Incorporating practice theory in sub-profile models for short term aggregated residential load forecasting

Stephen, Bruce and Tang, Xiaoqing and Harvey, Poppy R. and Galloway, Stuart and Jennett, Kyle I. (2017) Incorporating practice theory in sub-profile models for short term aggregated residential load forecasting. IEEE Transactions on Smart Grid, 8 (4). pp. 1591-1598. ISSN 1949-3053 (https://doi.org/10.1109/TSG.2015.2493205)

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Abstract

Aspirations of grid independence could be achieved by residential power systems connected only to small highly variable loads, if overall demand on the network can be accurately anticipated. Absence of the diversity found on networks with larger load cohorts or consistent industrial customers, makes such overall load profiles difficult to anticipate on even a short term basis. Here, existing forecasting techniques are employed alongside enhanced classification/clustering models in proposed methods for forecasting demand in a bottom up manner. A Markov Chain based sampling technique derived from Practice Theory of human behavior is proposed as a means of providing a forecast with low computational effort and reduced historical data requirements. The modeling approach proposed does not require seasonal adjustments or environmental data. Forecast and actual demand for a cohort of residential loads over a 5 month period are used to evaluate a number of models as well as demonstrate a significant performance improvement if utilized in an ensemble forecast.

  • Item type:Article
    ID code:54676
    Dates:
    Date
    Event
    1 July 2017
    Published
    9 November 2015
    Published Online
    17 October 2015
    Accepted
    Notes:(c) 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
    Subjects:Technology > Electrical engineering. Electronics Nuclear engineering
    Department:Faculty of Engineering > Electronic and Electrical Engineering
    Depositing user: Pure Administrator
    Date deposited:11 Dec 2015 01:11
    Last modified:18 Apr 2024 00:17
    URI:https://strathprints.strath.ac.uk/id/eprint/54676
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