AUTHOR: JAYME MILANEZI JUNIOR DIRECTOR: JOO PAULO C. LUSTOSA DA
COSTA CO-DIRECTOR: JOS LUS CERDA ARIAS ARRAY & ADAPTATIVE
SIGNAL PROCESSING AASP Comparison Between ANN and ARMAX as Load
Forecasting Methods Based on the Electrical Consumption Data of
Leipzig
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ADAPTATIVE & ARRAY SIGNAL PROCESSING AASP SUMMARY 1
Description of the work and data 2 ARMAX Model application and
results 3 Artificial Neural Networks application and results 4
Comparison between results from both methods
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Temporary Load Forecasting Initial classification and choice of
representatives Load forecasting model of representatives
Reconstruction of consumption Determination of the distribution
index Spatial Load Forecasting Spatial Distribution on the area
Module Objetives Proposed Methodology or Model Fuzzy K-means
Stepwise multiple regression Wavelets Theory & Armax Model
Extraplolation of the load forecasting results as a function of the
membership function of each measure station Relation between the
load forecasting and behaviour of each kind of consumption
Distribution of the load on the area of each kind of consumption as
a function of a Statistic Function Actually, we will compare
Auto-Regressive Moving Average with an eXogenous element (ARMAX)
and Artificial Neural Networks 1 Description of the work and
data
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- Clusters Representatives: centres of groups that will be
represented by these points - From Power Demand: minimum, mean and
maximum values per Substation
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- Matlab silhouette command - Fuzzy C-Mean Soft Cluster
(boundaries are not so hard) 4 clusters - Clusters Representatives:
centers of groups or bundles that will be represented by these
points
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- Clusters: centers of groups or bundles that will represent
them - Fuzzy C-Mean Soft Cluster (boundaries are not so hard)
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Output Data: maximum of demanded electrical power in the next
month (or in the future months) Period: september 2000 december
2002 Input: Three values were considered in each month: - Maximum
Electrical Power demanded to the Substation in a month - Gross
Domestic Product (GDP) variation into the month - Energy Intensity
(EI) depends on climatic conditions 1 Description of the work and
data
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M-n n months ago Two main periods: from M-4 to M-1 and from
M-11 to M-8 (since nk = 4) Inputs Considered M-4 to M-1M-11 to M-8
Maximum Electrical Power ANN, ARMAX GDP ARMAX EI ARMAX 24 input
values ANN 16 input values
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ARMAX
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n a = n b = n c = n k = 4 gives us... ARMAX Cluster 1 only This
set of values allowed to obtain the smallest FPE
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Armax projections of results... Power maximum values Gross
Domestic Product (GDP) Energy Intensity (EI)
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Artificial Neural Networks (ANN) Maximum power per month (past)
GDP (M-11 to M-8) EI (M-11 to M-8) Maximum future power demand
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4 months, 16 Substations 64 matches When one method achieved an
absolute smaller error, it was considered the winner in that
month/substation. Armax ANN
Aknowledgements Professor D. Ing. Joo Paulo C. Lustosa da
Costa, for helping me to find the research line and exorting me to
implement this comparison; Professor D. Ing. Jos Luiz Cerda-Arias,
for dedicating time to show me the clustering principles that were
the guide for the organization of the data into this work, as well
as making available the Leipzig data; Antonio R. Serrano, for
showing me how to work with a multi-layer ANN within the Matlab
environment.