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  • Textbook
  • Open Access
  • © 2023

Core Concepts and Methods in Load Forecasting

With Applications in Distribution Networks

  • Is the first textbook on load forecasting for the distribution network

  • Brings together both statistical and machine learning topics

  • Includes colorful illustrations and practical examples from many sectors and developing countries

  • Is open access, which means that you have free and unlimited access

Buying options

Hardcover Book USD 59.99
Price excludes VAT (USA)
  • ISBN: 978-3-031-27851-8
  • Dispatched in 3 to 5 business days
  • Exclusive offer for individuals only
  • Free shipping worldwide
    See shipping information.
  • Tax calculation will be finalised during checkout

Table of contents (15 chapters)

  1. Front Matter

    Pages i-xv
  2. Introduction

    • Stephen Haben, Marcus Voss, William Holderbaum
    Pages 1-11Open Access
  3. Primer on Distribution Electricity Networks

    • Stephen Haben, Marcus Voss, William Holderbaum
    Pages 13-22Open Access
  4. Primer on Statistics and Probability

    • Stephen Haben, Marcus Voss, William Holderbaum
    Pages 23-39Open Access
  5. Primer on Machine Learning

    • Stephen Haben, Marcus Voss, William Holderbaum
    Pages 41-53Open Access
  6. Time Series Forecasting: Core Concepts and Definitions

    • Stephen Haben, Marcus Voss, William Holderbaum
    Pages 55-66Open Access
  7. Load Data: Preparation, Analysis and Feature Generation

    • Stephen Haben, Marcus Voss, William Holderbaum
    Pages 67-88Open Access
  8. Verification and Evaluation of Load Forecast Models

    • Stephen Haben, Marcus Voss, William Holderbaum
    Pages 89-105Open Access
  9. Load Forecasting Model Training and Selection

    • Stephen Haben, Marcus Voss, William Holderbaum
    Pages 107-127Open Access
  10. Benchmark and Statistical Point Forecast Methods

    • Stephen Haben, Marcus Voss, William Holderbaum
    Pages 129-151Open Access
  11. Machine Learning Point Forecasts Methods

    • Stephen Haben, Marcus Voss, William Holderbaum
    Pages 153-199Open Access
  12. Probabilistic Forecast Methods

    • Stephen Haben, Marcus Voss, William Holderbaum
    Pages 201-227Open Access
  13. Load Forecast Process

    • Stephen Haben, Marcus Voss, William Holderbaum
    Pages 229-235Open Access
  14. Advanced and Additional Topics

    • Stephen Haben, Marcus Voss, William Holderbaum
    Pages 237-259Open Access
  15. Case Study: Low Voltage Demand Forecasts

    • Stephen Haben, Marcus Voss, William Holderbaum
    Pages 261-285Open Access
  16. Selected Applications and Examples

    • Stephen Haben, Marcus Voss, William Holderbaum
    Pages 287-304Open Access
  17. Back Matter

    Pages 305-331

About this book

This comprehensive open access book enables readers to discover the essential techniques for load forecasting in electricity networks, particularly for active distribution networks.

From statistical methods to deep learning and probabilistic approaches, the book covers a wide range of techniques and includes real-world applications and a worked examples using actual electricity data (including an example implemented through shared code). Advanced topics for further research are also included, as well as a detailed appendix on where to find data and additional reading. As the smart grid and low carbon economy continue to evolve, the proper development of forecasting methods is vital.

This book is a must-read for students, industry professionals, and anyone interested in forecasting for smart control applications, demand-side response, energy markets, and renewable utilization.

