@article {Talei2021,
	title = {Smart building energy inefficiencies detection through time series analysis and unsupervised machine learning},
	journal = {Energies},
	volume = {14},
	number = {19},
	year = {2021},
	note = {cited By 6},
	abstract = {The climate of Houston, classified as a humid subtropical climate with tropical influences, makes the heating, ventilation, and air conditioning (HVAC) systems the largest electricity consumers in buildings. HVAC systems in commercial buildings are usually operated by a centralized control system and/or an energy management system based on a fixed schedule and scheduled control of a zone setpoint, which is not appropriate for many buildings with changing occupancy rates. Lately, as part of energy efficiency analysis, attention has focused on collecting and analyzing smart meters and building-related data, as well as applying supervised learning techniques, to propose new strategies to operate HVAC systems and reduce energy consumption. On the other hand, unsupervised learning techniques have been used to study the consumption information and profile characterization of different buildings after cluster analysis is performed. This paper adopts a different approach by revealing the power of unsupervised learning to cluster data and unveiling hidden patterns. In this study, we also identify energy inefficiencies after exploring the cluster results of a single building{\textquoteright}s HVAC consumption data and building usage data as part of the energy efficiency analysis. Time series analysis and the K-means clustering algorithm are successfully applied to identify new energy-saving opportunities in a highly efficient office building located in the Houston area (TX, USA). The paper uses 1-year data from a highly efficient Leadership in Energy and Environment Design (LEED)-, Energy Star-, and Net Zero-certified building, showing a potential energy savings of 6\% using the K-means algorithm. The results show that clustering is instrumental in helping building managers identify potential additional energy savings. {\textcopyright} 2021 by the authors. Licensee MDPI, Basel, Switzerland.},
	keywords = {Clusterings, Conditioning systems, energy efficiency, Energy management systems, Energy savings, Energy utilization, Energy-savings, Harmonic analysis, Heating ventilation and air conditioning, Houston, HVAC, Internet of things, K-means clustering, Leadership in energy and environment design building, Leadership in energy and environment designs, Learning algorithms, Office buildings, Potential energy, Time series analysis, Time-series analysis, Times series, Tropics, Unsupervised learning},
	doi = {10.3390/en14196042},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115608885\&doi=10.3390\%2fen14196042\&partnerID=40\&md5=e01e465020f683b8a7ccebf61bf08077},
	author = {Talei, H. and Benhaddou, D. and Gamarra, C. and Benbrahim, H. and Essaaidi, M.}
}
@article {Nemiche2020i,
	title = {Special issue of the 4th world conference on complex systems (Wccs)},
	journal = {Complex Systems},
	volume = {29},
	number = {3},
	year = {2020},
	note = {cited By 0},
	pages = {i},
	doi = {10.25088/ComplexSystems.29.3.i},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85091660029\&doi=10.25088\%2fComplexSystems.29.3.i\&partnerID=40\&md5=c773e461854462f6930bd4c36c84a9f5},
	author = {Nemiche, M. and Essaaidi, M.}
}
@conference {Bekkali2019,
	title = {Securing the Internet of Things (IoT)},
	booktitle = {Proceedings - 2018 International Conference on Wireless Networks and Mobile Communications, WINCOM 2018},
	year = {2019},
	note = {cited By 0},
	doi = {10.1109/WINCOM.2018.8629652},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85062838771\&doi=10.1109\%2fWINCOM.2018.8629652\&partnerID=40\&md5=384b7ebf423613c054b0ec7d89c21c6a},
	author = {Bekkali, A.E. and Boulmalf, M. and Essaaidi, M. and Mezzour, G.}
}
@conference {Talei20171,
	title = {Smart campus energy management system: Advantages, architectures, and the impact of using cloud computing},
	booktitle = {ACM International Conference Proceeding Series},
	volume = {Part F130526},
	year = {2017},
	note = {cited By 0},
	pages = {1-7},
	abstract = {A Microgrid is a subset of smart grid, a small-scale electrical system powered with renewable energy resources that can operate either in a connected or a disconnected mode to/from the main grid. Given that universities are very important electricity consumers, using an academic Microgrid will solve many problems regarding energy usage[1]. The purpose of this paper, is to describe Microgrid components with an emphasis on energy management (EMS). Given its vital role, the paper presents different architectures of an EMS and discusses how cloud computing can be incorporated to the Microgrid architecture to improve the EMS efficiency. The paper concludes with presenting results of EMS data collection using Kaa IoT platform. {\textcopyright} 2017 Association for Computing Machinery.},
	doi = {10.1145/3128128.3128129},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85030324881\&doi=10.1145\%2f3128128.3128129\&partnerID=40\&md5=b3eb81f09422ad7caf4cf46665ae3eca},
	author = {Talei, H. and Essaaidi, M. and Benhaddou, D.}
}
@article {4980748920100419,
	title = {Stacked complementary metasurfaces for ultraslow microwave metamaterials.},
	journal = {Applied Physics Letters},
	volume = {96},
	number = {16},
	year = {2010},
	pages = {164103},
	abstract = {We have experimentally realized at microwaves a dual-band ultraslow regime by constructing a metamaterial based upon the alternative stack of conventional- and complementary-split-ring-resonators-surfaces. The group delay reaches values larger than two orders of magnitude than those obtained when the electromagnetic wave propagates the same thickness in free-space. The ultraslow waves have been initially predicted by a numerical eigenmode analysis and finite-integration frequency domain simulations. Such ultraslow modes can be integrated into free-space technology for spatial delay lines, and traveling wave amplifier as well as sensors due to the enhanced interaction between different beams or radiation and matter. [ABSTRACT FROM AUTHOR]},
	keywords = {Electromagnetic waves, Metamaterials, Microwaves, Resonators, Simulation methods \& models},
	issn = {00036951},
	url = {http://search.ebscohost.com/login.aspx?direct=true\&db=iih\&AN=49807489\&site=ehost-live},
	author = {Navarro-C{\'\i}a, M. and Aznabet, M. and Beruete, M. and Falcone, F. and El Mrabet, O. and Sorolla, M. and Essaaidi, M.}
}
