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K. Figueiredo¹, M.M.B.R. Vellasco² and J.C.A. Mattoso³

¹Department of Applied Mathematics and Computational Science, UEZO, Rio de Janeiro, Brazil
²Department of Electrical Engineering, Pontifical Catholic of Rio de Janeiro (PUC-Rio), Brazil
³Light S.A. Company, Rio de Janeiro, Brazil

doi:10.4203/ccp.103.32

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K. Figueiredo, M.M.B.R. Vellasco, J.C.A. Mattoso, "Load and Energy Billing Forecasting of an Electricity Utility", in Y. Tsompanakis, (Editor), "Proceedings of the Third International Conference on Soft Computing Technology in Civil, Structural and Environmental Engineering", Civil-Comp Press, Stirlingshire, UK, Paper 32, 2013. doi:10.4203/ccp.103.32

Keywords: prediction of billing and load, neural networks, climate and economic.

Summary

This study presents a methodology for predicting electrical energy load and billing that considers climate data and economic-financial factors. Using the clustering method, the concession area of a distributing company was divided into 17 microclimates according to the climate characteristics of districts (of the city of Rio de Janeiro) and municipalities (of the state of Rio de Janeiro). From the microclimates, the billing and load series were separated by consumption class (residential, commercial, public power, public service and rural). Models based on multilayer perceptron (MLP) multistep neural networks and linear regression were developed for the monthly prediction with a maximum of 15 steps forward (multistep). Adequate performance was achieved for the majority of the consumption and load classes.

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Front Page Browse CCP CSETS CTR IJRT Other Authors Search Purchase Guide FAQ Contact us	Civil-Comp Proceedings ISSN 1759-3433 CCP: 103 PROCEEDINGS OF THE THIRD INTERNATIONAL CONFERENCE ON SOFT COMPUTING TECHNOLOGY IN CIVIL, STRUCTURAL AND ENVIRONMENTAL ENGINEERING Edited by: Y. Tsompanakis Paper 32 Load and Energy Billing Forecasting of an Electricity Utility K. Figueiredo¹, M.M.B.R. Vellasco² and J.C.A. Mattoso³ ¹Department of Applied Mathematics and Computational Science, UEZO, Rio de Janeiro, Brazil ²Department of Electrical Engineering, Pontifical Catholic of Rio de Janeiro (PUC-Rio), Brazil ³Light S.A. Company, Rio de Janeiro, Brazil doi:10.4203/ccp.103.32 purchase the full-text of this paper Full Bibliographic Reference for this paper K. Figueiredo, M.M.B.R. Vellasco, J.C.A. Mattoso, "Load and Energy Billing Forecasting of an Electricity Utility", in Y. Tsompanakis, (Editor), "Proceedings of the Third International Conference on Soft Computing Technology in Civil, Structural and Environmental Engineering", Civil-Comp Press, Stirlingshire, UK, Paper 32, 2013. doi:10.4203/ccp.103.32 Keywords: prediction of billing and load, neural networks, climate and economic. Summary This study presents a methodology for predicting electrical energy load and billing that considers climate data and economic-financial factors. Using the clustering method, the concession area of a distributing company was divided into 17 microclimates according to the climate characteristics of districts (of the city of Rio de Janeiro) and municipalities (of the state of Rio de Janeiro). From the microclimates, the billing and load series were separated by consumption class (residential, commercial, public power, public service and rural). Models based on multilayer perceptron (MLP) multistep neural networks and linear regression were developed for the monthly prediction with a maximum of 15 steps forward (multistep). Adequate performance was achieved for the majority of the consumption and load classes. purchase the full-text of this paper (price £20) go to the previous paper go to the next paper return to the table of contents return to the book description purchase this book (price £42 +P&P)
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