Modelling prices in competitive electricity markets
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Notice bibliographique
Résumé
List of Contributors.Preface.1 Structural and Behavioural Foundations of Competitive Electricity Prices (Derek W. Bunn).PART I: PRICES AND STRATEGIC COMPETITION.2 Competitors' Response Representation for Market Simulation in the Spanish Daily Market (Efraim Centeno Hernaez, Julian Barquin Gil, Jose Ignacio de la Fuente Leon, Antonio Munoz San Roque, Mariano J. Ventosa Rodriguez, Javier Garcia Gonzalez, Alicia Mateo Gonzalez, and Agustin Martin Calmarza).2.1 Introduction.2.2 Hourly bidding-based Spanish electricity markets.2.3 A two-phase clustering procedure for the analysis of bid functions.2.4 Forecasting methods for residual demand functions using time series (ARIMA) models.2.5 Discovering electricity market states for forecasting the residual demand function using input output hidden Markov models.2.6 Conjectural variations approach for modelling electricity markets.2.7 Conclusions.Appendix: Nomenclature.References.3 Complementarity-Based Equilibrium Modeling for Electric Power Markets (Benjamin F. Hobbs and Udi Helman).3.1 Introduction.3.2 Definitions.3.3 A general complementarity-based model of energy commodity markets.3.4 A comparison of two approaches to modeling Cournot generators on a transmission network.3.5 A large-scale application: the North American Eastern Interconnection.3.6 Conclusion.Acknowledgments.References.4 Price Impact of Horizontal Mergers in the British Generation Market (John Bower).4.1 Introduction.4.2 England and Wales wholesale electricity market.4.3 Analysis.4.4 Price forecast.General references.Ofgem references.PART II: SPOT MARKET DYNAMICS.5 Testing for Weekly Seasonal Unit Roots in the Spanish Power Pool (Angel Le-on and Antonio Rubia).5.1 Introduction.5.2 Data.5.3 Testing for seasonal unit roots.5.4 Concluding remarks.Appendix A: Prewhitening procedure.Appendix B: Critical values of the HEGY test.Acknowledgements.References.6 Nonlinear Time Series Analysis of Alberta's Deregulated Electricity Market (Apostolos Serletis and Ioannis Andreadis).6.1 Introduction.6.2 A noise model.6.3 A multifractal formalism setting.6.4 On turbulent behavior.6.5 On nonlinearity.6.6 On chaos.6.7 Conclusion.Acknowledgments.References.7 Quantile-Based Probabilistic Models for Electricity Prices (Shi-Jie Deng and Wenjiang Jiang).7.1 Introduction.7.2 Quantile-based distributions and the modelling of marginal distributions of electricity price.7.3 Quantile-GARCH models and the modelling of time series of electricity price.7.4 Parameter Inference.7.5 Conclusion.Acknowledgements.References.8 Forecasting Time-Varying Covariance Matrices in the Intradaily Spot Market of Argentina (Angel Leon and Antonio Rubia).8.1 Introduction.8.2 VAR analysis for block bids.8.3 Modelling the conditional covariance matrix.8.4 Forecasting conditional covariance matrices.8.5 Concluding remarks.Acknowledgements.References.PART III: SPATIAL PRICE INTERACTIONS.9 Identifying Dynamic Interactions in Western US Spot Markets (Christine A. Jerko, James W. Mjelde and David A. Bessler).9.1 Introduction.9.2 Data.9.3 Methods.9.4 Results.9.5 Discussion.References.10 Transmission of Prices and Volatility in the Australian Electricity Spot Markets (Andrew C. Worthington and Helen Higgs).10.1 Introduction.10.2 Data and summary statistics.10.3 Multivariate GARCH model.10.4 Empirical results.10.5 Conclusion.References.PART IV: FORWARD PRICES.11 Forecasting Higher Moments of the Power Price Using Medium-Term Equilibrium Economics and the Value of Security of Supply (Chris Harris).11.1 Introduction.11.2 Construction of the moments of price.11.3 Worked example.11.4 Commentary.11.5 Conclusions.References.12 Modeling Electricity Forward Curve Dynamics in the Nordic Market (Nicolas Audet, Pirja Heiskanen, Jussi Keppo and Iivo Vehvilainen).12.1 Introduction.12.2 The model.12.3 Forward model in the Nordic market.12.4 Model usage examples.12.5 Conclusion.Appendix: Estimation of model parameters.Acknowledgments.References.13 The Forward Curve Dynamic and Market Transition Forecasts (Svetlana Borovkova).13.1 The term structure of commodity futures prices.13.2 Forecasting market transitions.13.3 Critical regions and bootstrap methods.13.4 Application to electricity and oil futures.13.5 Concluding remarks.References.PART V: FORECASTING AND RISK MANAGEMENT.14 Price Modelling for Profit at Risk Management (Jacob Lemming).14.1 Introduction.14.2 Electricity price modelling.14.3 A profit at risk risk management model.14.4 Modelling input parameters.14.5 Experimental results.14.6 Conclusions.References.15 ForecastingWeather Variable Densities for Weather Derivatives and Electricity Prices (James W. Taylor).15.1 Introduction.15.2 Weather ensemble predictions.15.3 Univariate time series modelling of weather variables.15.4 Empirical comparison of weather point forecasts.15.5 Empirical comparison of weather quantile forecasts.15.6 Summary of the analysis of temperature, wind speed and cloud cover.15.7 Forecasting the payoff density for a weather derivative.15.8 Electricity demand modelling.15.9 Concluding comments.References.Index.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle