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Open Access research with a European policy impact...

The Strathprints institutional repository is a digital archive of University of Strathclyde's Open Access research outputs. Strathprints provides access to thousands of Open Access research papers by Strathclyde researchers, including by researchers from the European Policies Research Centre (EPRC).

EPRC is a leading institute in Europe for comparative research on public policy, with a particular focus on regional development policies. Spanning 30 European countries, EPRC research programmes have a strong emphasis on applied research and knowledge exchange, including the provision of policy advice to EU institutions and national and sub-national government authorities throughout Europe.

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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. (2015) Incorporating practice theory in sub-profile models for short term aggregated residential load forecasting. IEEE Transactions on Smart Grid. ISSN 1949-3053

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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.