Lasi, . Heiner, . Fettke, . Peter, . Kemper et al., Business & information systems engineering, vol.6, pp.239-242, 2014.

P. O. Skobelev and S. Y. Borovik, On the way from Industry 4.0 to Industry 5.0: From digital manufacturing to digital society, pp.307-311, 2017.

V. Özdemir and N. Hekim, Birth of industry 5.0: Making sense of big data with artificial intelligence, Omics: a journal of integrative biology, vol.22, pp.65-76, 2018.

S. Nahavandi, Industry 5.0-A human-centric solution, Sustainability, vol.11, p.4371, 2019.

B. S. Glaser, Made in china 2025 and the future of American industry, 2019.

Y. Lin, Made in China 2025'and China's cross-border strategic M&As in OECD countries, Journal of Chinese Economic and Business Studies, pp.1-24, 2020.

D. Klomfass, Discourse on the 'Made in China 2025'Strategy in Germany, 2020.

K. Fukuda, Science, technology and innovation ecosystem transformation toward society 5.0, International Journal of Production Economics, vol.220, p.107460, 2020.

B. Swain, R. , Y. , and F. , Achieving sustainable development goals: predicaments and strategies, International Journal of Sustainable Development & World Ecology, vol.27, issue.2, pp.96-106, 2020.

T. Salimova, N. Guskova, I. Krakovskaya, and E. Sirota, From industry 4.0 to Society 5.0: Challenges for sustainable competitiveness of Russian industry, IOP Conference Series: Materials Science and Engineering, vol.497, p.12090, 2019.

C. Zhang, . Dong, . Mianxiong, . Ota, and . Kaoru, Enabling Computational Intelligence for Green Internet of Things: Data-Driven Adaptation in LPWA Networking, IEEE Computational Intelligence Magazine, vol.15, issue.1, pp.32-43, 2020.

E. Sisinni, A. Saifullah, S. Han, U. Jennehag, and M. Gidlund, Industrial internet of things: Challenges, opportunities, and directions, IEEE Transactions on Industrial Informatics, vol.14, issue.11, pp.4724-4734, 2018.

M. Younan, E. H. Houssein, M. Elhoseny, and A. A. Ali, Challenges and recommended technologies for the industrial internet of things: A comprehensive review, Measurement, p.107198, 2020.

G. Büchi, M. Cugno, and R. Castagnoli, Smart factory performance and Industry 4.0, Technological Forecasting and Social Change, vol.150, p.119790, 2020.

P. Osterrieder, . Budde, . Lukas, and T. Friedli, The smart factory as a key construct of industry 4.0: A systematic literature review, International Journal of Production Economics, vol.221, p.107476, 2020.

S. Russell and P. Norvig, Artificial intelligence: a modern approach, 2002.

D. Fogel and . Bruce, Evolving artificial intelligence, 1993.

D. Narciso, . Ac, and F. G. Martins, Application of machine learning tools for energy efficiency in industry: A review, Energy Reports, vol.6, pp.1181-1199, 2020.

P. Tubaro and A. A. Casilli, Micro-work, artificial intelligence and the automotive industry, Journal of Industrial and Business Economics, vol.46, pp.333-345, 2019.
URL : https://hal.archives-ouvertes.fr/hal-02148979

W. Hall and J. Pesenti, Growing the artificial intelligence industry in the UK, Culture, Media & Sport and Department for Business, Energy & Industrial Strategy. Part of the Industrial Strategy UK and the Commonwealth, 2017.

Z. X. Guo, Applications of artificial intelligence in the apparel industry: a review, Textile Research Journal, vol.81, pp.1871-1892, 2011.

. El-zaatari, . Shirine, . Marei, L. I. Mohamed, and . Weidong, Cobot programming for collaborative industrial tasks: An overview, Robotics and Autonomous Systems, vol.116, pp.162-180, 2019.

F. Tao, J. Cheng, Q. Qi, M. Zhang, H. Zhang et al., Digital twin-driven product design, manufacturing and service with big data, The International Journal of Advanced Manufacturing Technology, vol.94, issue.9, pp.3563-3576, 2018.

F. Tao, J. Cheng, Q. Qi, M. Zhang, H. Zhang et al., Digital twin-driven product design, manufacturing and service with big data, The International Journal of Advanced Manufacturing Technology, vol.94, issue.9, pp.3563-3576, 2018.

M. Grieves and J. Vickers, Digital twin: Mitigating unpredictable, undesirable emergent behavior in complex systems, pp.85-113, 2017.

M. Y. Mukta, M. A. Rahman, A. T. Asyhari, and M. Z. Bhuiyan, IoT for energy efficient green highway lighting systems: Challenges and issues, Journal of Network and Computer Applications, vol.158, p.102575, 2020.

Y. Saleem, N. Crespi, M. H. Rehmani, and R. Copeland, Internet of things-aided smart grid: technologies, architectures, applications, prototypes, and future research directions, IEEE Access, vol.7, pp.62962-63003, 2019.
URL : https://hal.archives-ouvertes.fr/hal-02339293

G. Dileep, A survey on smart grid technologies and applications, vol.146, pp.2589-2625, 2020.

F. Al-turjman and M. Abujubbeh, IoT-enabled smart grid via SM: An overview, Future Generation Computer Systems, vol.96, pp.579-590, 2019.

E. Ismagilova, L. Hughes, Y. K. Dwivedi, and K. R. Raman, Smart cities: Advances in research-An information systems perspective, International Journal of Information Management, vol.47, pp.88-100, 2019.

