Tin Petrovic

Bio: Tin Petrovic is an academic researcher from University of Tokyo. The author has contributed to research in topics: Router & Electric power. The author has an hindex of 3, co-authored 3 publications receiving 23 citations.

Active sensing approach to electrical load classification by smart plug

Abstract: Classification of electrical loads presents multiple benefits. From the ability to inform customer how their appliances contribute to the overall electricity cost, to allowing electric companies and virtual aggregators to institute demand response mechanisms. In this paper we propose a new type of active sensing method which breaks down real-time data into fixed segments which can be quickly analyzed and used to classify electrical loads to help reduce peak power demand. We have designed and implemented a simple smart plug with a bidirectional triode thyristor, gathered data from household appliances and performed classification. Our results show high accuracy with significantly shorter times than other similar proposed methods.

Detecting Presence From a WiFi Router’s Electric Power Consumption by Machine Learning

Abstract: Presence and occupancy detection in residential and office environments is used to predict movement of people, detect intruders, and manage electric power consumption. Specifically, we are developing methods to improve demand side electrical power management by reducing electrical power waste in unoccupied spaces. In this paper, we conduct an extensive analysis on the applicability of using a WiFi router’s electrical power consumption in different types of environments to determinate the number or people present in a space. We show the importance of a moving average filter for electrical load time series data, confirm the correlation between control packets and increased minimal router power consumption, and present our results on the accuracy of our approach. We conclude that a WiFi router’s power consumption can improve presence detection in home environments and occupancy estimation in office environments, and where possible, should be analysed separately from the aggregated power consumption.

Determining occupancy from a WiFi router's electric power consumption in an office environment

Abstract: The goal of most modern energy management systems in buildings is to reduce unnecessary waste and cut peak electric demand. Underpinning this need is the ability to detect the number of people occupying a given space. This allows for both the ability to shut down all nonessential devices contributing to the electric waste while no one is present, as well as optimise the amount of electricity used in heating, ventilation and air conditioning, depending on the number of people present. In this paper we demonstrate that it is possible to determine the number of people in a room by monitoring the electric power consumption of a WiFi router. We show that the WiFi router's minimum power consuption can be used as features to determine the number of connected devices and link this to the types of data packets used by the network. Finally, we also perform measurements in an office environment and show that it is possible to classify the number of people with high accuracy.

[ACM Press the 2nd ACM Workshop - Zurich, Switzerland (2010.11.02-2010.11.02)] Proceedings of the 2nd ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Building - BuildSys \'10 - Occupancy-driven energy management for smart building automation

Abstract: Buildings are among the largest consumers of electricity in the US. A significant portion of this energy use in buildings can be attributed to HVAC systems used to maintain comfort for occupants. In most cases these building HVAC systems run on fixed schedules and do not employ any fine grained control based on detailed occupancy information. In this paper we present the design and implementation of a presence sensor platform that can be used for accurate occupancy detection at the level of individual offices. Our presence sensor is low-cost, wireless, and incrementally deployable within existing buildings. Using a pilot deployment of our system across ten offices over a two week period we identify significant opportunities for energy savings due to periods of vacancy. Our energy measurements show that our presence node has an estimated battery lifetime of over five years, while detecting occupancy accurately. Furthermore, using a building simulation framework and the occupancy information from our testbed, we show potential energy savings from 10% to 15% using our system.

Technological innovations to assess and include the human dimension in the building-performance loop: A review

Abstract: The human dimension plays an essential role in the energy performance of buildings and is considered as significant as technological advances. Several studies highlighted the negative influence of occupant behaviour in underperforming buildings, while some support that technological innovations may reduce human-related uncertainties. Thus, one may consider that fully automated smart buildings are essential to achieve energy efficiency. However, if technology excludes people from decision-making processes, low acceptance and comfort/welfare levels may be reported from users. Therefore, the right combination of humans and technologies are expected to solve these problems. Buildings are emerging as complex Cyber-Physical Systems, including the Social dimension, and this provides an excellent opportunity to achieve high-performance outcomes, considering both technical and social aspects. Thus, the right choice among available up-to-date behavioural sensing – comprising active and passive sensors, as well as Kinect technology – are important in the Internet-of-Things (IoT) era. IoT-driven buildings can use real-time monitoring data to inform users and drive behavioural-based consumption change, which is an important aspect to achieve high-performance buildings and deliver user-centred services. An essential feature in this regard is to allow for human-in-the-loop approaches enabled by human-centric computing and smart devices, which has grown fast in the last few years. This literature review summarises applications and main challenges related to the combination of the human dimension and technological innovations in the building sector. This combination is expected to increase user welfare and reduce the energy consumption in buildings, as human and machine components of intelligence may complement each other regarding building performance.

An Intelligent Smart Plug with Shared Knowledge Capabilities

Abstract: The massive dissemination of smart devices in current markets provides innovative technologies that can be used in energy management systems. Particularly, smart plugs enable efficient remote monitoring and control capabilities of electrical resources at a low cost. However, smart plugs, besides their enabling capabilities, are not able to acquire and communicate information regarding the resource’s context. This paper proposes the EnAPlug, a new environmental awareness smart plug with knowledge capabilities concerning the context of where and how users utilize a controllable resource. This paper will focus on the abilities to learn and to share knowledge between different EnAPlugs. The EnAPlug is tested in two different case studies where user habits and consumption profiles are learned. A case study for distributed resource optimization is also shown, where a central heater is optimized according to the shared knowledge of five EnAPlugs.

A Reinforcement Learning-Based QAM/PSK Symbol Synchronizer

Abstract: Machine Learning (ML) based on supervised and unsupervised learning models has been recently applied in the telecommunication field. However, such techniques rely on application-specific large datasets and the performance deteriorates if the statistics of the inference data changes over time. Reinforcement Learning (RL) is a solution to these issues because it is able to adapt its behavior to the changing statistics of the input data. In this work, we propose the design of an RL Agent able to learn the behavior of a Timing Recovery Loop (TRL) through the Q-Learning algorithm. The Agent is compatible with popular PSK and QAM formats. We validated the RL synchronizer by comparing it to the Mueller and Muller TRL in terms of Modulation Error Ratio (MER) in a noisy channel scenario. The results show a good trade-off in terms of MER performance. The RL based synchronizer loses less than 1 dB of MER with respect to the conventional one but it is able to adapt its behavior to different modulation formats without the need of any tuning for the system parameters.

Honeypot Type Selection Games for Smart Grid Networks

Abstract: In this paper, we define a cyber deception game between the Advanced Metering Infrastructure (AMI) network administrator (henceforth, defender) and attacker. The defender decides to install between a low-interaction honeypot, high-interaction honeypot, and a real system with no honeypot. The attacker decides on whether or not to attack the system given her belief about the type of device she is facing. We model this interaction as a Bayesian game with complete but imperfect information. The choice of honeypot type is private information and characterizes the essence and objective of the defender i.e., the degree of deception and amount of threat intelligence. We study the players’ equilibrium strategies and provide numerical illustrations. The work presented in this paper has been motivated by the H2020 SPEAR project which investigates the implementation of honeypots in smart grid infrastructures to: (i) contribute towards creating attack data sets for training a SIEM (Security Information and Event Management) and (ii) to support post-incident forensics analysis by having recorded a collection of evidence regarding an attacker’s actions.