Energy Efficient Buildings
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ELEDIA Research Center

EEB Project

EEB is a European Community project aimed at increasing the energy efficiency of buildings and more generally of urban districts, through real-time monitoring and control of environmental parameters and the consumption / production of the necessary energy. The consequence of energy efficiency is savings not only for families, but for entire cities and nations, not to mention the benefits to the environment due to lower energy demand.

Go to EEB Website for more information

EEB Project Partners

E-DISTRIBUZIONE S.P.A.
EXPERT SYSTEM S.P.A.
LABORATORIO DELLE IDEE S.R.L.
LEONARDO S.P.A.
LOGICAL SYSTEM S.P.A.
MEDIAITALY S.R.L.
METODA S.P.A
POLITECNICO DI MILANO
POLITECNICO DI TORINO
POWER EVOLUTION
TELECOM ITALIA S.P.A
UNIVERSITÀ DEGLI STUDI DI TRENTO

supported by

EEB Project Objectives

  • Improve the energy efficiency of existing buildings (including historical buildings) and urban districts, by exploiting wireless technologies for the pervasive monitoring and control of energy consumption and production;
  • Make available information flow to building managers and end-users on remote and distributed platforms through an wireless infrastructure, data management technologies, and web services;
  • Develop a decision-making support system for enforcing policies and for the optimal management of energy consumption and production;
  • Enable augmented reality of the building, to simplify maintenance and energy awareness of users;
  • Estimate and Predict energy saving opportunities through a recommendation system.

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Environmental Monitoring

through Wireless Sensor Networks and Internet of Things.
In particular for buildings where the extensive retrofitting is not allowed.

Wireless Monitoring System Architecture

The monitored area is a rectangular space 10 [m] long and 8 [m] wide which simulates a large museum room and it is equipped with a standard thermo-regulation system and with both natural and artificial lighting.

The wireless sensors are located at different vertical levels in order to enable 3D mapping and analysis:

  • 8 Sensor IoT devices;
  • 1 Gateway IoT device;
  • 1 Control Unit.

The nodes periodically send the measures of temperature, humidity and light level to the control unit through the wireless bridge. Acquired data is sent and stored in the cloud database by the control unit for real-time visualization and AI processing.

The wireless network is based on a star-mesh architecture in order to maximize the battery life of sensors (i.e., the autonomy is at least 6 months). Towards this end, an optimized firmware written in TinyOS environment has been implemented.

The wireless bridge adapter supports multiple communication standards (e.g., Bluetooth, Wi-Fi, NFC, and Sub-GHz SPIRIT1 868 MHz) for maximizing the compatibility with different kind of sensor networks and control units accordingly to the requirements of the scenario at hand.

Real-time Web Monitoring

The data acquired by the wireless sensor network is converted in standard units and stored in the central database that enables real-time visualization (and processing) through the user-friendly web interface specifically designed for EEB objectives.

Web interface for real-time monitoring of environmental parameters.



Web interface for real-time monitoring of each sensor node.

IoT Sensor Node

During the first phase of the project, different kinds of IoT sensors and wireless technologies have been considered and experimentally validated with respect to EEB objectives.

The selected sensor node is based on the Tmote Sky hardware framework. A dedicated firmware was implemented to enable the acquisition of many heterogeneous environmental parameters: air temperature, air humidity, air pressure, multi-axial accelerations, noise, illumination level, battery voltage, network link status.

Wireless Sensor Node Tmote Sky equipped with Temperature and Humidity sensors.

Temperature Monitoring

The wireless IoT node is equipped with a Sensirion SHT-11 sensor for acquiring the air temperature with good accuracy and reliability.

Level 1 - Temperature in the period: April 2016 – October 2017.


Level 2 - Temperature in the period: April 2016 – October 2017.

Humidity Monitoring

The wireless IoT node is equipped with a Sensirion SHT-11 sensor for acquiring the air relative humidity with good accuracy and reliability.

Level 2 - Humidity in the period: April 2016 – October 2017.


Level 2 - Humidity in the period: April 2016 – October 2017.

