Modelling Electricity Price Expectations in a Day-Ahead Market: A Case of Latvia

Abedinia, O., Amjady, N., Shafie-khah, M. & Catalão J. P. S. (2015). Electricity price forecast using Combinatorial Neural Network trained by a new stochastic search method. Energy Conversion and Management, 105, 642–654. http://dx.doi.org/10.1016/j.enconman.2015.08.025AbediniaO.AmjadyN.Shafie-khahM.CatalãoJ. P. S.2015Electricity price forecast using Combinatorial Neural Network trained by a new stochastic search methodEnergy Conversion and Management105642654http://dx.doi.org/10.1016/j.enconman.2015.08.025Search in Google Scholar

Aggarwal, S. K., Saini, L. M. & Kumar, A. (2009). Electricity price forecasting in deregulated markets: A review and evaluation. International Journal of Electrical Power & Energy Systems, 31(1), 13–22. http://dx.doi.org/10.1016/j.ijepes.2008.09.003AggarwalS. K.SainiL. M.KumarA.2009Electricity price forecasting in deregulated markets: A review and evaluationInternational Journal of Electrical Power & Energy Systems3111322http://dx.doi.org/10.1016/j.ijepes.2008.09.003Search in Google Scholar

Al-Shakhs, M. & El-Hawary, M. (2015). Innovations-based Neural Network Seasonal Day-ahead Marginal Price Forecasting. Electric Power Components and Systems, 43(5), 588–593. http://dx.doi.org/10.1080/15325008.2014.995279Al-ShakhsM.El-HawaryM.2015Innovations-based Neural Network Seasonal Day-ahead Marginal Price ForecastingElectric Power Components and Systems435588593http://dx.doi.org/10.1080/15325008.2014.995279Search in Google Scholar

Amjady, N. & Keynia, F. (2009). Day-ahead price forecasting of electricity markets by a new feature selection algorithm and cascaded neural network technique. Energy Conversion and Management, 50(12), 2976–2982. http://dx.doi.org/10.1016/j.enconman.2009.07.016AmjadyN.KeyniaF.2009Day-ahead price forecasting of electricity markets by a new feature selection algorithm and cascaded neural network techniqueEnergy Conversion and Management501229762982http://dx.doi.org/10.1016/j.enconman.2009.07.016Search in Google Scholar

Anbazhagan, S. & Kumarappan, N. (2014). Day-ahead deregulated electricity market price forecasting using neural network input featured by DCT. Energy Conversion and Management, 78, 711–719. http://dx.doi.org/10.1016/j.enconman.2013.11.031AnbazhaganS.KumarappanN.2014Day-ahead deregulated electricity market price forecasting using neural network input featured by DCTEnergy Conversion and Management78711719http://dx.doi.org/10.1016/j.enconman.2013.11.031Search in Google Scholar

Anderson, D. R., Sweeney, D. J., Williams, T., Camm, J. D. & Martin, K. (2009). Quantitative Methods for Business. South-Western Cengage Learning.AndersonD. R.SweeneyD. J.WilliamsT.CammJ. D.MartinK.2009Quantitative Methods for BusinessSouth-Western Cengage LearningSearch in Google Scholar

Armstrong, J. S. & Green, K. C. (2010). Demand Forecasting: Evidence-Based Methods. Retrieved August 5, 2011, from http://kestencgreen.com/demandfor.pdfArmstrongJ. S.GreenK. C.2010Demand Forecasting: Evidence-Based MethodsRetrieved August 5, 2011, from http://kestencgreen.com/demandfor.pdfSearch in Google Scholar

Bartkienė, A. (1993). Rinkos kainų politika ir kainodara. Valstybinis leidybos centras. Vilnius.BartkienėA.1993Rinkos kainų politika ir kainodaraValstybinis leidybos centrasVilniusSearch in Google Scholar

Bartosevičienė, V. (2001). Ekonominė statistika. Technologija. Kaunas.BartosevičienėV.2001Ekonominė statistikaTechnologijaKaunasSearch in Google Scholar

