Modeling and Forecasting Electricity Loads and Prices: A Statistical Approach (The Wiley Finance Series)

✍ Scribed by Rafal Weron

• Year: 2006
• Tongue: English
• Leaves: 195
• Edition: 1
• Category: Library

✦ Synopsis

This book offers an in-depth and up-to-date review of different statistical tools that can be used to analyze and forecast the dynamics of two crucial for every energy company processes—electricity prices and loads. It provides coverage of seasonal decomposition, mean reversion, heavy-tailed distributions, exponential smoothing, spike preprocessing, autoregressive time series including models with exogenous variables and heteroskedastic (GARCH) components, regime-switching models, interval forecasts, jump-diffusion models, derivatives pricing and the market price of risk.Modeling and Forecasting Electricity Loads and Prices is packaged with a CD containing both the data and detailed examples of implementation of different techniques in Matlab, with additional examples in SAS. A reader can retrace all the intermediate steps of a practical implementation of a model and test his understanding of the method and correctness of the computer code using the same input data.The book will be of particular interest to the quants employed by the utilities, independent power generators and marketers, energy trading desks of the hedge funds and financial institutions, and the executives attending courses designed to help them to brush up on their technical skills. The text will be also of use to graduate students in electrical engineering, econometrics and finance wanting to get a grip on advanced statistical tools applied in this hot area. In fact, there are sixteen Case Studies in the book making it a self-contained tutorial to electricity load and price modeling and forecasting.

✦ Table of Contents

Modeling and Forecasting Electricity Loads and Prices......Page 4
Contents......Page 8
Preface......Page 12
Acknowledgments......Page 16
1.1 Liberalization......Page 18
1.2.1 Power Pools and Power Exchanges......Page 20
1.2.2 Nodal and Zonal Pricing......Page 23
1.2.4 Traded Products......Page 24
1.3.1 The England and Wales Electricity Market......Page 26
1.3.3 Price Setting at Nord Pool......Page 28
1.3.4 Continental Europe......Page 30
1.4 North America......Page 35
1.4.1 PJM Interconnection......Page 36
1.4.2 California and the Electricity Crisis......Page 37
1.4.3 Alberta and Ontario......Page 38
1.5 Australia and New Zealand......Page 39
1.7 Further Reading......Page 40
2.2 Price Spikes......Page 42
2.2.1 Case Study: The June 1998 Cinergy Price Spike......Page 45
2.2.2 When Supply Meets Demand......Page 46
2.3 Seasonality......Page 49
2.3.1 Measuring Serial Correlation......Page 53
2.3.2 Spectral Analysis and the Periodogram......Page 56
2.3.3 Case Study: Seasonal Behavior of Electricity Prices and Loads......Page 57
2.4 Seasonal Decomposition......Page 58
2.4.1 Differencing......Page 59
2.4.4 Annual Seasonality and Spectral Decomposition......Page 61
2.4.5 Rolling Volatility Technique......Page 62
2.4.6 Case Study: Rolling Volatility in Practice......Page 63
2.4.7 Wavelet Decomposition......Page 64
2.5 Mean Reversion......Page 66
2.5.1 R/S Analysis......Page 67
2.5.2 Detrended Fluctuation Analysis......Page 69
2.5.4 Average Wavelet Coefficient......Page 70
2.5.5 Case Study: Anti-persistence of Electricity Prices......Page 71
2.6.1 Stable Distributions......Page 73
2.6.2 Hyperbolic Distributions......Page 75
2.6.3 Case Study: Distribution of EEX Spot Prices......Page 76
2.6.4 Further Empirical Evidence and Possible Applications......Page 79
2.8 Further Reading......Page 81
3.1 Introduction......Page 84
3.2.1 Case Study: Dealing with Missing Values and Outliers......Page 86
3.2.3 Weather Conditions......Page 88
3.2.4 Case Study: California Weather vs Load......Page 89
3.2.5 Other Factors......Page 91
3.3 Overview of Artificial Intelligence-Based Methods......Page 92
3.4 Statistical Methods......Page 95
3.4.2 Exponential Smoothing......Page 96
3.4.3 Regression Methods......Page 98
3.4.4 Autoregressive Model......Page 99
3.4.5 Autoregressive Moving Average Model......Page 100
3.4.6 ARMA Model Identification......Page 101
3.4.7 Case Study: Modeling Daily Loads in California......Page 103
3.4.8 Autoregressive Integrated Moving Average Model......Page 112
3.4.9 Time Series Models with Exogenous Variables......Page 114
3.4.10 Case Study: Modeling Daily Loads in California with Exogenous Variables......Page 115
3.6 Further Reading......Page 117
4.1 Introduction......Page 118
4.2 Overview of Modeling Approaches......Page 119
4.3.1 Exogenous Factors......Page 123
4.3.3 How to Assess the Quality of Price Forecasts......Page 124
4.3.4 ARMA-type Models......Page 126
4.3.5 Time Series Models with Exogenous Variables......Page 128
4.3.6 Autoregressive GARCH Models......Page 130
4.3.7 Case Study: Forecasting Hourly CalPX Spot Prices with Linear Models......Page 131
4.3.8 Case Study: Is Spike Preprocessing Advantageous?......Page 142
4.3.9 Regime-Switching Models......Page 144
4.3.11 Case Study: Forecasting Hourly CalPX Spot Prices with Regime-Switching Models......Page 149
4.4 Quantitative Models and Derivatives Valuation......Page 153
4.4.1 Jump-Diffusion Models......Page 154
4.4.2 Calibration of Jump-Diffusion Models......Page 156
4.4.3 Case Study: A Mean-Reverting Jump-Diffusion Model for Nord Pool Spot Prices......Page 157
4.4.4 Hybrid Models......Page 160
4.4.5 Case Study: Regime-Switching Models for Nord Pool Spot Prices......Page 161
4.4.6 Hedging and the Use of Derivatives......Page 164
4.4.7 Derivatives Pricing and the Market Price of Risk......Page 165
4.4.8 Case Study: Asian-Style Electricity Options......Page 167
4.5 Summary......Page 170
4.6 Further Reading......Page 171
Bibliography......Page 174
Subject Index......Page 188