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Energy efficiency in research labs: energy consumption metering based on a 6LoWPAN network architecture

Asun Santamaría y Sergio Ramos, Research Group on Energy Efficiency and Smart Buildings, CeDInt-UPM. Spain11/12/2012
This communication presents a system for continuous monitoring of energy consumption in a research laboratory, designed by CeDInt-UPM. The system offers real time data in order to set the proper strategy and actions to optimize energy spending. The CeDInt-UPM monitoring system consists of a management platform (called Bat-MP) and a wireless transducer network (named BatNet) which allows real time metering of the electric line. The BatNet is connected to the Bat-MP through one of its nodes which acts as a coordinator. The BatNet sends data from sensors (BatMeter) to the Bat-MP. The Bat-MP, in combination with a cloud server, provides access to the monitoring service from any remote location, following a cloud computing service approach.
Fig.1. Structure of the Wireless System for Energy Efficiency in Research Laboratories
Fig.1. Structure of the Wireless System for Energy Efficiency in Research Laboratories.

The main characteristics of the BatNet and the BatMeter are the following:

• 6LoWPAN-based network: 6LoWPAN (IPv6 over Low power Wireless Personal Area Networks) working group from the IETF describes the adaptation of IPv6 packets to IEEE 802.15.4 based networks. 6LoWPAN uses physical and data link layers based on IEEE 802.15.4 protocol (like other low power wireless communications as ZigBee or MiWi) and uses IPv6 (where a single IP address is assigned to each BatNet device) for the network layer [1].

• CoAP communication interfaces: CoAP (Constrained Application Protocol) (currently an IETF draft) is a specific web transfer protocol designed to be used with constrained nodes and networks. This protocol is simple and easy to implement [2].

• Contiki Operating System: the open source operating system from SICS enables multitasking and implements both IPv6 and IEEE 802.15.4 standards under lowcapacitance hardware requirements [3].

• Networks nodes: Each node of the BatNet comprises a processing and communications module (BatMote) plus a transducer module for power metering (BatMeter). The BatMeter measures continuously instantaneous voltage and current, while the BatMote calculates instantaneous power. In addition, every 20 electric signal periods (400ms) average voltage, current, active power, apparent power and power factor are calculated, and sent every 15 seconds to the BatMote module.

Fig. 2. BatMeter
Fig. 2. BatMeter.

The proposed system aims to solve current commercial system barriers in energy metering: high cost, not real voltage measurement and limited number of electric lines monitoring. Given its technical accuracy based on real time monitoring (not on estimations) and its easy-toimplement design, the system can offer a practical solution to optimize energy consumption in the context of research laboratories, as they have a great potential for optimization [4] (4-5 times higher than other type of premises).

Furthermore the system has been designed to provide additional measures of different magnitudes such as temperature, illumination, humidity or presence, or control any element in the research premises, like HVAC systems, lamps or blinds. For that purpose a family of new transducer modules has been designed by CeDInt-UPM (ambient sensors - BatSense), power switches - BatPlug/BatSwitch), light dimmers – BatDimm, blinds control - BatBlind).

[1] Lu Chia-Wen, Li Shu-Cheng and Q. Wu, “Interconnecting ZigBee and 6LoWPAN wireless sensor networks for smart grid applications, ” Proc. 2011 Fifth International Conference on Sensing Technology (ICST), 2011, pp. 267-272, doi:10.1109/ICSensT.2011.6136979.

[2] Constrained Application Protocol (CoAP) draft https://datatracker.ietf.org/doc/draft-ietf-core-coap/.

[3] Zigduino, http://www.logos-electro.com/zigduino.

[4] Paul Mathew et al. “Rating Energy Efficiency and Sustainability in Laboratories: Results and Lessons from the Labs21 Program”. Lawrence Berkeley National Laboratory, 2004.

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