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Algorithms for appliance usage prediction

Algorithms for appliance usage prediction
Algorithms for appliance usage prediction
Demand-Side Management (DSM) is one of the key elements of future Smart Electricity Grids. DSM involves mechanisms to reduce or shift the consumption of electricity in an attempt to minimise peaks. By so doing it is possible to avoid using expensive peaking plants that are also highly carbon emitting. A key challenge in DSM, however, is the need to predict energy usage from specific home appliances accurately so that consumers can be notified to shift or reduce the use of high energy-consuming appliances. In some cases, such notifications may be also need to be given at very short notice. Hence, to solve the appliance usage prediction problem, in this thesis we develop novel algorithms that take into account both users' daily practices (by taking advantage of the cyclic nature of routine activities) and the inter-dependency between the usage of multiple appliances (i.e., the user's typical consumption patterns). We propose two prediction algorithms to satisfy the needs for fast prediction and high accuracy respectively: i) a rule-based approach, EGH-H, for scenarios in which notifications need to be given at short notice, to find significant patterns in the use of appliances that can capture the user's behaviour (or habits), ii) a graphical{model based approach, GM-PMA (Graphical Model for Prediction in Multiple Appliances) for scenarios that require high prediction accuracy. We demonstrate through extensive empirical evaluations on real{world data from a prominent database of home energy usage that GM-PMA outperforms existing methods by up to 41%, and the runtime of EGH-H is 100 times lower on average, than that of other benchmark algorithms, while maintaining competitive prediction accuracy. Moreover, we demonstrate the use of appliance usage prediction algorithms in the context of demand{side management by proposing an Intelligent Demand Responses (IDR) mechanism, where an agent uses Logistic Inference to learn the user's preferences, and hence provides the best personalised suggestions to the user. We use simulations to evaluate IDR on a number of user types, and show that, by using IDR, users are likely to improve their savings significantly.
Truong, Ngoc Cuong
fcc6a730-6c2d-4a88-8634-6dc79adbb678
Truong, Ngoc Cuong
fcc6a730-6c2d-4a88-8634-6dc79adbb678
Ramchurn, Sarvapali
1d62ae2a-a498-444e-912d-a6082d3aaea3

Truong, Ngoc Cuong (2014) Algorithms for appliance usage prediction. University of Southampton, Faculty of Physical Sciences and Engineering, Doctoral Thesis, 102pp.

Record type: Thesis (Doctoral)

Abstract

Demand-Side Management (DSM) is one of the key elements of future Smart Electricity Grids. DSM involves mechanisms to reduce or shift the consumption of electricity in an attempt to minimise peaks. By so doing it is possible to avoid using expensive peaking plants that are also highly carbon emitting. A key challenge in DSM, however, is the need to predict energy usage from specific home appliances accurately so that consumers can be notified to shift or reduce the use of high energy-consuming appliances. In some cases, such notifications may be also need to be given at very short notice. Hence, to solve the appliance usage prediction problem, in this thesis we develop novel algorithms that take into account both users' daily practices (by taking advantage of the cyclic nature of routine activities) and the inter-dependency between the usage of multiple appliances (i.e., the user's typical consumption patterns). We propose two prediction algorithms to satisfy the needs for fast prediction and high accuracy respectively: i) a rule-based approach, EGH-H, for scenarios in which notifications need to be given at short notice, to find significant patterns in the use of appliances that can capture the user's behaviour (or habits), ii) a graphical{model based approach, GM-PMA (Graphical Model for Prediction in Multiple Appliances) for scenarios that require high prediction accuracy. We demonstrate through extensive empirical evaluations on real{world data from a prominent database of home energy usage that GM-PMA outperforms existing methods by up to 41%, and the runtime of EGH-H is 100 times lower on average, than that of other benchmark algorithms, while maintaining competitive prediction accuracy. Moreover, we demonstrate the use of appliance usage prediction algorithms in the context of demand{side management by proposing an Intelligent Demand Responses (IDR) mechanism, where an agent uses Logistic Inference to learn the user's preferences, and hence provides the best personalised suggestions to the user. We use simulations to evaluate IDR on a number of user types, and show that, by using IDR, users are likely to improve their savings significantly.

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More information

Published date: June 2014
Organisations: University of Southampton, Agents, Interactions & Complexity

Identifiers

Local EPrints ID: 367540
URI: http://eprints.soton.ac.uk/id/eprint/367540
PURE UUID: d8578b1e-4512-4042-a55e-7fd145513caa
ORCID for Sarvapali Ramchurn: ORCID iD orcid.org/0000-0001-9686-4302

Catalogue record

Date deposited: 23 Oct 2014 15:39
Last modified: 06 Jun 2018 12:42

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Contributors

Author: Ngoc Cuong Truong
Thesis advisor: Sarvapali Ramchurn ORCID iD

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