F. Zantalis, . Koulouras, . Grigorios, . Karabetsos, and . Sotiris, A review of machine learning and IoT in smart transportation, Future Internet, vol.11, p.94, 2019.

J. Yan, J. Liu, . Tseng, and . Fang-mei, An evaluation system based on the self-organizing system framework of smart cities: A case study of smart transportation systems in China, Technological Forecasting and Social Change, vol.153, p.119371, 2020.

H. J. Ruohomaa, . Salminen, and K. Vesa, Mobility as a service in smart cities-new concept for smart mobility in Industry 4.0 framework, ISPIM Conference Proceedings. The International Society for Professional Innovation Management (ISPIM), pp.1-12, 2019.

M. Wollschlaeger, T. Sauter, and J. Jasperneite, The future of industrial communication: Automation networks in the era of the internet of things and industry 4.0. IEEE Ind, vol.11, pp.17-27, 2017.

M. Cheffena, Propagation channel characteristics of industrial wireless sensor networks, IEEE Antennas Propag. Mag, vol.58, pp.66-73, 2016.

M. K. Lakshmanan and H. Nikookar, A review of wavelets for digital wireless communication, Wirel. Pers. Commun, vol.37, pp.387-420, 2006.

M. Tabaa, A novel transceiver architecture based on wavelet packet modulation for UWB-IR WSN applications, Wirel. Sens. Netw, vol.8, pp.191-209, 2016.

T. Sauter, The three generations of field-level networks-Evolution and compatibility issues, IEEE Trans. Ind. Electron, vol.57, pp.3585-3595, 2010.

M. Andersson, Wireless Technologies for Industrial Applications (Version 2, Connect Blue, 2012.

H. Sasajima, T. Ishikuma, and H. Hayashi, Future IIOT in process automation-Latest trends of standardization in industrial automation, IEC/TC65, Proceedings of the 54th Annual Conference of the Society of Instrument and Control Engineers of Japan (SICE), pp.963-967, 2015.

N. Saleh, A. Kassem, and A. M. Haidar, Energy-efficient architecture for wireless sensor networks in healthcare applications, IEEE Access, vol.6, pp.6478-6486, 2018.

I. Daubechies, Ten Lectures on Wavelets; Society for Industrial and Applied Mathematics, p.357, 1992.

S. Mallat and . Wavelet, Signal Processing, 1989.

Q. Shan, S. Bhatti, I. A. Glover, R. Atkinson, I. E. Portugues et al., Characteristics of impulsive noise in electricity substations, Proceedings of the 2009 17th European Signal Processing Conference, pp.2136-2140, 2009.

D. Sexton, M. Mahony, and M. Lapinski, Radio channel quality in industrial wireless sensor networks, Proceedings of the 2005 Sensors for Industry Conference, pp.88-94, 2005.

S. Luo, N. Polu, Z. Chen, and J. Slipp, RF channel modeling of a WSN testbed for industrial environment, Proceedings of the 2011 IEEE Radio andWireless Symposium, pp.375-378, 2011.

A. A. Saleh and R. Valenzuela, A statistical model for indoor multipath propagation, IEEE J. Sel. Areas Commun, vol.5, pp.128-137, 1987.

J. Karedal, A measurement-based statistical model for industrial ultra-wideband channels, IEEE Trans. Wirel. Commun, 2007.

M. Cheffena, Industrial wireless sensor networks: Channel modeling and performance evaluation, EURASIP J. Wirel. Commun. Netw, vol.297, pp.1-8, 2012.

S. Saadaoui, M. Tabaa, F. Monteiro, A. Dandache, and K. Alami, A new WSN transceiver based on DWPT for WBAN applications, Proceedings of the International conference on Microelectronics ICM, pp.20-23, 2015.

L. Li, Energy-Efficient Design and Implementation of Turbo Codes for Wireless Sensor Network, 2012.

D. Schmidt, M. Berning, and N. Wehn, Error correction in single-hop wireless sensor networks: A case study, Proceedings of the Conference on Design, Automation and Test, pp.1296-1301, 2009.

X. Hu, Z. Chen, and F. Yin, Impulsive noise cancellation for MIMO power line communications, J. Commun, vol.9, pp.241-247, 2014.

H. Oh, H. Nam, and S. Park, Adaptive threshold blanker in an impulsive noise environment, IEEE Trans. Electromagn. Compat, vol.56, pp.1045-1052, 2014.

S. Hakimi and G. A. Hodtani, Generalized maximum correntropy detector for non-Gaussian environments, Int. J. Adapt. Control Signal Process, vol.32, pp.83-97, 2018.

A. Khalil, S. Saadoui, M. Tabaa, M. Chehaitly, F. Monteiro et al., Combined Reed-Solomon and Convolutional codes for IWSN based on IDWPT/DWPT Architecture, Procedia Computer Science, vol.155, pp.666-671, 2019.
URL : https://hal.archives-ouvertes.fr/hal-02414621

R. V. Nee and R. Prasad, OFDM for wireless multimedia communications, 2000.

B. G. Negash and H. Nikookar, Wavelet-based multicarrier transmission over multipath wireless channels, Electronics Letters, vol.36, issue.21, pp.1787-1788, 2000.

F. A. Pinto-benel, M. Blanco-velasco, and F. Cruz-roldán, Throughput analysis of wavelet OFDM in broadband power line communications, IEEE Access, vol.6, pp.16727-16763, 2018.

A. R. Khan and S. M. Gulhane, A highly sustainable multi-band orthogonal wavelet code division multiplexing UWB communication system for underground mine channel, Digital Communications and Networks, vol.4, issue.4, pp.264-76, 2018.