Binder, C. C. (2016). Estimation of historical inflation expectations. Explorations in Economic History, 61, 1–31. http://dx.doi.org/10.1016/j.eeh.2016.01.002BinderC. C.2016Estimation of historical inflation expectationsExplorations in Economic History61131http://dx.doi.org/10.1016/j.eeh.2016.01.002Search in Google Scholar

BN Vocabulary (2016). Expectations. Retrieved January 25, 2016, from http://zodynas.vz.lt/LukesciaiBN Vocabulary2016ExpectationsRetrieved January 25, 2016, from http://zodynas.vz.lt/LukesciaiSearch in Google Scholar

Bobinaitė, V. & Konstantinavičiūtė, I. (2012). Wholesale electricity price forecasting models: Lithuanian case. The 7th international conference on electrical and control technologies ECT-2012, Kaunas, Lithuania, May 3–4, 2012. Kaunas: Technologija, 2012. ISSN 1822-5934, p. 249–254.BobinaitėV.KonstantinavičiūtėI.2012Wholesale electricity price forecasting models: Lithuanian caseThe 7th international conference on electrical and control technologies ECT-2012Kaunas, LithuaniaMay 3–4, 2012KaunasTechnologija2012ISSN 1822-5934249254Search in Google Scholar

Bonacina, M. & Gulli, F. (2007). Electricity pricing under “carbon emissions trading”: A dominant firm with competitive fringe model. Energy Policy, 35(8), 4200–4220. http://dx.doi.org/10.1016/j.enpol.2007.02.016BonacinaM.GulliF.2007Electricity pricing under “carbon emissions trading”: A dominant firm with competitive fringe modelEnergy Policy35842004220http://dx.doi.org/10.1016/j.enpol.2007.02.016Search in Google Scholar

Catalão, J. P. S., Mariano, S. J. P. S., Mendes, V. M. F. & Ferreira, L. A. F. M. (2007). Short-term electricity prices forecasting in a competitive market: A neural network approach. Electric Power Systems Research, 77(10), 1297–1304. http://dx.doi.org/10.1016/j.epsr.2006.09.022CatalãoJ. P. S.MarianoS. J. P. S.MendesV. M. F.FerreiraL. A. F. M.2007Short-term electricity prices forecasting in a competitive market: A neural network approachElectric Power Systems Research771012971304http://dx.doi.org/10.1016/j.epsr.2006.09.022Search in Google Scholar

Chen, S. & Billings, S. A. (1992). Neural networks for nonlinear dynamic system modelling and identification. International Journal of Control, 56(2), 319–346. http://dx.doi.org/10.1080/00207179208934317ChenS.BillingsS. A.1992Neural networks for nonlinear dynamic system modelling and identificationInternational Journal of Control562319346http://dx.doi.org/10.1080/00207179208934317Search in Google Scholar

Chogumaira, E. N. & Hiyama, T. (2011). Short-Term Electricity Price Forecasting Using a Combination of Neural Networks and Fuzzy Inference. Energy and Power Engineering, 3, 9–16. http://dx.doi.org/10.4236/epe.2011.31002ChogumairaE. N.HiyamaT.2011Short-Term Electricity Price Forecasting Using a Combination of Neural Networks and Fuzzy InferenceEnergy and Power Engineering3916http://dx.doi.org/10.4236/epe.2011.31002Search in Google Scholar

Chow, G. C. (2011). Usefulness of Adaptive and Rational Expectations in Economics. CEPS Working paper 221. Retrieved January 20, 2016, from https://www.princeton.edu/~gchow/Usefulness%20of%20%20Adaptive%20and%20Rational%20Expectations%20in%20Economics%20(2).pdfChowG. C.2011Usefulness of Adaptive and Rational Expectations in EconomicsCEPS Working paper 221. Retrieved January 20, 2016, from https://www.princeton.edu/~gchow/Usefulness%20of%20%20Adaptive%20and%20Rational%20Expectations%20in%20Economics%20(2).pdfSearch in Google Scholar

