Author: Rafal Weron
Publisher: John Wiley & Sons
ISBN: 0470059990
Category : Business & Economics
Languages : en
Pages : 192
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Book Description
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.
Author: Fany Nan
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
Author: Javier Contreras
Publisher: MDPI
ISBN: 3038424153
Category : Technology & Engineering
Languages : en
Pages : 259
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Book Description
This book is a printed edition of the Special Issue "Forecasting Models of Electricity Prices" that was published in Energies
Author: Mohamed E. El-Hawary
Publisher: John Wiley & Sons
ISBN: 1118171349
Category : Technology & Engineering
Languages : en
Pages : 324
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Book Description
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 operations. Written for practicing engineers, researchers, and post-grads concerned with power systems planning and forecasting, this book brings together contributions from many of the world’s foremost names in the field who address a range of critical issues, from forecasting power system load to power system pricing to post-storm service restoration times, river flow forecasting, and more. In a time of ever-increasing energy demands, mounting concerns over the environmental impacts of power generation, and the emergence of new, smart-grid technologies, electricity price forecasting has assumed a prominent role within both the academic and industrial arenas. Short-run forecasting of electricity prices has become necessary for power generation unit schedule, since it is the basis of every maximization strategy. This book fills a gap in the literature on this increasingly important topic. Following an introductory chapter offering background information necessary for a full understanding of the forecasting issues covered, this book: Introduces advanced methods of time series forecasting, as well as neural networks Provides in-depth coverage of state-of-the-art power system load forecasting and electricity price forecasting Addresses river flow forecasting based on autonomous neural network models Deals with price forecasting in a competitive market Includes estimation of post-storm restoration times for electric power distribution systems Features contributions from world-renowned experts sharing their insights and expertise in a series of self-contained chapters Advances in Electric Power and Energy Systems is a valuable resource for practicing engineers, regulators, planners, and consultants working in or concerned with the electric power industry. It is also a must read for senior undergraduates, graduate students, and researchers involved in power system planning and operation.
Author: Eran Raviv
Publisher:
ISBN:
Category :
Languages : en
Pages : 35
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Book Description
The daily average price of electricity represents the price of electricity to be delivered over the full next day and serves as a key reference price in the electricity market. It is an aggregate that equals the average of hourly prices for delivery during each of the 24 individual hours. This paper demonstrates that the disaggregated hourly prices contain useful predictive information for the daily average price. Multivariate models for the full panel of hourly prices significantly outperform univariate models of the daily average price, with reductions in Root Mean Squared Error of up to 16%. Substantial care is required in order to achieve these forecast improvements. Rich multivariate models are needed to exploit the relations between different hourly prices, but the risk of overfitting must be mitigated by using dimension reduction techniques, shrinkage and forecast combinations.
Author: Behrouz Banitalebi
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
Electricity price forecasting plays an important role in decision-making on bidding strategies of selling and buying electricity. This thesis computes one-day-ahead quantile forecasts of electricity prices in a highly volatile market by applying regression models to a pool of point forecasts. Three data-driven forecasting methods are implemented to generate day-ahead point forecasts of the Ontario market's electricity prices. In order to generate the three sets of point forecasts, I use: i) the Triple Exponential Smoothing (TES) method, ii) a Neural Network (NN) that combines layers of Convolutional neurons and Gradient Recurrent Units (GRU), iii) an eXtreme Gradient Boosting (XGB) non-linear regression approach. The performance of the three models is compared against a benchmark that considers the forecast of electricity prices as the average price of the same hour and day during the last four weeks. The TES method decreases the Mean Absolute Error (MAE) of the benchmark model from 10.29 to 9.42. The Convolutional GRU (ConvGRU) model and XGB regression also reduce the MAE to 8.20 and 7.06, respectively. Finally, Quantile Regression Averaging (QRA) is applied to the pool of point forecasts obtained by TES, ConvGRU, and XGB methods to compute day-ahead quantile forecasts of electricity prices. Moreover, the QRA method is further developed in this thesis by employing Gradient Boosting Regression (GBR). It follows from my real data analysis that the GBR method provides more reliable quantiles and tighter prediction intervals with smaller forecasting errors than QRA. The obtained probabilistic forecasts are used to find the optimal energy procurement plan for a large consumer and the linear programming method is applied to solve the problem. The simulation results indicate that using probabilistic forecasts of electricity prices leads to a more flexible and efficient bidding strategy than using point forecasts. Moreover, the regularized probabilistic forecast of day-ahead electricity demands is computed and used to model power generation units' scheduling.
