This document discusses using an artificial neural network to forecast power loads by taking the University of Lagos as a sample space. It involves gathering and arranging historical load data, determining an appropriate network type and topology, training the network using an algorithm, and analyzing the results to test the network's accuracy in predicting loads. The methodology includes randomizing and tagging the training data, experimenting to determine the network topology, training with cross-validation, and performing sensitivity and mean squared error analysis on the network.

1. BYUDUEHI OMOYE I.10/29/20091LOAD FORECASTING USING ARTIFICIAL NEURAL NETWORKS

2. WHY LOAD FORECASTING?10/29/20092PROPER PLANNING OF POWER SYSTEMS IS A VERY IMPORTANT PART OF POWER SYSTEMS OPERATIONLOAD FORECASTS ARE AN IMPORTANT FACTOR FOR PLANNING IN THE LONG, SHORT AND MEDIUM TERMTHIS PROJECT AIMS TO SOLVE A LOAD FORECAST PROBLEM TAKING THE UNIVERSITY OF LAGOS AS THE SAMPLE SPACE.

3. HOW?10/29/20093BY USING A NEURAL NETWORKTHIS IS A NETWORK OF INTERCONNECTED MATHEMATICAL ELEMENTS THAT ARE USED TO SOLVE COMPLEX PROBLEMS

4. METHODOLOGY10/29/20094FIRST GATHERING AND ARRANGING THE DATATAGGING THE DATA INTO GROUPSDETERMINING THE NETWORK TYPE AND TOPOLOGYTRAINING THE NETWORK USING THE CHOSEN ALGORITHMANALYSES

5. IMPLEMENTATION10/29/20095GATHERING AND ARRANGING THE DATA IN MS EXCEL SPREADSHEETRANDOMIZATION AND TAGGING THE DATANETWORK TOPOLOGY DETERMINATION (MAINLY BY EXPERIMENTATION)TRAINING WITH CROSS VALIDATIONTESTING SENSITIVITY AND MSE ANALYSIS

6. MSE CROSS VALIDATION10/29/20096

7. FINAL NETWORK TOPOLOGY10/29/20097

8. RESULTS 10/29/20098TESTING DATA RESULTS

9. 10/29/20099THANK YOU!