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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

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Methodall errors MEANall errors VARIANCE ARMAX0,23%2,0816 ANN-5,15%2,3707

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3 Artificial Neural Networks Winner: ARMAX

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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.