M. Chehaitly, M. Tabaa, F. Monteiro, and A. Dandache, A fast and configurable architecture for discrete wavelet packet transform ». Design of Circuits and Integrated Systems (DCIS), 2015 Conference on, pp.1-6, 2015.

M. Chehaitly, M. Tabaa, F. Monteiro, and A. Dandache, « A VHDL-RTL implementation for a fast and configurable design of inverse discrete wavelet packet transform, Design of Circuits and Integrated Systems (DCIS), 2016.

M. Vishwanath and R. Owens, « A common architecture for the DWT and IDWT, IEEE International Conference on Application Specific Systems, Architectures and Processors (ASAP), vol.8, pp.193-198, 1996.

P. Motra, I. Bora, and . Chakrabarti, « An efficient hardware implementation of DWT and IDWT, IEEE Conference on Convergent Technologies for the Asia-Pacific Region (TENCON), vol.1, pp.95-99, 2003.

T. Denk and K. Parhi, « Architectures for lattice structure based orthonormal discrete wavelet transforms, IEEE International Conference on Application Specific Array Processors, pp.259-270, 1994.

B. Wu and Y. Hu, « An efficient VLSI implementation of the discrete wavelet transform using embedded instruction codes for symmetric filters, IEEE Transactions on Circuits and Systems for Video Technology, vol.13, pp.936-943, 2003.

C. Jing and H. Y. Bin, « Efficient wavelet transform on FPGA using advanced distributed arithmetic, th IEEE International Conference on Electronic Measurement and Instruments (ICEMI'2007), pp.2-512, 2007.

Z. Wu and W. Wang, « Pipelined architecture for FPGA implementation of lifting-based DWT, 2011 International Conference on Electric Information and Control Engineering, pp.1535-1538, 2011.

R. Palero, R. Gironés, and A. Cortes, Journal of VLSI signal processing systems for signal, image and video technology, vol.42, pp.273-284, 2006.

Y. Hu and C. Jong, « A memory-efficient high-throughput architecture for lifting-based multi-level 2-D DWT, IEEE Transactions on Signal Processing, vol.61, issue.20, pp.4975-4987, 2013.

O. Fatemi, S. Bolouki, and . Pipeline, Efficient and Programmable Architecture for the 1-D Discrete Wavelet Transform using Lifting Scheme, The Second Conference On Machine Vision, Image Processing & Applications (MVIP 2003), 2003.

K. B. Sowmya and J. Mathew, Discrete Wavelet Transform Based on Coextensive Distributive Computation on FPGA». Materials Today: Proceedings, Second International Conference on Large Area Flexible Microelectronics (ILAFM 2016): Wearable Electronics, 2016.

H. Hatem, H. El-matbouly, N. Hamdy, and K. Shehata, « VLSI architecture of QMF for DWT integrated system, 44th IEEE Midwest Symposium on Circuits and Systems (MWSCAS), pp.560-563, 2001.

G. Paya, M. M. Peiro, F. J. Ballester, and V. Herrero, A new inverse architecture discrete wavelet packet transform architecture ». IEEE, Signal Processing and Its Applications, vol.2, pp.443-446, 2003.

T. Acharya and . Systolic, Architecture for Discrete Wavelet Transforms, Digital Signal Processing Proceedings, 13th International Conference on, vol.2, pp.571-574, 1997.

M. Farahani and M. Eshghi, Architecture of a Wavelet Packet Transform Using Parallel Filters ». TENCON 2006 -IEEE Region 10 Conference, 2006.

S. Mallat, « A wavelet tour of signal processing, 1999.

A. Mansouri, A. Ahaitouf, and F. Abdi, « An efficient VLSI architecture and FPGA implementation of highspeed and low power 2-D DWT for, Int. J. Comput. Sci. Netw. Secur, vol.9, issue.9, 2009.

, The top 10 causes of death. World Health Organization (WHO)

E. J. Benjamin, M. J. Blaha, S. E. Chiuve, M. Cushman, S. R. Das et al., Heart Disease and Stroke Statistics-2017 Update: A Report From the American Heart Association, Circulation, vol.135, pp.1-458, 2017.

K. Karboub, M. Tabaa, S. Dellagi, A. Dandache, and F. Moutaouakkil, Full training convolutional neural network for ECG signals classification, AIP conference Proceedings, vol.2123, issue.1, 2019.

K. Karboub, M. Tabaa, S. Dellagi, A. Dandache, and F. Moutaouakkil, Emergency patient's arrivals management based on IoT and Dsicrete Simulation using ARENA, Ubiquitous Networking: 5 the International Symposium, 2019.

T. Karboub, Intelligent Patient Monitoring for Arrhythmia and Congestive Failure Patients Using Internet of Things and Convolutional Neural Network, 31th International Conference of Microelectronics, 2019.

K. Karboub, M. Tabaa, S. Dellagi, A. Dandache, and F. Moutaouakkil, Toward Health 4.0: Challenges and Opportunities, International Conference on Innovation and New Trends in Information Technology, 2019.

H. Li, H. Liang, C. Miao, L. Cao, X. Feng et al., Novel ECG signal classification based on KICA nonlinear feature extraction. Circuits, Systems, and Signal Processing, vol.35, pp.1187-97, 2016.

E. D. Übeyli, Combining recurrent neural networks with eigenvector methods for classification of ECG beats, Digital Signal Process, vol.19, issue.2, pp.320-329, 2009.

E. D. Übeyli, Usage of eigenvector methods in implementation of automated diagnostic systems for ECG beats, Digital Signal Process, vol.18, issue.1, pp.33-48, 2008.

