IoT software infrastructure for Energy  Management and Simulation in Smart Cities

Abstract:   

 This paper presents an IoT software infrastructure  that enables energy management and simulation of new control  policies in a city district. The proposed platform enables the  interoperability and the correlation of (near-)real-time building  energy profiles with environmental data from sensors as well as  building and grid models. In a smart city context, this platform  fulfills i) the integration of heterogeneous data sources at  building and district level, and ii) the simulation of novel energy  policies at district level aimed at the optimization of the energy  usage accounting also for its impact on building comfort. The  platform has been deployed in a real world district and a novel  control policy for the heating distribution network has been  developed and tested. Results are presented and discussed in the  paper.

 

Existing System:

 

 Another relevant impact of IoT is the ease of integration of  heterogeneous data sources that can be exploited to develop  smarter policies. In this way, sensor data can be integrated  with Building Information Models (BIMs) [4], grid models  and Geographical Information Systems (GISs) [5]. BIMs are  3D models with construction and energy characteristics and  they allow detailed energy simulations, that can be used to  evaluate the impact of thermal reshaping on indoor air  temperature. From the other side, building simulations can be  compared with temperatures provided by indoor sensors to  tune energy models, that can be used to evaluate policy impact  on buildings where environmental sensors are not deployed.  In the last decade, several frameworks have been proposed  to exploit IoT technologies at building and house level [6] [7]  [8], and software solutions have been proposed to enable  interoperability a various data formats and protocols [9]  [10] [11]. However, the integration of district models with  sensor data remains a challenge.

 

Proposed System:

 

In a Smart Grid context, Kim et al. [13] presented a datacentric  middleware to allow decentralized monitoring and  control, exploiting a publish/subscribe model [14], which is  appropriate for delivering information but is not yet sufficient  to have data access that is independent of this model. Indeed,  the request/response communication approach is also needed  to provide novel services that can easily retrieve data without  having to wait for new events.  In [15], a distributed software infrastructure for general  purpose services in power systems is presented. The software  architecture enables the interoperability across heterogeneous  devices by creating a secure peer-to-per network.  Differently from these solutions, the IoT platform presented  in this paper aims at creating a virtual model of a city district  for providing Smart City services. Hence, considering data  coming from IoT devices deployed across the energy  distribution networks is not yet enough. Indeed, such data  have to be integrated and correlated together with information,  often geo-referenced, about buildings.

 

Conclusion: 

 

This work presented a novel IoT platform for city district data  management and energy flow simulations. In particular, this  platform is instrumental i) to integrate heterogeneous IoT  devices for monitoring and management of a whole city  district; ii) to share building and energy network resources,  both for visualization and simulation of energy policies, at  building and district level; and iii) to assess the quality of the  energy model of buildings.  The presented platform is designed and developed  following the micro-service paradigm, and it is able to scale  up reliably. Further, the use of shared open standards of the Web makes it easy to integrate into existing systems. The  platform is ready to send actuation commands to the devices  deployed in the district. For instance, we are currently testing  it on the DH scheduling control system. In this case, scheduled  on-off heating switching times are sent to IoT devices to  control heat exchangers. The results of this experimentation  are preliminary and will be discussed in future work.

 

References:

 

[1] World Health Organization, “Urban population growth,” [Online].  Available:  http://www.who.int/gho/urban_health/situation_trends/urban_population  growth text/en/. [Accessed February 2016].

 

[2] K. Ashton, “That ‘Internet-of-Things’ Thing,” [Online]. Available:  http://www.rfidjournal.com/articles/view?4986. [Accessed September  2015].

 

[3] D. Giusto, A. Iera, G. Morabito and L. Atzori, The internet of things:  20th Tyrrhenian workshop on digital communications, Springer Science  & Business Media, 2010.

 

[4] C. Eastman, C. M. Eastman, P. Teicholz and R. Sacks, BIM handbook:  A guide to building information modeling for owners, managers,  designers, engineers and contractors, John Wiley & Sons, 2011.

 

[5] R. F. Tomlinson, “A geographic information system for regional  planning,” in In GA Stewart,(ed.: Symposium on Land Evaluation,  Commonwealth Scientific and Industrial Research Organization,  Melbourne, 1968.

 

[6] A. Kamilaris, A. Pitsillides and V. Trifa, “The smart home meets the  web of things,” International Journal of Ad Hoc and Ubiquitous  Computing, vol. 7, no. 3, pp. 145-154, 2011.

 

[7] E. Patti, A. Acquaviva, M. Jahn, F. Pramudianto, R. Tomasi, D.  Rabourdin, J. Virgone and E. Macii, “Event-driven user-centric  middleware for energy-efficient buildings and public spaces,” IEEE Syst.  J., DOI: 10.1109/JSYST.2014.2302750.

 

[8] D. Bonino, E. Castellina and F. Corno, “The DOG gateway: enabling  ontology-based intelligent domotic environments,” IEEE Trans.  Consum. Electron., vol. 54, no. 4, pp. 1656-1664, 2008.