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Development of models for short-term load forecasting using artificial neural networks
Author(s)
Amakali, Simaneka
Date Issued
2008
Type
Thesis
Publisher
Cape Peninsula University of Technology
Abstract
Optimal daily operation of electric power generating plants is very essential for any power utility
organization to reduce input costs and possibly the prices of electricity in general. For a fossil fuel –
fired power plant for example, the benefits of power generation optimalization (i.e. generate what is
reasonably required) extends even to environmental issues such as the subsequent reduction in air
pollution. Now to generate “what is reasonably required” one needs forecast the future electricity
demands. Because power generation relies heavily on the electricity demand, the consumers are also
practically speaking required to wisely manage their loads to consolidate the power utility’s optimal
power generation efforts. Thus, for both cases, accurate and reliable electric load forecasting systems
are absolutely required.
To date, there are numerous forecasting methods developed primarily for electric load forecasting.
Some of these forecasting techniques are conventional and often less favoured.
To get a broad picture of the problem at hand, a literature survey was first conducted to identify
possible drawbacks of the existing forecasting techniques including the conventional one. Artificial
neural networks (ANNs) approach for short-term load forecasting (STLF) has been recently proposed
by a majority of researchers. But there still is a need to find optimal neural network structures or
convenient training approach that would possibly improve the forecasting accuracy.
This thesis developed models for STLF using ANNs approach. The evolved models are intended to
be a basis for real forecasting application. These models are tested using actual load data of the Cape
Peninsula University of Technology (CPUT) Bellville campus reticulation network and weather data
to predict the load of the campus for one week in advance.
The models were divided into two classes: first, forecasting the load for a whole week at once was
evaluated, and then hourly models were studied. In both cases, the inclusion of weather data was
considered. The test results showed that the hour-by-hour approach is more suitable and efficient for
a forecasting application. The work suggests that incremental training approach of a neural network
model should be implemented for on-line testing application to acquire a universal final view on its
applicability.
Keywords – power system operations, load forecasting, artificial neural networks, training mode,
accuracy
organization to reduce input costs and possibly the prices of electricity in general. For a fossil fuel –
fired power plant for example, the benefits of power generation optimalization (i.e. generate what is
reasonably required) extends even to environmental issues such as the subsequent reduction in air
pollution. Now to generate “what is reasonably required” one needs forecast the future electricity
demands. Because power generation relies heavily on the electricity demand, the consumers are also
practically speaking required to wisely manage their loads to consolidate the power utility’s optimal
power generation efforts. Thus, for both cases, accurate and reliable electric load forecasting systems
are absolutely required.
To date, there are numerous forecasting methods developed primarily for electric load forecasting.
Some of these forecasting techniques are conventional and often less favoured.
To get a broad picture of the problem at hand, a literature survey was first conducted to identify
possible drawbacks of the existing forecasting techniques including the conventional one. Artificial
neural networks (ANNs) approach for short-term load forecasting (STLF) has been recently proposed
by a majority of researchers. But there still is a need to find optimal neural network structures or
convenient training approach that would possibly improve the forecasting accuracy.
This thesis developed models for STLF using ANNs approach. The evolved models are intended to
be a basis for real forecasting application. These models are tested using actual load data of the Cape
Peninsula University of Technology (CPUT) Bellville campus reticulation network and weather data
to predict the load of the campus for one week in advance.
The models were divided into two classes: first, forecasting the load for a whole week at once was
evaluated, and then hourly models were studied. In both cases, the inclusion of weather data was
considered. The test results showed that the hour-by-hour approach is more suitable and efficient for
a forecasting application. The work suggests that incremental training approach of a neural network
model should be implemented for on-line testing application to acquire a universal final view on its
applicability.
Keywords – power system operations, load forecasting, artificial neural networks, training mode,
accuracy
Additional information
Thesis (MTech (Electrical Engineering))--Cape Peninsula University of Technology, 2008
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