Conejo, A. J., Javier Contreras, J., Espínola, R. & Plazas, M. A. (2005). Forecasting electricity prices for a day-ahead pool-based electric energy market. International Journal of Forecasting, 21(3), 435–462. http://dx.doi.org/10.1016/j.ijforecast.2004.12.005ConejoA. J.Javier ContrerasJ.EspínolaR.PlazasM. A.2005Forecasting electricity prices for a day-ahead pool-based electric energy marketInternational Journal of Forecasting213435462http://dx.doi.org/10.1016/j.ijforecast.2004.12.005Search in Google Scholar

Cuaresma, J. C., Hlouskova, J., Kossmeier, S. & Obersteiner M. (2004). Forecasting electricity spot-prices using linear univariate time-series models. Applied Energy, 77(1), 87–106. http://dx.doi.org/10.1016/S0306-2619(03)00096-5CuaresmaJ. C.HlouskovaJ.KossmeierS.ObersteinerM.2004Forecasting electricity spot-prices using linear univariate time-series modelsApplied Energy77187106http://dx.doi.org/10.1016/S0306-2619(03)00096-5Search in Google Scholar

de Menil, G. & Bhalla, S. (1973). Direct Measurement of Popular Price Expectations. Econometric Research Program, Research Memorandum No. 149. Retrieved February 5, 2016, from https://www.princeton.edu/~erp/ERParchives/archivepdfs/M149.pdfde MenilG.BhallaS.1973Direct Measurement of Popular Price ExpectationsEconometric Research Program, Research Memorandum No. 149. Retrieved February 5, 2016, from https://www.princeton.edu/~erp/ERParchives/archivepdfs/M149.pdfSearch in Google Scholar

Demery, D. & Duck, N. W. (2007). The theory of rational expectations and the interpretation of macroeconomic data. Journal of Macroeconomics, 29(1), 1–18. http://dx.doi.org/10.1016/j.jmacro.2005.02.005DemeryD.DuckN. W.2007The theory of rational expectations and the interpretation of macroeconomic dataJournal of Macroeconomics291118http://dx.doi.org/10.1016/j.jmacro.2005.02.005Search in Google Scholar

Dev, P. & Martin, M. A. (2014). Using neural networks and extreme value distributions to model electricity pool prices: Evidence from the Australian National Electricity Market 1998–2013. Energy Conversion and Management, 84, 122–132. http://dx.doi.org/10.1016/j.enconman.2014.04.012DevP.MartinM. A.2014Using neural networks and extreme value distributions to model electricity pool prices: Evidence from the Australian National Electricity Market 1998–2013Energy Conversion and Management84122132http://dx.doi.org/10.1016/j.enconman.2014.04.012Search in Google Scholar

EEX (2016). Market data of European Emission Allowances. Retrieved March 10, 2016, from https://www.eex.com/en/market-data/emission-allowances/spot-market/european-emission-allowances#!/2016/03/10EEX2016Market data of European Emission AllowancesRetrieved March 10, 2016, from https://www.eex.com/en/market-data/emission-allowances/spot-market/european-emission-allowances#!/2016/03/10Search in Google Scholar

Evans, G. W. & Ramey, G. (2006). Adaptive Expectations, Underparameterization and the Lucas Critique. Journal of Monetary Economics, 53(2), 249–264. http://dx.doi.org/10.1016/j.jmoneco.2004.12.002EvansG. W.RameyG.2006Adaptive Expectations, Underparameterization and the Lucas CritiqueJournal of Monetary Economics532249264http://dx.doi.org/10.1016/j.jmoneco.2004.12.002Search in Google Scholar