T. Li and M. Zhou, ECG Classification Using Wavelet Packet Entropy and Random Forests, Entropy, vol.18, issue.8, p.285, 2016.

A. K. Mishra and S. Raghav, Local fractal dimension based ECG arrhythmia classification, Biomed Signal Process Control, vol.5, issue.2, pp.114-123, 2010.

H. Khorrami and M. Moavenian, A comparative study of DWT, CWT and DCT transformations in ECG arrhythmias classification, 2010.

B. Y?lmaz, M. H. Asyal?, E. Ar?kan, S. Yetkin, and F. Özgen, Sleep stage and obstructive apneaic epoch classification using single-lead ECG, BioMedical Engineering OnLine, vol.9, issue.1, p.39, 2010.

Q. Zhao and L. Zhang, ECG Feature Extraction and Classification Using Wavelet Transform and Support Vector Machines, International Conference on Neural Networks and Brain, 2005.

U. Desai, R. J. Martis, C. G. Nayak, K. Sarika, and G. Seshikala, Machine intelligent diagnosis of ECG for arrhythmia classification using DWT, ICA and SVM techniques, Annual IEEE India Conference (INDICON), 2015.

M. K. Das and S. Ari, ECG arrhythmia recognition using artificial neural network with S-transform based effective features, Annual IEEE India Conference (INDICON), 2013.

S. Khalighi, T. Sousa, G. Pires, and U. Nunes, Automatic sleep staging: A computer assisted approach for optimal combination of features and polysomnographic channels, Expert Systems with Applications, vol.40, issue.17, pp.7046-7059, 2013.

C. Garcia, G. Zikos, and G. Tziritas, A wavelet-based framework for face recognition, Workshop on Advances in Facial Image Analysis and Recognition Technology, 5 th European Conference on Computer Vision, pp.84-92, 1998.

C. C. Liu, D. Q. Dai, and H. Yan, Local Discriminant Wavelet Packet Coordinates for Face Recognition, Journal of Machine learning Research, pp.1165-1195, 2007.

G. B. Moody and R. G. Mark, The impact of the MIT-BIH Arrhythmia Database, IEEE Eng in Med and Biol, vol.20, issue.3, pp.45-50, 2001.

A. L. Goldberger, L. Amaral, L. Glass, J. M. Hausdorff, I. Pch et al., Components of a New Research Resource for Complex Physiologic Signals, vol.101, pp.215-220, 2003.

D. S. Baim, W. S. Colucci, E. S. Monrad, H. S. Smith, R. F. Wright et al., Survival of patients with severe congestive heart failure treated with oral milrinone, J American College of Cardiology, vol.7, issue.3, pp.661-670, 1986.

S. Poungponsri and X. Yu, An adaptive filtering approach for electrocardiogram (ECG) signal noise reduction using neural networks, Neurocomputing, vol.117, pp.206-213, 2013.

F. A. Elhaj, N. Salim, A. R. Harris, T. T. Swee, and T. Ahmed, Arrhythmia recognition and classification using combined linear and nonlinear features of ECG signals, Computer Methods and Programs in Biomedicine, vol.127, pp.52-63, 2016.

F. Y. Abdalla, L. Wu, H. Ullah, G. Ren, A. Noor et al., ECG arrhythmia classification using artificial intelligence and nonlinear and nonstationary decomposition. Signal, Image and Video Processing, 2019.

Y. Aït-sahalia and D. Xiu, Principal Component Analysis of High-Frequency Data, Journal of the American Statistical Association, pp.1-17, 2017.

Z. Ge, Z. Zhu, P. Feng, S. Zhang, J. Wang et al., ECG-Signal Classification Using SVM with Multifeature, 8th International Symposium on Next Generation Electronics (ISNE), 2019.

J. Schmidhuber, Deep Learning in neural networks: an overview, Neural Netw, vol.61, pp.85-117, 2015.

R. O. Duda, P. E. Hart, and D. G. Stork, Pattern Classification, 2001.

I. Saini, D. Singh, and A. Khosla, QRS detection using K-Nearest Neighbor algorithm (KNN) and evaluation on standard ECG databases, Journal of Advanced Research, vol.4, issue.4, pp.331-344, 2013.

L. Jiang, D. Wang, Z. Cai, and X. Yan, Survey of Improving Naive Bayes for Classification, Lecture Notes in Computer Science, pp.134-145, 2007.

W. M. Grove and P. E. Meehl, Comparative efficiency of informal (subjective, impressionistic) and formal (mechanical, algorithmic) prediction procedures: The clinical-statistical controversy, Psychology, Public Policy, and Law, vol.2, issue.2, pp.293-323, 1996.

J. Prel, . Du, G. Hommel, B. Röhrig, and M. Blettner, Confidence Interval or P-Value? Part 4 of a Series on Evaluation of Scientific Publications. Deutsches Aerzteblatt Online, 2009.

, Acquisition and Analysis of an ECG (electrocardiography) Signal | Protocol, pp.2019-2031

S. N. Singh, R. D. Fletcher, S. G. Fisher, B. N. Singh, H. D. Lewis et al.,

, Amiodarone in Patients with Congestive Heart Failure and Asymptomatic Ventricular Arrhythmia, New England Journal of Medicine, vol.333, issue.2, pp.77-82

J. A. Queiroz, A. Junior, F. Lucena, and A. K. Barros, Diagnostic decision support systems for atrial fibrillation based on a novel electrocardiogram approach, Journal of Electrocardiology, vol.51, issue.2, pp.252-259, 2018.

