Harold D. de Mello Junior 
, Karla Figueiredo , Marcos C. R. Seruffo ,Fernando A. R. Costa , Flávio R. Trindade Moura , Fausto M. Rodrigues Junior & Guilherme Baptista Bastos
Abstract: The essential difference between the Free Contracting Environment (FCE) and the Regulated Contracting Environment (RCE) is the possibility of freely negotiating energy terms and prices with suppliers. Disconnected from the tariffs regulated by the government, in the FCE, consumers bear the costly difference between the contracted energy and that consumed. This cost can be reduced with accurate knowledge of the consumer profile, based on the analysis of historical data. In this article, a methodology is proposed to evaluate the migration of consumers to the FCE. In a case study, graphical statistical techniques help identify the profile of a consumer in the city of Rio de Janeiro, subgroup A4 and with green tariff modality, in the period from 2016 to 2019. Then, classical and artificial neural network-based methods are used for consumption forecasting twelve months ahead. In particular, Long and Short Term Memories (LSTM) networks performed better than Autoregressive Integrated Moving Average (ARIMA) models. At the end, it is demonstrated with economic and financial indicators, the right decision of this consumer to migrate to the FCE, prior to the analysis performed in this case study.
Keywords: Free energy market, time series forecasting, artificial neural networks, LSTM.
DOI code: 10.21528/lnlm-vol21-no1-art6
PDF file: vol21-no1-art6.pdf
BibTex file: vol21-no1-art6.bib