Energy Time Series Forecasting Efficient and Accurate Forecasting of
Lars Dannecker developed a novel online forecasting process that significantly improves how forecasts are calculated. It increases forecasting efficiency and accuracy, as well as allowing the process to adapt to different situations and applications. Impr
- PDF / 7,119,179 Bytes
- 241 Pages / 419.52 x 595.2 pts Page_size
- 108 Downloads / 310 Views
Lars Dannecker
Energy Time Series Forecasting Efficient and Accurate Forecasting of Evolving Time Series from the Energy Domain
Lars Dannecker Dresden, Germany Doctorate at the Technische Universität Dresden, 20.11.2014 Original title: Efficient and Accurate Forecasting of Evolving Time Series from the Energy Domain
ISBN 978-3-658-11038-3 ISBN 978-3-658-11039-0 (eBook) DOI 10.1007/978-3-658-11039-0 Library of Congress Control Number: 2015947268 Springer Vieweg © Springer Fachmedien Wiesbaden 2015 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, speci¿cally the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on micro¿lms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a speci¿c statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, express or implied, with respect to the material contained herein or for any errors or omissions that may have been made. Printed on acid-free paper Springer Vieweg is a brand of Springer Fachmedien Wiesbaden Springer Fachmedien Wiesbaden is part of Springer Science+Business Media (www.springer.com)
To my wife Ulrike and my daughter Luisa.
Preface
Continuous balancing of electric power consumption and production is a fundamental prerequisite for the stability and efficiency of electricity grids. This balancing task requires accurate forecasts of future electricity demand and supply at any point in time. For this purpose, today’s energy data management systems (EDMS) typically use quantitative models—called forecast models—that already provide accurate predictions. However, recent developments in the energy domain such as real-time intra-day trading and the integration of more renewable energy sources also require more efficient forecasting calculations and a rapid provisioning of forecasting results. Furthermore, today’s EDMSs fulfill a number of different tasks, each exhibiting different requirements for the calculation of forecasts with respect to runtime and accuracy. Thus, it is necessary to flexibly adapt the forecasting process with respect to the needs of the current requests. In contrast, currently employed forecasting approaches are rather time-consuming and inflexible. One reason is the very expensive estimation of the forecast model parameters, involving a large number of simulations in a search space that increases exponential with the numbe
Data Loading...