Diagnostic Information

The wireless devices provide diagnostic information such as the battery voltage and the network link status for enabling real-time anomaly detection of failures and predictive maintenance.

Monitored Battery Voltage decay during preliminary tests of the network.

Smart Lighting:
Metering and Control

through dedicated highly Autonomous Wireless Smart Sensors and Actuators for Monitoring and Controlling Energy Consumption and Production.

Smart Lighting System

The intelligent management of the lighting systems offers to the visitor an optimal optical performance, while avoiding energy waste (e.g., using natural lighting when available).
The system architecture includes four type of devices:

  • Dimmer Lights
  • Light Sensors
  • Power Meters
  • Control Unit

The multi-sensor devices are located nearby the art-works, they acquire not only the light level and acquire multiple parameters but also other environmental parameters.

The smart systems controls in real-time the dimming level of the lamps to maximize users experience while minimizing energy consumptions and the exposure of art-works. Toward this end, the system exploits the room occupancy and the localization of users.

In particular, the AI optimization engine considers the lighting level acquired in the all measure points (exploiting their spatial correlation) and the power consumption of each monitored lamp as well as the natural light from the windows. The output of the system is the dimming level of each lamp. Moreover, in order to limit the impacts on visitors, the system is designed to change the light level smoothly.

Sensing Architecture

The wireless monitoring system has been designed to be scalable, low-cost, reliable, and is characterized by a very low energy profile. Toward this end, Bluetooth-based wireless sensors such as the SimpleLink Sensor Tag can guarantee a very long battery duration (about one year). Moreover, measurements can also be acquired by a common smart-phone.

System architecture of the wireless lighting monitoring system using Bluetooth low energy profile.

IoT Wireless Sensor Node

The IoT device that have been selected for acquiring lighting level (as well as more environmental parameters) is the SimpleLink Sensor Tag. The device is equipped with a digital ALS lux meter and supports Bluetooth wireless communication (BLE).

Moreover, as the device is a very compact, it can be placed anywhere room and also very close to art-works.

SimpleLink Sensor Tag with Light sensor [Lux].

Artificial vs. Natural Light Detection

By processing all available information acquired in the room, the designed AI engine is able to recognize the type and the source of the light in the room. This information is latter exploited for the smart actuation and the optimization of user comfort and energy consumption.

Recognition of illumination type (artificial or natural).

Decision Support
and Smart Management

AI and Game Theory for the Analysis, the Prediction,
and the Optimization of Energy Resources and Energy Saving.

AI System for Smart Energy Management

The innovative management of energy production and consumption is a key feature on smart buildings and green urban districts. In particular, the main objectives are related to:

  • Energy Saving for green users and buildings;
  • Prefer Green Energy whenever is possible;
  • Urban and grid level Energy Peak Reduction;
  • Reduction of Costs for users and the environment.

Toward this end, different state-of-the-art technologies and strategies have been designed to motivate users to be part of the energy management process.

For example, the management of peaks in the energy demand is a critical problem for smart grid and building management.

This problem can be addressed by optimizing the schedule of the industry and users' load (e.g., washing machine): evaluation and balancing needs with respect to the availability and the source of energy resources as well as its cost.

Actuation Schedule

The proposed system has been experimentally validated in a small-case scenario having four users (loads) connected to power-meters devices. The power meter monitors the load consumption and controls the on/off status of the load.

The DSS system periodically estimates the optimal schedule of users loads (e.g., washing machine) for minimizing energy peaks (at grid level) and reducing costs (for each user). The user can also change the optimal schedule proposed by the system through the interactive web interface.

Temporal profile of power-meters status (on/off).

Loads Monitoring

The users' energy demand and loads usage history is exploited by the DSS to infer users' habits and reduce the impact of the proposed loads scheduling. The web interface allows to monitor and browse historical data.

Users' load monitoring and the effects of load activation / periodical re-planning.

Optimization Charts

The decision support system exploits Game Theory, Convex Programming and Evolutionary optimization techniques to plan the global and per-user optimal schedule. Accordingly to Game Theory paradigm, the final configuration satisfy goals both of the game (i.e., peak reduction for the energy grid / building) and for each user (i.e., few changes in the desired schedule, energy cost reduction).