Ezekiel, M. (1938). The Cobweb Theorem. The Quarterly Journal of Economics, 52(2), 255–280. Retrieved February 5, 2016, from http://harvey.binghamton.edu/~polachek/econ_360/Ezekiel%20QJE%201938%20COBWEB.pdfEzekielM.1938The Cobweb TheoremThe Quarterly Journal of Economics522255280Retrieved February 5, 2016, from http://harvey.binghamton.edu/~polachek/econ_360/Ezekiel%20QJE%201938%20COBWEB.pdf10.2307/1881734Search in Google Scholar

Goodwin, R. M. (1947). Dynamical Coupling with Especial Reference to Markets Having Production Lags. Econometrica, 15(3), 181–204. http://dx.doi.org/10.2307/1905478GoodwinR. M.1947Dynamical Coupling with Especial Reference to Markets Having Production LagsEconometrica153181204http://dx.doi.org/10.2307/1905478Search in Google Scholar

Janeliūnas, T. & Kasčiūnas, L. (2007). Prognozavimo metodų taikymas politikos moksluose. Politologija. 3(47), 3–43.JaneliūnasT.KasčiūnasL.2007Prognozavimo metodų taikymas politikos moksluosePolitologija347343Search in Google Scholar

Johnson, M. D., Anderson, E. W. & Fornell, C. (1995). Rational and Adaptive Performance Expectations in a Customer Satisfaction Framework. Journal of Consumer Research, 21(4), 695–707. http://dx.doi.org/10.1086/209428JohnsonM. D.AndersonE. W.FornellC.1995Rational and Adaptive Performance Expectations in a Customer Satisfaction FrameworkJournal of Consumer Research214695707http://dx.doi.org/10.1086/209428Search in Google Scholar

Keles, D., Scelle, J., Paraschiv, F. & Fichtner, W. (2016). Extended forecast methods for day-ahead electricity spot prices applying artificial neural networks. Applied Energy, 162, 218–230. http://dx.doi.org/10.1016/j.apenergy.2015.09.087KelesD.ScelleJ.ParaschivF.FichtnerW.2016Extended forecast methods for day-ahead electricity spot prices applying artificial neural networksApplied Energy162218230http://dx.doi.org/10.1016/j.apenergy.2015.09.087Search in Google Scholar

Keynia, F. (2012). A new feature selection algorithm and composite neural network for electricity price forecasting. Engineering Applications of Artificial Intelligence, 25(8), 1687–1697. http://dx.doi.org/10.1016/j.engappai.2011.12.001KeyniaF.2012A new feature selection algorithm and composite neural network for electricity price forecastingEngineering Applications of Artificial Intelligence25816871697http://dx.doi.org/10.1016/j.engappai.2011.12.001Search in Google Scholar

Knoll, L. & Engels, A. (2012). Exploring the Linkages between Carbon markets and Sustainable Innovations in the Energy Sector: Lessons from the EU Emissions Trading Scheme. Sustainability Innovations in the Electricity Sector. Springer-Verlag Berlin Heidelberg. http://dx.doi.org/10.1007/978-3-7908-2730-9_6KnollL.EngelsA.2012Exploring the Linkages between Carbon markets and Sustainable Innovations in the Energy Sector: Lessons from the EU Emissions Trading SchemeSustainability Innovations in the Electricity SectorSpringer-VerlagBerlin Heidelberghttp://dx.doi.org/10.1007/978-3-7908-2730-9_6Search in Google Scholar

Konstantinaviciute, I. & Bobinaite, V. (2015). Comparative analysis of carbon dioxide emission factors for energy industries in European Union countries. Renewable and Sustainable Energy Reviews, 51, 603–612. http://dx.doi.org/10.1016/j.rser.2015.06.058KonstantinaviciuteI.BobinaiteV.2015Comparative analysis of carbon dioxide emission factors for energy industries in European Union countriesRenewable and Sustainable Energy Reviews51603612http://dx.doi.org/10.1016/j.rser.2015.06.058Search in Google Scholar