S. S. Xu, M. Mak, and C. Cheung, Towards End-to-End ECG Classification with Raw Signal Extraction and Deep Neural Networks, IEEE Journal of Biomedical and Health Informatics, pp.1-1, 2018.

G. Sannino and G. De-pietro, A deep learning approach for ECG-based heartbeat classification for arrhythmia detection, Future Generation Computer Systems, vol.86, pp.446-455, 2018.

Ö. Y?ld?r?m, P. P?awiak, R. Tan, and U. R. Acharya, Arrhythmia detection using deep convolutional neural network with long duration ECG signals, Computers in Biology and Medicine, 2018.

T. Tuncer, S. Dogan, P. P?awiak, and U. Rajendra-acharya, Automated arrhythmia detection using novel hexadecimal local pattern and multilevel wavelet transform with ECG signals. Knowledge-Based Systems, 2019.

S. Celin and K. Vasanth, Detection and Classification of R-Peak Using Naïve Bayes Classifier, International journal of engineering and technology, vol.7, issue.3, pp.397-403, 2018.

B. Vishwanath, R. V. Pujeri, and G. Devanagavi, Probabilistic principal component analysis-based dimensionality reduction and optimization for arrhythmia classification using ECG signals. Bio-Algorithms and Med-Systems, 0(0), 2019.

H. M. Rai and K. Chatterjee, A unique feature extraction using MRDWT for automatic classification of abnormal heartbeat from ECG big data with Multilayered Probabilistic Neural Network classifier, Applied Soft Computing, 2018.

E. Alickovic and A. Subasi, Effect of Multiscale PCA De-noising in ECG Beat Classification for Diagnosis of Cardiovascular Diseases. Circuits, Systems, and Signal Processing, vol.34, pp.513-533, 2014.

Y. Yeh, W. Wang, and C. W. Chiou, A novel fuzzy c-means method for classifying heartbeat cases from ECG signals, Measurement, vol.43, issue.10, pp.1542-1555, 2010.

S. Patidar, R. B. Pachori, and U. Rajendra-acharya, Automated diagnosis of coronary artery disease using tunable-Q wavelet transform applied on heart rate signals. Knowledge-Based Systems, 2015.

A. Davari-dolatabadi, S. E. Khadem, and B. M. Asl, Automated diagnosis of coronary artery disease (CAD) patients using optimized SVM, Computer Methods and Programs in Biomedicine, vol.138, pp.117-126, 2017.

V. K. Sudarshan, Automated diagnosis of congestive heart failure using dual tree complex wavelet transform and statistical features extracted from 2 s of ECG signals, Comput Biol Med, vol.83, pp.48-58, 2017.

S. L. Oh, E. Y. Ng, R. S. Tan, and U. R. Acharya, Automated diagnosis of arrhythmia using combination of CNN and LSTM techniques with variable length heart beats, Computers in Biology and Medicine, 2018.

A. Fentis, L. Bahatti, M. Tabaa, and M. , Mestari « Short-term nonlinear autoregressive photovoltaic power forecasting using statistical learning approaches and in-situ observations, International Journal of Energy and Environmental Engineering, vol.10, issue.2, pp.189-206, 2019.

A. Fentis, H. Bahatti, M. Tabaa, M. Mestari, and «. , Simple model for short-term photovoltaic power forecasting using statistical learning approach, 3rd IEEE International Conference on Renewable Energies, Power Systems and Green Inclusive Economy, 2018.

A. Fentis, L. Bahatti, M. Tabaa, B. Chouri, M. Mestari et al., Data driven Model for Short Term PV Power Forecasting using Least Square Support Vector Regression, 5th IEEE International Renewable and Sustainable Energy Conference (IRSEC), pp.4-7, 2017.

A. Fentis, L. Bahatti, M. Mestari, M. Tabaa, and B. , Chouri «Simple modèle pour prévision de la puissance photovoltaïque à court terme, Conférence internationale sut les énergies renouvelables et l'efficacité énergétique, pp.8-9

Y. Zhang, M. Beaudin, R. Taheri, H. Zareipour, and D. Wood, Dayahead power output forecasting for small-scale solar photovoltaic electricity generators, IEEE Trans. Smart Grid, vol.6, issue.5, pp.2253-2262, 2015.

Y. Huang, J. Lu, C. Liu, X. Xu, W. Wang et al., Comparative study of power forecasting methods for pv stations, Power System Technology (POWERCON), 2010 International Conference on, pp.1-6, 2010.

P. Bacher, H. Madsen, and H. A. Nielsen, Online short-term solar power forecasting, Solar Energy, vol.83, issue.10, pp.1772-1783, 2009.

P. Ramsami and V. Oree, A hybrid method for forecasting the energy output of photovoltaic systems, Energy Convers Manag, vol.95, pp.406-413, 2015.

A. Mellit, A. M. Pavan, and V. Lughi, Short-term forecasting of power production in a large-scale photovoltaic plant, Solar Energy, vol.105, pp.401-413, 2014.

J. Shi, W. Lee, Y. Liu, Y. Yang, and P. Wang, Forecasting power output of photovoltaic systems based on weather classification and support vector machines, IEEE Transa. Ind. Appl, vol.48, issue.3, pp.1064-1069, 2012.

S. Fonseca, J. G. Oozeki, T. Takashima, T. Koshimizu, G. Uchida et al., Use of support vector regression and numerically predicted cloudiness to forecast power output of a photovoltaic power plant in kitakyushu, japan, Prog. Photovolt. Res. Appl, vol.20, issue.7, pp.874-882, 2012.

