The Prediction Of Electric Energy Consumption
Using An Artificial Neural Network
Author(s)
I. Chernykh, D. Chechushkov & T. Panikovskaya
Abstract
This paper presents the results of the studies on forecasting the electrical loads
for a megapolis district with the use of artificial neural networks (ANN) as one
of the most accomplished and promising solutions to this challenge. A
theoretical approach to the issue is combined with the results of experimental
studies using real schedules.
Keywords: artificial neural network, electrical loads, updated input data.
1 Introduction
The transition to the wholesale electricity market [1] has exacerbated the
problems associated with the accuracy of forecasting electricity demand by
tightening the requirements for compiling speed and the reliability of forecasts.
New conditions associated with the introduction of heavy penalties require
the introduction of modern electricity consumers of software and hardware for
the collection of information on power consumption, for making accurate
forecasts using flexible models and for reacting appropriately to changing trends.
The relevance of research in this area is shown in the application of the results
to the operational and tactical management of power consumption that can
reduce production costs and, as a consequence, increase the company's
competitiveness by reducing production costs. Thus for the entity acquiring
electricity on the wholesale and/or retail market, there is the task of compiling
reliable applications for electricity consumption for a certain period ahead. The
relevance of this research is supported by the peculiarities of distributed
Energy Production and Management in the 21st Century, Vol. 1 109
www.witpress.com, ISSN 1743-3541 (on-line)
WIT Transactions on Ecology and The Environment, Vol 190, © 2014 WIT Press
doi:10.2495/EQ140121
Keywords
artificial neural network, electrical loads, updated input data
Related Book
Other papers in this volume
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