Author: Adela Maria Bolet
Publisher: Routledge
ISBN: 0429691459
Category : Political Science
Languages : en
Pages : 274
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Book Description
Although the energy headlines of 1985 proclaim the waning of OPEC, the collapse of oil prices, and the demise of the nuclear power industry, few policy analysts are examining the dynamic challenges and opportunities that may confront the electric power industry during the remainder of this century. In this pioneering work, Adela Maria Bolet attempts to do exactly this, namely, to reconcile the differences among forecasters as to the future of electricity demand in the industrial, commercial, and residential sectors.
Author: Markus Burger
Publisher: John Wiley & Sons
ISBN: 9780470725467
Category : Business & Economics
Languages : en
Pages : 316
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Book Description
Mathematical techniques for trading and risk management. Managing Energy Risk closes the gap between modern techniques from financial mathematics and the practical implementation for trading and risk management. It takes a multi-commodity approach that covers the mutual influences of the markets for fuels, emission certificates, and power. It includes many practical examples and covers methods from financial mathematics as well as economics and energy-related models.
Author: Marcel Prokopczuk
Publisher: Palgrave Macmillan
ISBN: 9781137377340
Category : Business & Economics
Languages : en
Pages : 0
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Book Description
Following the liberalization of global energy markets, the world has witnessed a substantial growth in energy commodity trading. Moreover, prices and volatilities have significantly increased, partly due to geopolitical crises, but mostly resulting from increased participation of financial investors. Such newfound interest in energy markets has spawned greater demand for state-of-the-art models and methods necessary to understand the challenges related to trading and risk management. Energy Pricing Models showcases original cutting-edge research to best illustrate the latest advances and future implications of trading in energy markets. Prokopczuk assembles an all-star team of leading academics and practitioners in order to provide a well-balanced analysis of the topic. This work is required reading for market practitioners wishing to gain greater insight into the field, as well as academics and researchers interested in learning more about the latest developments from an applied perspective.
Author: Jian Xu (Ph. D in electrical and computer engineering)
Publisher:
ISBN:
Category :
Languages : en
Pages : 210
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Book Description
Electricity generation and load should always be balanced to maintain a tightly regulated system frequency in the power grid. Electricity generation and load both depend on many factors, such as the weather, temperature, and wind. These characteristics make the dynamics of electricity price very different from that of any other commodities or financial assets. The electricity price can exhibit hourly, daily, and seasonal fluctuations, as well as abrupt unanticipated spikes. Almost all electricity market participants use wind/load/price forecasting tools in their daily operations to optimize their operation plans, and bidding and hedging strategies, in order to maximize the profits and avoid price risks. However, the unreliable and inaccurate predictions with current forecasting tools have caused many serious problems, which can cause system instabilities and result in extreme prices even in the absence of scarcity. This dissertation presents an implementation of state of the art machine learning approaches into the forecasting tools to improve the reliability and accuracy of electricity price prediction. Most existing wholesale electricity markets consist of a Day-Ahead Market and a Real-Time Market that work together to ensure the adequacy of electricity generation capacity for the Real-Time operation to secure the reliability of the grid. The two markets have different purposes, with the Day-Ahead Market serving as preparation for and hedging against variation in the Real-Time Market. Also, the Day-Ahead Market uses hourly Day-Ahead forecasting information and the Real-Time Market uses most up-to-date Real-Time information when running calculations. So the forecasting strategies of Day-Ahead and Real-Time Markets should be different as well. The dissertation has two parts. The first part focuses on Day-Ahead price forecasting and the second part focuses on Real-Time price forecasting
