A Data Mining Approach To Support The
Development Of Long-term Load Forecasting
Author(s)
M. R. Maia, K. de Oliveira Gonçalves Veloso, M. T. Okamoto,
A. dos Santos Rigueira, G. M. Tavares, Â. M. Cister,
M. A. F. Zarur, F. T. de Souza, G. S. Terra, A. G. Evsukoff
& N. F. F. Ebecken
Abstract
Load forecasting is an important subject for power distribution systems and has
been studied comparing different points of view. In general, load forecasts
should be performed over a broad spectrum of time intervals, which could be
classified into short-term, medium-term and long-term forecasts. Several
research groups have proposed various techniques for either short-term load
forecasting or medium-term load forecasting or long-term load forecasting.
This paper presents two approaches for modelling the long-term load
forecasting: a neural network (NN) and a non-linear (cause/effect) model. The
data used by the models are gross domestic product (GDP), the national
minimum salary, the electrical energy price, the estimated national population
and the total number of electrical connections.
The suitability of the proposed approach is illustrated through a long-term
load forecasting application (electricity consumption in Brazil ten years ahead).
Keywords: neural networks; long-term; load forecasting; power distribution
systems.
1 Introduction
Brazil’s electric power system (EPS) consists of two major interconnected
systems (SIN) and many small isolated systems. The energy is mainly generated
Keywords
neural networks; long-term; load forecasting; power distribution
systems.
Related Book
Other papers in this volume
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