An accompanying CD containing both the data and detailed examples of implementation of different techniques in Matlab will enable readers to retrace all the intermediate steps of a practical implementation of a model and test their 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 rush up on their technical skills. The text will be also of use to graduate students in electrical engineering, econometrics and fiance wanting to get a grip on advanced Statistical tools applied in this hot area. Complete with sixteen case studies, this book is a highly practical, self–contained tutorial to electricity load and price modeling and forecasting.
"the ability to predict correctly the system load, customer specific load and the electricity prices is of critical importance to any regulated utility, independent power producer, power marketers and traders. Given high volatility of electricity prices, even a small forecasting error can have a very significant impact on the bottom line. Dr. Weron′s book provides an in–depth, up–to–date and very well organized review of Statistical techniques for forecasting power load and prices and is highly recommended to any practitioner of the modern electricity markets."
Vince Kaminski, Managing Director, Citigroup, Houston and Adjunct Professor, Rice University, Houston
1 Complex Electricity Markets.
1.2 The Marketplace.
1.2.1 Power Pools and Power Exchanges.
1.2.2 Nodal and Zonal Pricing.
1.2.3 Market Structure.
1.2.4 Traded Products.
1.3.1 The England and Wales Electricity Market.
1.3.2 The Nordic Market.
1.3.3 Price Setting at Nord Pool.
1.3.4 Continental Europe 13.
1.4 North America.
1.4.1 PJM Interconnection.
1.4.2 California and the Electricity Crisis.
1.4.3 Alberta and Ontario.
1.5 Australia and New Zealand.
1.7 Further Reading.
2 Stylized Facts of Electricity Loads and Prices.
2.2 Price Spikes.
2.2.1 Case Study: The June 1998 Cinergy Price Spike.
2.2.2 When Supply Meets Demand.
2.2.3 What is Causing the Spikes?.
2.2.4 The Definition.
2.3.1 Measuring Serial Correlation.
2.3.2 Spectral Analysis and the Periodogram.
2.3.3 Case Study: Seasonal Behavior of Electricity Prices and Loads.
2.4 Seasonal Decomposition.
2.4.2 Mean or Median Week.
2.4.3 Moving Average Technique.
2.4.4 Annual Seasonality and Spectral Decomposition.
2.4.5 Rolling Volatility Technique.
2.4.6 Case Study: Rolling Volatility in Practice.
2.4.7 Wavelet Decomposition.
2.4.8 Case Study: Wavelet Filtering of Nord Pool Hourly System Prices.
2.5 Mean Reversion.
2.5.1 R/S Analysis.
2.5.2 Detrended Fluctuation Analysis.
2.5.3 Periodogram Regression.
2.5.4 Average Wavelet Coefficient.
2.5.5 Case Study: Anti–persistence of Electricity Prices.
2.6 Distributions of Electricity Prices.
2.6.1 Stable Distributions.
2.6.2 Hyperbolic Distributions.
2.6.3 Case Study: Distribution of EEX Spot Prices.
2.6.4 Further Empirical Evidence and Possible Applications.
2.8 Further Reading.
3 Modeling and Forecasting Electricity Loads.
3.2 Factors Affecting Load Patterns.
3.2.1 Case Study: Dealing with Missing Values and Outliers.
3.2.2 Time Factors.
3.2.3 Weather Conditions.
3.2.4 Case Study: California Weather vs Load.
3.2.5 Other Factors.
3.3 Overview of Artificial Intelligence–Based Methods.
3.4 Statistical Methods.
3.4.1 Similar–Day Method.
3.4.2 Exponential Smoothing.
3.4.3 Regression Methods.
3.4.4 Autoregressive Model.
3.4.5 Autoregressive Moving Average Model.
3.4.6 ARMA Model Identification.
3.4.7 Case Study: Modeling Daily Loads in California.
3.4.8 Autoregressive Integrated Moving Average Model.
3.4.9 Time Series Models with Exogenous Variables.
3.4.10 Case Study: Modeling Daily Loads in California with Exogenous Variables.
3.6 Further Reading.
4 Modeling and Forecasting Electricity Prices.
4.2 Overview of Modeling Approaches.
4.3 Statistical Methods and Price Forecasting.
4.3.1 Exogenous Factors.
4.3.2 Spike Preprocessing.
4.3.3 How to Assess the Quality of Price Forecasts.
4.3.4 ARMA–type Models.
4.3.5 Time Series Models with Exogenous Variables.
4.3.6 Autoregressive GARCH Models.
4.3.7 Case Study: Forecasting Hourly CalPX Spot Prices with Linear Models.
4.3.8 Case Study: Is Spike Preprocessing Advantageous?.
4.3.9 Regime–Switching Models.
4.3.10 Calibration of Regime–Switching Models.
4.3.11 Case Study: Forecasting Hourly CalPX Spot Prices with Regime–Switching Models.
4.3.12 Interval Forecasts.
4.4 Quantitative Models and Derivatives Valuation.
4.4.1 Jump–Diffusion Models.
4.4.2 Calibration of Jump–Diffusion Models.
4.4.3 Case Study: A Mean–Reverting Jump–Diffusion Model for Nord Pool Spot Prices.
4.4.4 Hybrid Models.
4.4.5 Case Study: Regime–Switching Models for Nord Pool Spot Prices.
4.4.6 Hedging and the Use of Derivatives.
4.4.7 Derivatives Pricing and the Market Price of Risk.
4.4.8 Case Study: Asian–Style Electricity Options.
4.6 Further Reading.