On the nonlinear behaviour of population dynamics in electric demand forecasting

The problem of electric demand forecasting is analyzed for behavioral traits in the hope that an underlying dynamical process may be revealed. It is assumed that since electric demand is closely related to population growth, the iterative chaotic quadratic difference equation should yield a deeper l...

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Main Author: Hook, Chris
Language:English
Published: University of British Columbia 2010
Online Access:http://hdl.handle.net/2429/27888
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spelling ndltd-UBC-oai-circle.library.ubc.ca-2429-278882018-01-05T17:44:24Z On the nonlinear behaviour of population dynamics in electric demand forecasting Hook, Chris The problem of electric demand forecasting is analyzed for behavioral traits in the hope that an underlying dynamical process may be revealed. It is assumed that since electric demand is closely related to population growth, the iterative chaotic quadratic difference equation should yield a deeper level of insight toward the understanding of such a process. In particular, the topic of mathematical dynamical systems is developed and then applied, first to problems of constrained population growth, and then to the pertinent issue of per capita electric power demand. A new, randomized quadratic difference model is developed whose behavior appears both predictable and unexpected. Of primary significance, it was found that the hypothesized underlying dynamical system was sensitive to both the level or rate of population growth and the mean level of a randomly distributed (where a gaussian distribution was assumed) per capita electric demand. This was concluded with suggestions on relevant analytical models to be used for forecasting under differing parametric situations. Applied Science, Faculty of Civil Engineering, Department of Graduate 2010-08-28T17:18:50Z 2010-08-28T17:18:50Z 1988 Text Thesis/Dissertation http://hdl.handle.net/2429/27888 eng For non-commercial purposes only, such as research, private study and education. Additional conditions apply, see Terms of Use https://open.library.ubc.ca/terms_of_use. University of British Columbia
collection NDLTD
language English
sources NDLTD
description The problem of electric demand forecasting is analyzed for behavioral traits in the hope that an underlying dynamical process may be revealed. It is assumed that since electric demand is closely related to population growth, the iterative chaotic quadratic difference equation should yield a deeper level of insight toward the understanding of such a process. In particular, the topic of mathematical dynamical systems is developed and then applied, first to problems of constrained population growth, and then to the pertinent issue of per capita electric power demand. A new, randomized quadratic difference model is developed whose behavior appears both predictable and unexpected. Of primary significance, it was found that the hypothesized underlying dynamical system was sensitive to both the level or rate of population growth and the mean level of a randomly distributed (where a gaussian distribution was assumed) per capita electric demand. This was concluded with suggestions on relevant analytical models to be used for forecasting under differing parametric situations. === Applied Science, Faculty of === Civil Engineering, Department of === Graduate
author Hook, Chris
spellingShingle Hook, Chris
On the nonlinear behaviour of population dynamics in electric demand forecasting
author_facet Hook, Chris
author_sort Hook, Chris
title On the nonlinear behaviour of population dynamics in electric demand forecasting
title_short On the nonlinear behaviour of population dynamics in electric demand forecasting
title_full On the nonlinear behaviour of population dynamics in electric demand forecasting
title_fullStr On the nonlinear behaviour of population dynamics in electric demand forecasting
title_full_unstemmed On the nonlinear behaviour of population dynamics in electric demand forecasting
title_sort on the nonlinear behaviour of population dynamics in electric demand forecasting
publisher University of British Columbia
publishDate 2010
url http://hdl.handle.net/2429/27888
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