PLUG-N-HARVEST Architecture for Secure and Intelligent Management of Near-Zero Energy Buildings

Building Automation (BA) is key to encourage the growth of more sustainable cities and smart homes. However, current BA systems are not able to manage new constructions based on Adaptable/Dynamic Building Envelopes (ADBE) achieving near-zero energy-efficiency. The ADBE buildings integrate Renewable Energy Sources (RES) and Envelope Retrofitting (ER) that must be managed by new BA systems based on Artificial Intelligence (AI) and Internet of Things (IoT) through secure protocols. This paper presents the PLUG-N-HARVEST architecture based on cloud AI systems and security-by-design IoT networks to manage near-zero ADBE constructions in both residential and commercial buildings. To demonstrate the PLUG-N-HARVEST architecture, three different real-world pilots have been considered in Germany, Greece and Spain. The paper describes the Spain pilot of residential buildings including the deployment of IoT wireless networks (i.e., sensors and actuators) based on Zwave technology to enable plug-and-play installations. The real-world tests showed the high efficiency of security-by-design Internet communications between building equipment and cloud management systems. Moreover, the results of cloud intelligent management demonstrate the improvements in both energy consumption and comfort conditions.

[1]  Anastasios I. Dounis,et al.  Advanced control systems engineering for energy and comfort management in a building environment--A review , 2009 .

[2]  Iakovos Michailidis,et al.  Model-based and model-free “plug-and-play” building energy efficient control , 2015 .

[3]  Francesco Goia Dynamic Building Envelope Components and nearly Zero Energy Buildings: Theoretical and experimental analysis of concepts, systems and technologies for an adaptive building skin , 2013 .

[4]  Lingfeng Wang,et al.  Multi-objective optimization for decision-making of energy and comfort management in building automation and control , 2012 .

[5]  Iakovos Michailidis,et al.  Adaptive Optimal Control for Large-Scale Nonlinear Systems , 2017, IEEE Transactions on Automatic Control.

[6]  Andrea Zanella,et al.  Internet of Things for Smart Cities , 2014, IEEE Internet of Things Journal.

[7]  Terence P. Speed,et al.  Statistical Models: Theory and Practice, Revised Edition by David A. Freedman , 2010 .

[8]  Antonio F. Gómez-Skarmeta,et al.  DCapBAC: embedding authorization logic into smart things through ECC optimizations , 2016, Int. J. Comput. Math..

[9]  José Domingo Álvarez,et al.  Optimizing building comfort temperature regulation via model predictive control , 2013 .

[10]  Aleš Krainer,et al.  Integral control system of indoor environment in continuously occupied spaces , 2012 .

[11]  Alexandros G. Fragkiadakis,et al.  An IoT Middleware for Enhanced Security and Privacy: The RERUM Approach , 2016, 2016 8th IFIP International Conference on New Technologies, Mobility and Security (NTMS).

[12]  Anupriya Ankolekar,et al.  DOLCE ergo SUMO: On foundational and domain models in the SmartWeb Integrated Ontology (SWIntO) , 2007, J. Web Semant..

[13]  Antonio F. Gómez-Skarmeta,et al.  Holistic Privacy-Preserving Identity Management System for the Internet of Things , 2017, Mob. Inf. Syst..

[14]  Muneer Bani Yassein,et al.  Smart homes automation using Z-wave protocol , 2016, 2016 International Conference on Engineering & MIS (ICEMIS).

[15]  Liu Yang,et al.  Thermal comfort and building energy consumption implications - A review , 2014 .

[16]  Nursyarizal Mohd Nor,et al.  A review on optimized control systems for building energy and comfort management of smart sustainable buildings , 2014 .

[17]  F.达里 Cloud enabled building automation system , 2014 .

[18]  Valeriu Manuel Ionescu The analysis of the performance of RabbitMQ and ActiveMQ , 2015, 2015 14th RoEduNet International Conference - Networking in Education and Research (RoEduNet NER).

[19]  Iakovos Michailidis,et al.  Proactive control for solar energy exploitation: A german high-inertia building case study , 2015 .

[20]  Luis Pérez-Lombard,et al.  A review on buildings energy consumption information , 2008 .

[21]  Yulian Rangelov,et al.  Application of the Fuzzy Logic for Information Analysis in Communication Networks and Systems for Power Utility Automation , 2018, 2018 20th International Symposium on Electrical Apparatus and Technologies (SIELA).

[22]  Iakovos Michailidis,et al.  Automated control calibration exploiting exogenous environment energy: An Israeli commercial building case study , 2016 .

[23]  Hyunjoo Kim,et al.  Analysis of an energy efficient building design through data mining approach , 2011 .

[24]  Sneha A. Dalvi,et al.  Internet of Things for Smart Cities , 2017 .

[25]  Nathan Mendes,et al.  Predictive controllers for thermal comfort optimization and energy savings , 2008 .

[26]  Fu Xiao,et al.  Data mining in building automation system for improving building operational performance , 2014 .

[27]  Manfred Morari,et al.  Use of model predictive control and weather forecasts for energy efficient building climate control , 2012 .

[28]  Antonio F. Gómez-Skarmeta,et al.  Shifting Primes: Optimizing Elliptic Curve Cryptography for Smart Things , 2012, 2012 Sixth International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing.

[29]  Mihai Sanduleac,et al.  Enabling novel smart grid energy services with the nobel grid architecture , 2017, 2017 IEEE Manchester PowerTech.

[30]  Klaus Moessner,et al.  Context-aware stream processing for distributed IoT applications , 2015, 2015 IEEE 2nd World Forum on Internet of Things (WF-IoT).

[31]  Iakovos Michailidis,et al.  A "plug and play" computationally efficient approach for control design of large-scale nonlinear systems using cosimulation: a combination of two "ingredients" , 2014, IEEE Control Systems.

[32]  Marco Perino,et al.  Switching from static to adaptable and dynamic building envelopes: A paradigm shift for the energy efficiency in buildings , 2015 .