R. De-leone, M. Pietrini, and A. Giovannelli, Photovoltaic energy production forecast using support vector regression, Neural Comput. Appl, vol.26, issue.8, pp.1955-1962, 2015.

C. Tao, D. Shanxu, and C. Changsong, Forecasting power output for grid-connected photovoltaic power system without using solar radiation measurement, Power Electronics for Distributed Generation Systems (PEDG), pp.773-777, 2010.

C. Chen, S. Duan, T. Cai, and B. Liu, Online 24-h solar power forecasting based on weather type classification using artificial neural network, Solar Energy, vol.85, issue.11, pp.2856-2870, 2011.

H. Yang, C. Huang, Y. Huang, and Y. Pai, A weatherbased hybrid method for 1-day ahead hourly forecasting of pv power output, IEEE Trans. Sustain. Energy, vol.5, issue.3, pp.917-926, 2014.

P. Mandal, S. T. Madhira, J. Meng, and R. L. Pineda, Forecasting power output of solar photovoltaic system using wavelet transform and artificial intelligence techniques, Procedia Comput. Sci, vol.12, pp.332-337, 2012.

S. Pelland, J. Remund, J. Kleissl, T. Oozeki, and K. De-brabandere, Photovoltaic and solar forecasting: state of the art, IEA PVPS Task, vol.14, pp.1-36, 2013.

S. Fonseca, J. G. Oozeki, T. Takashima, T. Koshimizu, G. Uchida et al., Use of support vector regression and numerically predicted cloudiness to forecast power output of a photovoltaic power plant in kitakyushu, japan, Prog. Photovolt. Res. Appl, vol.20, issue.7, pp.874-882, 2012.

J. Liu, W. Fang, X. Zhang, and C. Yang, An improved photovoltaic power forecasting model with the assistance of aerosol index data, IEEE Trans. Sustain. Energy, vol.6, issue.2, pp.434-442, 2015.

R. De-leone, M. Pietrini, and A. Giovannelli, Photovoltaic energy production forecast using support vector regression, Neural Comput. Appl, vol.26, issue.8, pp.1955-1962, 2015.

C. Chupong and B. Plangklang, Forecasting power output of pv grid connected system in thailand without using solar radiation measurement, Energy Procedia, vol.9, pp.230-237, 2011.

C. Chen, S. Duan, T. Cai, and B. Liu, Online 24-h solar power forecasting based on weather type classification using artificial neural network, Solar Energy, vol.85, issue.11, pp.2856-2870, 2011.

M. Ding, L. Wang, and R. Bi, An ann-based approach for forecasting the power output of photovoltaic system, Procedia Environ. Sci, vol.11, pp.1308-1315, 2011.

J. A. Suykens and J. Vandewalle, Least squares support vector machine classifiers, Neural Process. Lett, vol.9, issue.3, pp.293-300, 1999.

K. Hornik, Approximation capabilities of multilayer feedforward networks, Neural Netw, vol.4, issue.2, pp.251-257, 1991.

O. T. Ogunsola and L. Song, Restoration of long-term missing gaps in solar radiation, Energy Build, vol.82, pp.580-591, 2014.

M. J. Brooks, T. W. Backström, and E. E. Van-dyk, Performance characteristics of a perforated shadow band in the presence of cloud, Solar Energy, vol.139, pp.533-546, 2016.

A. Chakir, M. Tabaa, F. Moutaouakkil, H. Medromi, M. Julien et al., Alami « Optimal Energy management for a grid connected PV-battery system, Press-IF : 3.83 , Indexed JCR

A. Chakir, M. Tabaa, F. Moutaouakkil, H. Medromi, and K. , Alami « A Combined Source and Demand-Side Energy Management System for a Grid-Connected PV-Wind Hybrid System, SCA 2019, LNITI, pp.1-15, 2020.

A. Chakir, M. Tabaa, F. Moutaouakkil, H. Medromi, and K. , Alami «Optimal control design for a grid connected PV-windbattery hybrid system feeding residential loads, Proceedings of the 4th ACM-International Conference on Smart City Applications, p.63, 2019.

A. Chakir, M. Tabaa, F. Moutaouakil, H. Medromi, and K. , Alami «Comparative study of MPPT methods for PV systems: Case of Moroccan house, 30th IEEE International Conference on Microelectronics, ICM 2018, 2018.

A. Chakir, M. Tabaa, F. Moutaouakkil, H. Medromi, K. Alami et al., Vers un réseau électrique intelligent basé sur l'internet des énergies» 7th International Conference on Innovation and New Trends in Information Systems, pp.21-22, 2018.

X. Q. Zhang, The trends, promises and challenges of urbanisation in the world, Habitat Int, 2016.

R. Madlener and Y. Sunak, Impacts of urbanization on urban structures and energy demand: What can we learn for urban energy planning and urbanization management?, Sustain. Cities Soc, 2011.

A. Camero and E. Alba, Smart City and information technology: A review, Cities, 2019.

P. Girardi and A. Temporelli, Smartainability: A Methodology for Assessing the Sustainability of the Smart City, Energy Procedia, 2017.

K. Zhou, S. Yang, and Z. Shao, Energy Internet: The business perspective, 2016.

T. Adefarati and R. C. Bansal, Energizing Renewable Energy Systems and Distribution Generation, Pathways to a Smarter Power System, pp.29-65, 2019.

J. Kartite and M. Cherkaoui, Study of the different structures of hybrid systems in renewable energies: A review, Energy Procedia, vol.157, pp.323-330, 2019.

