Forecasting the Electricity Consumption by Applying Stochastic Modelling Techniques: The Case of Greece

Giovanis N. Apostolos and Christos H. Skiadas
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Giovanis N. Apostolos: Technical University of Crete, Dept. of Production Engineering & Management
Christos H. Skiadas: Technical University of Crete, Dept. of Production Engineering & Management

A chapter in Advances in Stochastic Modelling and Data Analysis, 1995, pp 85-100 from Springer

Abstract: Abstract In this paper we present a method for the solution of multiplicative autonomous SDE’s with general binomial drift coefficient. This method is based on the reduction of the proposed nonlinear stochastic differential equation to a linear SDE by using an appropriate transformation. Furthermore we find the solution of the logistic stochastic model which is a special case of the general nonlinear stochastic model as well as the first moment of the solution. Then the parameter estimators of the model are derived by using a method which provides the M.L.E’s of the parameters using time discrete data. The model is applied to the data concerning the electricity consumption in Greece. Using an easy simulation we are able to produce a predicting value-interval of the process. It is well known that the logistic model express growth patterns of technological, biological, social or marketing systems.

Keywords: Reducible Stochastic Differential Equations; Ito Calculus; Growth Models; Stochastic Logistic Model; Maximum Likelihood Estimators; Forecasting. (search for similar items in EconPapers)
Date: 1995
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-94-017-0663-6_5

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DOI: 10.1007/978-94-017-0663-6_5

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