Lin, W.-M., Gow, H.-J. & Tsai, M.-T. (2010). Electricity price forecasting using Enhanced Probability Neural Network. Energy Conversion and Management, 51(12), 2707–2714 http://dx.doi.org/10.1016/j.enconman.2010.06.006LinW.-M.GowH.-J.TsaiM.-T.2010Electricity price forecasting using Enhanced Probability Neural NetworkEnergy Conversion and Management511227072714http://dx.doi.org/10.1016/j.enconman.2010.06.006Search in Google Scholar

Lise, W. & Kruseman, G. (2008). Long–term price and environmental effects in a liberalised electricity market. Energy Economics, 30(2), 230–248. http://dx.doi.org/10.1016/j.eneco.2006.06.005LiseW.KrusemanG.2008Long–term price and environmental effects in a liberalised electricity marketEnergy Economics302230248http://dx.doi.org/10.1016/j.eneco.2006.06.005Search in Google Scholar

Lithuanian District Heating Association (2013). Šilumos tiekimo bendrovių 2012 metų ūkinės veiklos apžvalga. Vilnius.Lithuanian District Heating Association2013Šilumos tiekimo bendrovių 2012 metų ūkinės veiklos apžvalgaVilniusSearch in Google Scholar

Lucas, R. E. (1976). Econometric Policy Evaluation: A Critique. Carnegie-Rochester Conference Series on Public Policy, 1, 19–46. http://dx.doi.org/10.1016/S0167-2231(76)80003-6LucasR. E.1976Econometric Policy Evaluation: A CritiqueCarnegie-Rochester Conference Series on Public Policy11946http://dx.doi.org/10.1016/S0167-2231(76)80003-6Search in Google Scholar

Mlambo, L. (2012). Adaptive and rational expectations hypotheses: reviewing the critiques. Journal of Economic Behavior, 2, 3–15. http://ijeb.faa.ro/download/459_MLAMBO.pdfMlamboL.2012Adaptive and rational expectations hypotheses: reviewing the critiquesJournal of Economic Behavior2315http://ijeb.faa.ro/download/459_MLAMBO.pdfSearch in Google Scholar

Moreno, M. J. J., Palmer, P. A. & Muñoz Gracia, P. (2011). Artificial neural networks applied to forecasting time series. Psicothema, 23(2), 322–329. http://www.psicothema.com/pdf/3889.pdfMorenoM. J. J.PalmerP. A.Muñoz GraciaP.2011Artificial neural networks applied to forecasting time seriesPsicothema232322329http://www.psicothema.com/pdf/3889.pdfSearch in Google Scholar

Muller, B., Reinhardt, J. & Strickland, M. T. (1995). Neural Networks: An Introduction. 2nd updated and corrected edition. Springer-Verlag Berlin Heidelberg. http://dx.doi.org/10.1007/978-3-642-57760-4MullerB.ReinhardtJ.StricklandM. T.1995Neural Networks: An Introduction2nd updated and corrected editionSpringer-VerlagBerlin Heidelberghttp://dx.doi.org/10.1007/978-3-642-57760-4Search in Google Scholar

Muth, J. F. (1961). Rational Expectations and the Theory of Price Movements. Econometrica, 29(3), 315–335. http://dx.doi.org/10.2307/1909635MuthJ. F.1961Rational Expectations and the Theory of Price MovementsEconometrica293315335http://dx.doi.org/10.2307/1909635Search in Google Scholar

Nerlove, M. (1958). Adaptive Expectations and Cobweb Phenomena. Journal of Economics, 73, 227–240. http://dx.doi.org/10.2307/1880597NerloveM.1958Adaptive Expectations and Cobweb PhenomenaJournal of Economics73227240http://dx.doi.org/10.2307/1880597Search in Google Scholar

Nord Pool Spot (2016). Historical market data. Retrieved January 15 to March 16, 2016, from http://www.nordpoolspot.com/historical-market-data/Nord Pool Spot2016Historical market dataRetrieved January 15 to March 16, 2016, from http://www.nordpoolspot.com/historical-market-data/Search in Google Scholar