F. A. Khan, N. Pal, and S. H. Saeed, Review of solar photovoltaic and wind hybrid energy systems for sizing strategies optimization techniques and cost analysis methodologies, Renewable and Sustainable Energy Reviews, 2018.

J. Lian, Y. Zhang, C. Ma, Y. Yang, and E. Chaima, A review on recent sizing methodologies of hybrid renewable energy systems, Energy Conversion and Management, vol.199, p.1, 2019.

D. Kumar and K. Chatterjee, A review of conventional and advanced MPPT algorithms for wind energy systems, Renewable and Sustainable Energy Reviews, vol.55, pp.957-970, 2016.

A. Chakir, M. Tabaa, F. Moutaouakkil, H. Medromi, and K. Alami, Compartive study of MPPT methods for PV systems : Case of Moroccan house, Proceedings of the International Conference on Microelectronics, ICM, 2019.

S. Motahhir, A. E. Hammoumi, and A. E. Ghzizal, The Most Used MPPT Algorithms: Review and the Suitable Low-cost Embedded Board for Each Algorithm, J. Clean. Prod, p.118983, 2019.

L. Olatomiwa, S. Mekhilef, M. S. Ismail, and M. Moghavvemi, Energy management strategies in hybrid renewable energy systems: A review, Renewable and Sustainable Energy Reviews, 2016.

H. Shareef, M. S. Ahmed, A. Mohamed, and E. Hassan, Review on Home Energy Management System Considering Demand Responses, Smart Technologies, and Intelligent Controllers, IEEE Access, vol.6, pp.24498-24509, 2018.

M. S. Alam and S. A. Arefifar, Energy Management in Power Distribution Systems: Review, Classification, Limitations and Challenges, IEEE Access, vol.7, pp.92979-93001, 2019.

M. A. Hannan, A review of internet of energy based building energy management systems: Issues and recommendations, IEEE Access, vol.6, pp.38997-39014, 2018.

A. Chakir, Optimal energy management for a grid connected PV-battery system, Energy Reports, vol.6, 2020.
URL : https://hal.archives-ouvertes.fr/hal-02938854

A. Chakir, M. Tabaa, F. Moutaouakkil, H. Medromi, and K. Alami, Optimal control design for a grid connected PV-Wind-Battery hybrid system feeding residential loads, ACM International Conference Proceeding Series, pp.1-7, 2019.

A. Chakir, M. Tabaa, F. Moutaouakkil, H. Medromi, and K. Alami, A Combined Source and Demand-Side Energy Management System for a Grid-Connected PV-Wind Hybrid System, pp.707-721

A. C. Luna, N. L. Diaz, M. Graells, J. C. Vasquez, and J. M. Guerrero, Mixed-integer-linear-programming-based energy management system for hybrid PV-wind-battery microgrids: Modeling, design, and experimental verification, IEEE Trans. Power Electron, vol.32, issue.4, pp.2769-2783, 2017.

S. Kumaravel and S. Ashok, Optimal power management controller for a stand-alone solar

. Pv/wind, Energy Sources, Part A Recover, Util. Environ. Eff, vol.37, issue.4, pp.407-415, 2015.

A. Aktas, K. Erhan, S. Özdemir, and E. Özdemir, Dynamic energy management for photovoltaic power system including hybrid energy storage in smart grid applications, Energy, vol.162, pp.72-82, 2018.

Y. Xu and X. Shen, Optimal Control Based Energy Management of Multiple Energy Storage Systems in a Microgrid, IEEE Access, vol.6, pp.32925-32934, 2018.

N. Nikmehr and S. Najafi-ravadanegh, Optimal operation of distributed generations in micro-grids under uncertainties in load and renewable power generation using heuristic algorithm, IET Renew. Power Gener, vol.9, issue.8, pp.982-990, 2015.

T. Sarkar, A. Bhattacharjee, H. Samanta, K. Bhattacharya, and H. Saha, Optimal design and implementation of solar PV-wind-biogas-VRFB storage integrated smart hybrid microgrid for ensuring zero loss of power supply probability, Energy Convers. Manag, vol.191, pp.102-118, 2019.

Y. Khawaja, A. Allahham, D. Giaouris, C. Patsios, S. Walker et al., An integrated framework for sizing and energy management of hybrid energy systems using finite automata, Appl. Energy, vol.250, pp.257-272, 2019.

J. Liu, X. Chen, H. Yang, and Y. Li, Energy storage and management system design optimization for a photovoltaic integrated low-energy building, Energy, p.116424, 2019.

A. Kadri, H. Marzougui, A. Aouiti, and F. Bacha, Energy management and control strategy for a DFIG wind turbine/fuel cell hybrid system with super capacitor storage system, Energy, p.116518, 2019.

E. Noghreian and H. R. Koofigar, Power control of hybrid energy systems with renewable sources (windphotovoltaic) using switched systems strategy, Sustain. Energy, Grids Networks, 2019.

S. M. Hakimi and A. Hasankhani, Intelligent energy management in off-grid smart buildings with energy interaction, J. Clean. Prod, 2019.

V. Khare, S. Nema, and P. Baredar, Solar-wind hybrid renewable energy system: A review, 2016.

L. Zhao, W. Wang, L. Zhu, Y. Liu, and A. Dubios, Economic analysis of solar energy development in North Africa, Glob. Energy Interconnect, 2018.

V. Tamrakar, S. C. Gupta, and Y. Sawle, Study of characteristics of single and double diode electrical equivalent circuit models of solar PV module, International Conference on Energy Systems and Applications, ICESA 2015, pp.312-317, 2016.

