Now showing 1 - 3 of 3
  • Publication
    Generating power footprints without appliance interaction : an enabler for privacy intrusion
    Appliance load monitoring (ALM) systems are systems capable of monitoring appliances’ operation within a building using a single metering point. As such, they uncover information on occupants’ activities of daily living and subsequently an exploitable privacy leak. Related work has shown monitoring accuracies higher than 90% ̇ achieved by ALM systems, yet requiring interaction with appliances for system calibration. In the context of external privacy intrusion, ALM systems have the following obstacles for system calibration: (1) type and model of appliances inside the monitored building are entirely unknown; (2) appliances cannot be operated to record power footprints; and (3) ground truth data is not available to fine- tune algorithms. Within this work, we focus on monitoring those appliances from which we can infer occupants’ activities. Without appliance interaction, appliances’ profiling is realised via automated capture and analysis of shapes, steady-state durations, and occurrence patterns of power loads. Such automated process produces unique power footprints, and naming is realised using heuristics and known characteristics of typical home equipment. Data recorded within a kitchen area and one home illustrates the various processing steps, from data acquisition to power footprint naming.
      628Scopus© Citations 2
  • Publication
    Real-time recognition and profiling of appliances through a single electricity sensor
    Sensing, monitoring and actuating systems are expected to play a key role in reducing buildings overall energy consumption. Leveraging sensor systems to support energy efficiency in buildings poses novel research challenges in monitoring space usage, controlling devices, interfacing with smart energy meters and communicating with the energy grid. In the attempt of reducing electricity consumption in buildings, identifying individual sources of energy consumption is key to generate energy awareness and improve efficiency of available energy resources usage. Previous work studied several non-intrusive load monitoring techniques to classify appliances; however, the literature lacks of an comprehensive system that can be easily installed in existing buildings to empower users profiling, benchmarking and recognizing loads in real-time. This has been a major reason holding back the practice adoption of load monitoring techniques. In this paper we present RECAP: RECognition of electrical Appliances and Profiling in real-time. RECAP uses a single wireless energy monitoring sensor easily clipped to the main electrical unit. The energy monitoring unit transmits energy data wirelessly to a local machine for data processing and storage. The RECAP system consists of three parts: (1) Guiding the user for profiling electrical appliances within premises and generating a database of unique appliance signatures; (2) Using those signatures to train an artificial neural network that is then employed to recognize appliance activities (3) Providing a Load descriptor to allow peer appliance benchmarking. RECAP addresses the need of an integrated and intuitive tool to empower building owners with energy awareness. Enabling real-time appliance recognition is a stepping-stone towards reducing energy consumption and allowing a number of major applications including load-shifting techniques, energy expenditure breakdown per appliance, detection of power hungry and faulty appliances, and recognition of occupant activity. This paper describes the system design and performance evaluation in domestic environment.
    Scopus© Citations 223  2962
  • Publication
    NetBem : business equipment energy monitoring through network auditing
    Modern office buildings are fully equipped and furnished spaces with arrangements including networked business equipment, such as PC-class machines, copiers, wireless routers and fax machines, and other electrical equipment such as home appliances e.g. coffee machines, and appliances for environmental comfort e.g. electric heaters. The unique characteristics of networked business equipment are well-defined usage pattern, low-power current draw, and connectivity to the local area network (LAN). Business equipment is generally used over working hours adding up to important costs, motivating the need for a system capable of tracking equipment usage and associated energy expenditure, as well as identifying cost saving opportunities. Techniques for monitoring power loads are generally based on power step edge detection, and cannot be applied to business equipment due to the low power consumption of individual devices. This paper presents NetBem, a novel energy monitoring technique ad hoc to office buildings, capturing the contribution of networked business equipment to a power load via side-band detection of the equipment’s operating state through the LAN. The technique is presented, and results from experiments within the School of Computer Science and Informatics at University College Dublin in Ireland are given.
    Scopus© Citations 8  624