Panapakidis, I. P. & Dagoumas, A. S. (2016). Day-ahead electricity price forecasting via the application of artificial neural network based models. Applied Energy, 172, 132–151. http://dx.doi.org/10.1016/j.apenergy.2016.03.089PanapakidisI. P.DagoumasA. S.2016Day-ahead electricity price forecasting via the application of artificial neural network based modelsApplied Energy172132151http://dx.doi.org/10.1016/j.apenergy.2016.03.089Search in Google Scholar

Panchal, G., Ganatra, A., Kosta, Y. P. & Panchal, D. (2011). Behaviour Analysis of Multilayer Perceptrons with Multiple Hidden Neurons and Hidden Layers. International Journal of Computer Theory and Engineering, 3(2), 332–337. http://dx.doi.org/10.7763/IJCTE.2011.V3.328PanchalG.GanatraA.KostaY. P.PanchalD.2011Behaviour Analysis of Multilayer Perceptrons with Multiple Hidden Neurons and Hidden LayersInternational Journal of Computer Theory and Engineering32332337http://dx.doi.org/10.7763/IJCTE.2011.V3.328Search in Google Scholar

Pearce, D. K. (1975). The Measurement of Price Expectations. Journal of Behavioral Economics, 4(1), 145–165. http://dx.doi.org/10.1016/0090-5720(75)90030-3PearceD. K.1975The Measurement of Price ExpectationsJournal of Behavioral Economics41145165http://dx.doi.org/10.1016/0090-5720(75)90030-3Search in Google Scholar

Sargent, T. J. (2008). Rational Expectations. The Concise Encyclopedia of Economics, 2nd ed. Retrieved February 6, 2016, from http://www.econlib.org/library/Enc/RationalExpectations.htmlSargentT. J.2008Rational ExpectationsThe Concise Encyclopedia of Economics2nd ed.Retrieved February 6, 2016, from http://www.econlib.org/library/Enc/RationalExpectations.html10.1057/978-1-349-95121-5_1684-2Search in Google Scholar

Schlueter, S. (2010). A long–term/short–term model for daily electricity prices with dynamic volatility. Energy Economics, 32(5), 1074–1081. http://dx.doi.org/10.1016/j.eneco.2010.06.008SchlueterS.2010A long–term/short–term model for daily electricity prices with dynamic volatilityEnergy Economics32510741081http://dx.doi.org/10.1016/j.eneco.2010.06.008Search in Google Scholar

Schwarz, H. G. & Lang, Ch. (2006). The rise in German wholesale electricity prices: fundamental factors, exercise of market power, or both? IWE Working Paper No. 2. Institute of Economics.SchwarzH. G.LangCh.2006The rise in German wholesale electricity prices: fundamental factors, exercise of market power, or both?IWE Working Paper No. 2.Institute of EconomicsSearch in Google Scholar

Shahram Fattahi Gakieh, M. A. (2008). Modeling Inflation Expectations: The Case of Iran. Retrieved February 10, 2016, from http://sundoc.bibliothek.uni-halle.de/diss-online/08/08H147/t1.pdfShahram Fattahi GakiehM. A.2008Modeling Inflation Expectations: The Case of IranRetrieved February 10, 2016, from http://sundoc.bibliothek.uni-halle.de/diss-online/08/08H147/t1.pdfSearch in Google Scholar

Shepherd, B. (2012). When are adaptive expectations rational? A generalization. Economics Letters, 115(1), 4–6. http://dx.doi.org/10.1016/j.econlet.2011.11.017ShepherdB.2012When are adaptive expectations rational? A generalizationEconomics Letters115146http://dx.doi.org/10.1016/j.econlet.2011.11.017Search in Google Scholar