N. M. Shannan, N. Z. Yahaya, and B. Singh, Single-diode model and two-diode model of PV modules: A comparison, Proceedings -2013 IEEE International Conference on Control System, Computing and Engineering, pp.210-214, 2013.

P. A. James and A. B. Bahaj, Small-Scale Wind Turbines," in Wind Energy Engineering: A Handbook for Onshore and Offshore Wind Turbines, pp.389-418, 2017.

Y. Xia, K. H. Ahmed, and B. W. Williams, Wind turbine power coefficient analysis of a new maximum power point tracking technique, IEEE Trans. Ind. Electron, vol.60, issue.3, pp.1122-1132, 2013.

N. E. Ghossein, J. P. Salameh, N. Karami, M. E. Hassan, and M. B. Najjar, Survey on electrical modeling methods applied on different battery types, 2015 3rd International Conference on Technological Advances in Electrical, Electronics and Computer Engineering, pp.39-44, 2015.

W. S. Putra, B. R. Dewangga, A. Cahyadi, and O. Wahyunggoro, Current estimation using Thevenin battery model, Proceedings -Joint International Conference on Electric Vehicular Technology and Industrial, Mechanical, Electrical and Chemical Engineering, ICEVT 2015 and IMECE, pp.5-9, 2015.

M. Azeroual, A. El, H. E. Makrini, H. Moussaoui, and . El-markhi, Renewable Energy Potential and Available Capacity for Wind and Solar Power in Morocco Towards 2030, J. Eng. Sci. Technol. Rev, vol.11, issue.1, pp.189-198, 2018.

, Data & Statistics -IEA, p.26

, Evolution de la taille moyenne des ménages par milieu de résidence : 1960-2050, p.8, 2020.

B. Chegari, M. Tabaa, F. Moutaouakkil, E. Simeu, and H. , Medromi « Contribution of thermos-physical parameters of thermal insulation and renewable energies to the energy performance of a building in different climates regions of Morocco », IF : 2.37, Indexed JCR

B. Chegari, M. Tabaa, F. Moutaouakkil, E. Simeu, H. Medromi et al., Shading Devices Benefits on Thermal Comfort and Energy Performance of a Residential Building in Different Climates in, SCA 2019, LNITI, pp.1-18, 2020.

B. Chegari, M. Tabaa, F. Moutaouakkil, E. Simeu, and H. , Medromi «Energy savings and thermal comfort benefits of shading devices: case study of a typical moroccan building, Proceedings of the 4th ACM-International Conference on Smart City Applications, p.63, 2019.

B. Chegari, M. Tabaa, F. Moutaouakkil, E. Simeu, H. Medroumi et al., The impact of the thermos-physical parameters of insulation on the energy performance of a building in Morocco, IEEE International Conference of Computer Science and Renewable Energies (ICCSRE), pp.1-6, 2019.
URL : https://hal.archives-ouvertes.fr/hal-02650715

&. Amee and C. Réglementaire, , p.8

, Secrétariat d'Etat chargé du Tourisme, Programme Azur 2020, p.13, 2019.

A. L. Alami,

H. Sghiouri, A. Mezrhab, M. Karkri, and H. Naji, Shading devices optimization to enhance thermal comfort and energy performance of a residential building in Morocco, J. Build. Eng, vol.18, pp.292-302, 2017.

A. S. Jihad and M. Tahiri, Forecasting the heating and cooling load of residential buildings by using a learning algorithm 'gradient descent, Case Stud. Therm. Eng, vol.12, pp.85-93, 2018.

Z. Romani, A. Draoui, and F. Allard, Metamodeling the heating and cooling energy needs and simultaneous building envelope optimization for low energy building design in Morocco, Energy Build, vol.102, pp.139-148, 2015.

H. Sghiouri, M. Charai, A. Mezrhab, and M. Karkri, Comparison of passive cooling techniques in reducing overheating of clay-straw building in semi-arid climate, Build. Simul, pp.0-47, 2019.

L. Zhang, T. Luo, X. Meng, Y. Wang, C. Hou et al., Effect of the thermal insulation layer location on wall dynamic thermal response rate under the air-conditioning intermittent operation, Case Stud. Therm. Eng, vol.10, issue.288, pp.79-85, 2017.

H. Khelifa, D. Belkacem, H. Omar, and I. Omar, ème Séminaire International sur les Energies Nouvelles et Modélisation de l ' Effet Ener gétique de l ' Enveloppe d ' un Bâtiment sur l ' Ambiance Intér ieur e, vol.36000, pp.1-5, 2012.

S. Bendara, Energy Efficiency and Insulation Thickness According to the Compactness Index Case of a Studio Apartment Under Saharan Weather Conditions, J. Sol. Energy Eng, vol.141, issue.4, p.41011, 2019.

R. Lapisa, E. Bozonnet, P. Salagnac, and M. O. Abadie, Optimized design of low-rise commercial buildings under various climates -Energy performance and passive cooling strategies, Build. Environ, vol.132, pp.83-95, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01839977

S. A. Klein, TRNSYS: A Transient System Simulation Program, 2010.

S. A. Klein, Multizone Building modeling with Type56 and TRNBuild -CSTB, vol.6, 2007.

, Centre Scientifique et Technique du Bâtiment -CSTB, p.31, 2019.

. Amee, , vol.91, 2014.

, International Standard -ISO : 13786 Thermal Performance of building components -Dynamic thermal characteristics -Calculcation methods, ISO13786, p.13, 2006.

, Energy performance of buildings-Calculation of energy use for space heating and cooling, ISO13790, vol.1, p.140, 2006.