Electric power load in Brazil: view on the long-term forecasting models
Larissa Resende; Murilo Soares; Pedro Ferreira
Abstract
Keywords
References
Andrade, T. A., & Lobão, W. J. (1997).
Arsenault, E., Bernard, J.-T., Carr, C., & Genest-Laplante, E. (1995). A total energy demand model of Québec: forecasting properties.
Barakat, E., & Al-Qasem, J. (1998). Methodology for weekly load forecasting.
Bentzen, J., & Engsted, T. (1993). Short-and long-run elasticities in energy demand: a cointegration approach.
Bernstein, M. A., & Griffin, J. M. (2006).
Chern, W. S., & Just, R. E. (1982). Assessing the need for power: a regional econometric model.
Dudek, G. (2016). Pattern-based local linear regression models for short-term load forecasting.
Ediger, V., & Akar, S. (2007). ARIMA forecasting of primary energy demand by fuel in turkey.
Engle, R. F., & Granger, C. W. (1987). Co-integration and error correction: representation, estimation, and testing.
Empresa de Pesquisa Energética – EPE. (2015).
Fei, S., Wang, M.-J., Miao, Y., Tu, J., & Liu, C. (2009). Particle swarm optimization-based support vector machine for forecasting dissolved gases content in power transformer oil.
Ferreira, P. G. C., Oliveira, F. L. C., & Souza, R. C. (2015). The stochastic effects on the Brazilian electrical sector.
Irffi, G., Castelar, I., Siqueira, M. L., & Linhares, F. C. (2009). Previsão da demanda por energia elétrica para classes de consumo na região nordeste, usando OLS dinâmico e mudança de regime.
Kucukali, S., & Cokgor, S. (2007). Fuzzy logic model to predict hydraulic jump aeration efficiency.
Lobão, W., & Ferreira, P. G. C. (2015).
Maceira, M. E. P., Marzano, L., Penna, D. D. J., Diniz, A., & Justino, T. (2015). Application of CVAR risk aversion approach in the expansion and operation planning and for setting the spot price in the Brazilian hydrothermal interconnected system.
Mattos, L. B., & Lima, J. E. (2005). Demanda residencial de energia elétrica em Minas Gerais: 1970-2002.
Operador Nacional do Sistema Elétrico – ONS. (2015).
Pai, P.-F., & Lin, C.-S. (2005). A hybrid ARIMA and support vector machines model in stock price forecasting.
Philpott, A., & Matos, V. (2012). Dynamic sampling algorithms for multistage stochastic programs with risk aversion.
Ranaweera, D., Hubele, N., & Karady, G. (1996). Fuzzy logic for short term load forecasting.
Rowland, C. S., Mjelde, J. W., & Dharmasena, S. (2017). Policy implications of considering pre-commitments in us aggregate energy demand system.
Schmidt, C. A. J., & Lima, M. A. (2004). A demanda por energia elétrica no Brasil.
Shapiro, A. (2011). Analysis of stochastic dual dynamic programming method.
Shapiro, A., Tekaya, W., Costa, J. P., & Soares, M. P. (2013a). Risk neutral and risk averse Stochastic Dual Dynamic Programming method.
Shapiro, A., Tekaya, W., Soares, M. P., & Costa, J. P. (2013b). Worst-case expectation approach to optimization under uncertainty.
Siqueira, M. L., Cordeiro, J. R. H., & Castelar, I. (2006). A demanda por energia elétrica no nordeste brasileiro após o racionamento de 2001-2002: previsões de longo prazo.
Torrini, F. C., Souza, R. C., Oliveira, F. L. C., & Pessanha, J. F. M. (2016). Long-term electricity consumption forecast in Brazil: A fuzzy logic approach.
Viana, G., & Silva, A. L. M. (2014). Um modelo para projeções para demanda por energia elétrica, 2009-2017 para o setor residencial no Brasil.
Westley, G. D. (1984). Electricity demand in a developing country.
Westley, G. D. (1989). Commercial electricity demand in a Central American economy.
Zhang, G., Patuwo, B. E., & Hu, M. Y. (1998). Forecasting with artificial neural networks: the state of the art.
Zjavka, L., & Snášel, V. (2016). Short-term power load forecasting with ordinary differential equation substitutions of polynomial networks.
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