Singhal, D. & Swarup, K. S. (2011). Electricity price forecasting using artificial neural networks. International Journal of Electrical Power & Energy Systems, 33(3), 550–555. http://dx.doi.org/10.1016/j.ijepes.2010.12.009SinghalD.SwarupK. S.2011Electricity price forecasting using artificial neural networksInternational Journal of Electrical Power & Energy Systems333550555http://dx.doi.org/10.1016/j.ijepes.2010.12.009Search in Google Scholar

Skribans, V. & Balodis, M. (2016). Development of the Latvian energy sector competitiveness system dynamic model. 9th International Scientific Conference “Business and Management 2016”, Vilnius, Lithuania, May 12–13, 2016. Vilnius Gediminas Technical University, 2016. http://dx.doi.org/10.3846/bm.2016.12SkribansV.BalodisM.2016Development of the Latvian energy sector competitiveness system dynamic model9th International Scientific Conference “Business and Management 2016”Vilnius, LithuaniaMay 12–13, 2016Vilnius Gediminas Technical University2016http://dx.doi.org/10.3846/bm.2016.12Search in Google Scholar

Tan, Z., Zhang, J., Wang, J. & Xu, J. (2010). Day–ahead electricity price forecasting using wavelet transform combined with ARIMA and GARCH models. Applied Energy, 87(11), 3606–3610. http://dx.doi.org/10.1016/j.apenergy.2010.05.012TanZ.ZhangJ.WangJ.XuJ.2010Day–ahead electricity price forecasting using wavelet transform combined with ARIMA and GARCH modelsApplied Energy871136063610http://dx.doi.org/10.1016/j.apenergy.2010.05.012Search in Google Scholar

Tanaka, M. (2009). Transmission–constrained oligopoly in the Japanese electricity market. Energy Economics, 31(5), 690–701. http://dx.doi.org/10.1016/j.eneco.2009.03.004TanakaM.2009Transmission–constrained oligopoly in the Japanese electricity marketEnergy Economics315690701http://dx.doi.org/10.1016/j.eneco.2009.03.004Search in Google Scholar

Thiemer, A. (2007). Cobweb Models and Expectations. Retrieved February 10, 2016, from https://www.fh-kiel.de/fileadmin/data/wirtschaft/Dozenten/Thiemer__Andreas/MCD/cobweb_e.pdfThiemerA.2007Cobweb Models and ExpectationsRetrieved February 10, 2016, from https://www.fh-kiel.de/fileadmin/data/wirtschaft/Dozenten/Thiemer__Andreas/MCD/cobweb_e.pdfSearch in Google Scholar

Vahidinasab, V., Jadid, S. & Kazemi, A. (2008). Day-ahead price forecasting in restructured power systems using artificial neural networks. Electric Power Systems Research, 78(8), 1332–1342. http://dx.doi.org/10.1016/j.epsr.2007.12.001VahidinasabV.JadidS.KazemiA.2008Day-ahead price forecasting in restructured power systems using artificial neural networksElectric Power Systems Research78813321342http://dx.doi.org/10.1016/j.epsr.2007.12.001Search in Google Scholar

Weron, R. & Misiorek, A. (2008). Forecasting Spot Electricity Prices: A Comparison of Parametric and Semiparametric Time Series Models. International Journal of Forecasting, 24, 744–763. http://dx.doi.org/10.1016/j.ijforecast.2008.08.004WeronR.MisiorekA.2008Forecasting Spot Electricity Prices: A Comparison of Parametric and Semiparametric Time Series ModelsInternational Journal of Forecasting24744763http://dx.doi.org/10.1016/j.ijforecast.2008.08.004Search in Google Scholar

Weron, R. (2014). Electricity price forecasting: A review of the state-of-the-art with a look into the future. International Journal of Forecasting, 30(4), 1030–1081. http://dx.doi.org/10.1016/j.ijforecast.2014.08.008WeronR.2014Electricity price forecasting: A review of the state-of-the-art with a look into the futureInternational Journal of Forecasting30410301081http://dx.doi.org/10.1016/j.ijforecast.2014.08.008Search in Google Scholar