scholarly journals On the Impact of Side Information on Smart Meter Privacy-Preserving Methods

Author(s):  
Mohammadhadi Shateri ◽  
Francisco Messina ◽  
Pablo Piantanida ◽  
Fabrice Labeau
2020 ◽  
Vol 28 (4) ◽  
pp. 21-37
Author(s):  
Roya Gholami ◽  
Ali Emrouznejad ◽  
Yazan Alnsour ◽  
Hasan B. Kartal ◽  
Julija Veselova

The continuous development of energy management systems, coupled with a growing population, and increasing energy consumption, highlights the necessity to develop a deep understanding of household energy consumption behavior and interventions that facilitate behavioral change. Using a data mining segmentation technique, 2,505 Northern Ireland households were segmented into four distinctive profiles, based on their energy consumption patterns, socio-demographic, and dwelling characteristics. The change in attitude towards energy consumption behavior was analyzed to evaluate the impact of smart meter feedback as well. The key finding was 81% of trial participants perceived smart meters to be helpful in reducing their energy consumption. In addition, we found that the potential to reduce energy bills and environmental concerns were the strongest motivations for behavior change.


Electronics ◽  
2020 ◽  
Vol 9 (11) ◽  
pp. 1754
Author(s):  
Depeng Chen ◽  
Carlos Borrego ◽  
Guillermo Navarro-Arribas

This paper focuses on the problem of providing anonymous communications in opportunistic networks. To that end, we propose an approach using Mix networks that enables a relatively simple solution. Opportunistic networks present some constraints that make the deployment of typical network anonymity solutions difficult or infeasible. We show, utilizing simulations on the basis of real mobility traces, that the proposed solution is feasible for some scenarios by introducing a tolerable penalty in terms of message delay and delivery. To investigate the impact of routing strategies, we offer two different methods to select Mix nodes. From the experiment results, we show the trade-off between network performance and security.


Author(s):  
Roya Gholami ◽  
Ali Emrouznejad ◽  
Yazan Alnsour ◽  
Hasan B. Kartal ◽  
Julija Veselova

The continuous development of energy management systems, coupled with a growing population, and increasing energy consumption, highlights the necessity to develop a deep understanding of household energy consumption behavior and interventions that facilitate behavioral change. Using a data mining segmentation technique, 2,505 Northern Ireland households were segmented into four distinctive profiles, based on their energy consumption patterns, socio-demographic, and dwelling characteristics. The change in attitude towards energy consumption behavior was analyzed to evaluate the impact of smart meter feedback as well. The key finding was 81% of trial participants perceived smart meters to be helpful in reducing their energy consumption. In addition, we found that the potential to reduce energy bills and environmental concerns were the strongest motivations for behavior change.


Energies ◽  
2020 ◽  
Vol 13 (20) ◽  
pp. 5393
Author(s):  
Philippe Voinov ◽  
Patrick Huber ◽  
Alberto Calatroni ◽  
Andreas Rumsch ◽  
Andrew Paice

Grid-connected photovoltaic (PV) capacity is increasing and is currently estimated to account for 3.0% of worldwide energy generation. One strategy to balance fluctuating PV power is to incentivize self-consumption by shifting certain loads. The potential improvement in the amount of self-consumption is usually estimated using smart meter and PV production data. Smart meter data are usually available only at sampling frequences far below the Nyquist limit. In this paper we investigate how this insufficient sampling rate affects the estimated self-consumption potential of shiftable household appliances (washing machines, tumble dryers and dishwashers). We base our analyses on measured consumption data from 16 households in the UK and corresponding PV data. We found that the simulated results have a marked dependence on the data sampling rate. The amount of self-consumed energy estimated with data sampled every 10 min was overestimated by 30–40% compared to estimations using data with 1 min sampling rate. We therefore recommend to take this factor into account when making predictions on the impact of appliance load shifting on the rate of self-consumption.


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