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Advances in Electric Power and Energy : Forecasting in Electric Power Systems.
A comprehensive review of state-of-the-art approaches to power systems forecasting from the most respected names in the field, internationally Advances in Electric Power and Energy Systems is the first book devoted exclusively to a subject of increasing urgency to power systems planning and operatio...
| Clasificación: | Libro Electrónico |
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| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
New York :
John Wiley & Sons, Incorporated,
2013.
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| Colección: | IEEE Press Series on Power Engineering Ser.
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| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- Advances in Electric Power and Energy Systems; Contents; Preface and Acknowledgments; Contributors; 1 Introduction; Prelude; Forecasting: General Considerations; Forecasting in Electric Power Systems; Load Forecasting in Electric Power Systems; Electricity Price Forecasting in Electric Power Systems; Time Series Analysis; Multiplicative and Additive Models; Occams Razor, the Principle of Parsimony; The Stationarity Concept; The Autoregressive (AR) Process; Moving Average Processes; Autoregressive Moving Average Processes; Integrated Processes and ARIMA Models.
- Seasonality and Seasonal ARIMA ModelsArtificial Neural Networks; Radial Basis Function Networks; Overview of Chapters; References; 2 Univariate Methods for Short-Term Load Forecasting; Introduction; Intraday Load Data; Univariate Methods for Load Forecasting; Simplistic Benchmark Methods; Seasonal ARMA; Periodic AR; Exponential Smoothing for Double Seasonality; Intraday Cycle Exponential Smoothing; A Method Based on Principal Components Analysis; Empirical Forecasting Study; Extensions of the Methods; Very Short-Term Forecasting with Minute-by-Minute Data.
- Recently Developed Exponentially Weighted MethodsTriple Seasonal Methods for Multiple Years of Intraday Data; Summary and Concluding Comments; Acknowledgments; References; 3 Application of the Weighted Nearest Neighbor Method to Power System Forecasting Problems; Introduction; Background; Data Mining Techniques and Time Series Analysis; Demand and Price Forecasting in Power Systems; Weighted Nearest Neighbors Methodology; Determination of the Weighting Coefficients; Tuning the Model; Performance assessment; Application to aggregated load forecasting; Description of the Data Base; Test Results.
- Comparison with ARApplication to pool energy price forecasting; Characterization of Spanish Electricity Market Prices; Test Results; Comparison with Other Techniques; Application to customer-level forecasting; Characterization and Processing of Data Records; Test Results; Comparison with an AR Model; Conclusions; References; 4 Electricity Prices as a Stochastic Process; Introduction; Characteristics of Electricity Prices; Stochastic Process Models for Electricity Prices; From Random Walk to Brownian Motion; Brownian Motion with Drift; Ito Process; Geometric Brownian Motion.
- Mean Reversion ProcessProperties of a Lognormal Distribution; Risk-Adjusted Price; Electricity Price with Spikes; Time Series Methods for Electricity Prices; Autoregressive (AR) Model; Autoregressive Moving Average (ARMA) Model; Volatility of Prices; Numerical Examples; Estimate Parameters of Mean Reversion Process by AR Model; Numerical Examples of AR Model; Numerical Examples of ARMA Model; Conclusions; Acknowledgment; References; 5 Short-Term Forecasting of Electricity Prices Using Mixed Models; Introduction and Problem Statement; State of the Art; Models Presented in this Chapter.