| Summary: | Eskom, South Africa national electricity provider, estimates that in the year 2006
South Africa will experience a capacity shortage. One way to address this
problem is through the implementation of Demand-Side Management (DSM) in
various sectors, ranging from the commercial to the industrial. For Eskom to
succeed in their vision of implementing DSM, a tool has to be developed that can
illustrate the long-term impact of various DSM options for a region. This tool could
then be used to illustrate the various role players the advantages of implementing
DSM.
The purpose of this study was to test if Neural Networks (NN's) can be applied in
the construction of an hourly baseline for a region. This baseline could then be
used to illustrate the Long-term impact of various DSM options.
In this study a technique was developed using hourly data to construct a baseline
model for the calculation of the long-term impact of DSM. This technique was then
tested and evaluated on a case study.
To achieve this goal, an investigation was launched to determine which inputs
have an influence on the energy use of a region. The different variables that
influence the neural network topology were also investigated.
This information was then used to develop a technique that models the energy use
of the area. To determine accuracy of the simulated energy use, a verification
procedure was developed based on an internationally accepted verification model,
using data the NN did not "learn" on. Sufficient accurate results were obtained
using the defined indices. Thus NN can be used to model the energy use of a
region.
The major disadvantage of this technique is that hourly data for the whole year
was used to train the model on. The question arises into just how much
information is needed to model the NN. Subsequently an investigation was
launched to determine the minimum data set needed to model the energy use.
It was found that a full factorial data set is the minimum set of data that a NN
needs to train on. In choosing this data, a study has to be conducted on previous
data to determine exactly when the best combination could be obtained. The
results indicated that the data could not be minimised due to the configuration of
input data.
For this study, the months of the year were encoded. This aided the NN in
learning the relationship between the various inputs and the energy use. It was
found that this is a crucial step in aiding the NN. Thus the NN could not simulate
accurate enough results without the encoded data. This results that the number of
months cannot be minimised with the current technique used.
The model then can be used to evaluate different DSM options by subtracting the
hourly differences from the baseline. This information is then used to evaluate the
options using various indices. The indices included monthly energy use,
maximum demand, the energy use during the various time of use periods and the
impact of greenhouse gasses. The concept was illustrated by an actual case
study.
The use of NN for modelling the baseline for the forecasting of the long-term
impact of DSM is considerable faster than current techniques with a timesaving
element of up to 90 %.
The use of NN is thus a viable technique to model the baseline. The results
indicate that NN can successfully be used for cases with a high diversity in the
load, and with little or no knowledge of the underlying systems. === Thesis (M.Ing. (Mechanical Engineering))--North-West University, Potchefstroom Campus, 2004.
|
|---|