In this framework, it is worth noticing that the activation of many common loads, such as of the washing machine, can be shifted (anticipated or delayed, e.g. during the night) with low impact and side-effects for the user.

The optimal schedule of energy allocation in a 10 users scenario.

Wireless-based Occupancy Estimation and User Localization

Opportunistic and Privacy-Aware Localization techniques as applied to infere users' behavior and to improve building efficiency.

Occupancy and Localization for Smart Buildings

Localization technologies provide relevant information for the smart management of the building. In particular, the two major features of interest are:

  • Privacy-Aware Localization of the users
  • Occupancy Estimation for each room

In fact, knowing the occupancy status of rooms and the exact location of users can be exploited for:

  • Building Security
  • Heating and Lighting Cost Reduction
  • Maximize Users' Comfort (learn and exploit habits)

Read more on ELEDIA Passive (device-free) and ELEDIA Active localization.

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PUBLICATIONS

  • F. Viani, A. Polo, P. Garofalo, N. Anselmi, M. Salucci, and E. Giarola, Evolutionary optimization applied to wireless smart lighting in energy-efficient museums, IEEE Sensors Journal, vol. 17, no. 5, pp. 1213-1214, March 2017.
  • F. Viani, M. D. Migliore, A. Polo, M. Salucci, and A. Massa, An iterative classification strategy for multi-resolution wireless sensing of passive targets, Electronics Letters, vol. 54, no. 2, pp. 101-103, January 2018.
  • A. Massa, G. Oliveri, M. Salucci, N. Anselmi, and P. Rocca, Learning-by-examples techniques as applied to electromagnetics, Journal of Electromagnetic Waves and Applications, Invited Review Article, vol. 32, no. 4, pp. 516-541, 2018.
  • F. Viani and A. Polo, A forecasting strategy based on wireless sensing for thermal comfort optimization in smart buildings, Microwave and Optical Technology Letters, vol. 59, no.11, pp. 2913-2917, November 2017.
  • H. Ahmadi, A. Polo, T. Moriyama, M. Salucci, and F. Viani, Semantic wireless localization of WiFi terminals in smart buildings, Radio Science – Special Issue on ‘Innovative Microwave Devices, Methods and Applications,’ Invited Paper, vol. 51, no. 6, pp. 876-892, June 2016.
  • F. Viani and M. Salucci, A user perspective optimization scheme for demand-side energy management, IEEE Systems Journal, vol. 12, no. 4, pp. 3857-3860, Dec. 2018.
  • H. Ahmadi, F. Viani, and R. Bouallegue, An accurate prediction method for moving target localization and tracking in wireless sensor networks, Ad Hoc Networks, vol. 70, no. 1, pp. 14-22, March 2018.
  • F. Robol, F. Viani, A. Polo, E. Giarola, P. Garofalo, C. Zambiasi, and A. Massa, Opportunistic crowd sensing in WiFi-enabled indoor areas, 2015 IEEE AP-S International Symposium and USNC-URSI Radio Science Meeting, Vancouver, BC, Canada, pp. 274-275, July 19-25, 2015.
  • F. Viani, E. Giarola, F. Robol, G. Oliveri, and A. Massa, Distributed monitoring for energy consumption optimization in smart buildings, 2014 IEEE Antenna Conference on Antenna Measurements and Applications (IEEE CAMA 2014), Antibes Juan-les-Pins, France, pp. 1-3, November 16-19, 2014.
  • F. Robol, F. Viani, E. Giarola, and A. Massa, Wireless sensors for distributed monitoring of energy-efficient smart buildings, 2015 IEEE Mediterranean Microwave Symposium (MMS’2015), Lecce, Italy, pp. 1-4, November 30 – December 2, 2015.
  • H. Ahmadi, M. S. Dao, E. Giarola, A. Polo, F. Robol, F. Viani, and A. Massa, Distributed wireless sensing, monitoring, and decision support: current activities @ ELEDIA Research Center, Atti XXI Riunione Nazionale di Elettromagnetismo (XXI RiNEm), Parma, 12-14 Settembre 2016.

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