 


Keywords

  • Load forecasting
  • Probabilistic forecasting
  • Smart grid applications
  • Distribution networks
  • Low voltage
  • Smart meter
  • Demand management
  • Time series analysis
  • Time series forecasting
  • Smart Storage
  • Open Access

Authors and Affiliations

  • Mathematical Institute, University of Oxford, Oxford, UK

    Stephen Haben

  • Faculty IV—Electrical Engineering and Computer Science, TU Berlin, Berlin, Germany

    Marcus Voss

  • Engineering, University of Reading, Reading, UK

    William Holderbaum

About the authors

Stephen Haben has been working in forecasting and data analytics for over 15 years. His Ph.D. was in Data Assimilation for numerical weather prediction which was supported with the UK’s meteorological office (Met Office). From 2011, Stephen worked on and led the academic team on the Thames Valley Vision project, a low carbon network fund project where the aim was to investigate new technologies and solutions which could help support the future low carbon economy. This included investigating forecasting methods for low-voltage networks, integrating the forecasts within storage control algorithms, modelling consumer demand, simulating low-voltage network demand and producing long-term scenario forecasts to understand the impact of high uptakes of low carbon technologies such as electric vehicles, PV and heat pumps. Since 2018, Stephen has worked as a data scientist and then digital and data consultant at Energy Systems Catapult, a not-for-profit, independent technology and innovation centre which supports innovators in the energy sector and aims to help bridge the gap between academia, industry and government. He continues to research low voltage forecasting methods and energy analytics as part of his visiting research position at the Mathematical Institute at the University of Oxford.

Professor William Holderbaum is a professor of Mathematics and Engineering at the University of Reading in the UK. He has played major leadership roles in research, while maintaining a very strong international reputation and an extensive list of industrial collaborations. Over the years, he has applied his control and mathematical modelling expertise to several applications and in particular energy transmission, storage for electrical systems and power systems. In 2011 he was involved in the Thames Valley Vision (TVV) project, a £30M low carbon network fund project. The aim of the projects is to assist distribution network operators in the UK to prepare for the future low carbon economy by testing various technological and operational solutions. Furthermore, he led a Climate/KIC EU-funded project “Susports: delivering sustainable energy solutions to ports” which had the aim of reducing greenhouse gases emissions in ports using data analysis and forecasts of energy flows to eventually develop a system which will reduce fuel consumption and will enable energy recovery using energy storage devices and innovative control techniques.

Marcus Voss has been working at the intersection of machine learning, climate change, the energy transition and sustainability for over 10 years. He is an AI Expert and Intelligence Architect at Birds on Mars, where he is responsible for AI applications for advancing sustainability. He serves on the board of directors at Climate Change AI, an international NPO catalysing impactful work at the intersection of climate change and machine learning. There he is the Community Lead for buildings and transportation. He has been an external lecturer on AI and data science, for instance, at TU Berlin, Leuphana University Lüneburg, and CODE University. Previously, Marcus Voss was a Research Associate at TU Berlin, where he led the Smart Energy Systems research group of the DAI-Labor, working on AI applications in the smart grid and the sustainable development of AI systems. In his doctoral research, he has been working on data analysis of low voltage-level smart meter data with a focus on load forecasting using machine learning methods.


Bibliographic Information

  • Book Title: Core Concepts and Methods in Load Forecasting

  • Book Subtitle: With Applications in Distribution Networks

  • Authors: Stephen Haben, Marcus Voss, William Holderbaum

  • DOI: https://doi.org/10.1007/978-3-031-27852-5

  • Publisher: Springer Cham

  • eBook Packages: Energy, Energy (R0)

  • Copyright Information: The Editor(s) (if applicable) and The Author(s) 2023

  • License: CC BY

  • Hardcover ISBN: 978-3-031-27851-8Published: 01 May 2023

  • Softcover ISBN: 978-3-031-27854-9Due: 15 May 2024

  • eBook ISBN: 978-3-031-27852-5Published: 30 April 2023

  • Edition Number: 1

  • Number of Pages: XV, 331

  • Number of Illustrations: 50 b/w illustrations, 89 illustrations in colour

  • Topics: Power Stations, Applied Probability, Electrical Power Engineering, Neural Circuits, Mechanical and Thermal Energy Storage, Control, Robotics, Automation

Buying options

Hardcover Book USD 59.99
Price excludes VAT (USA)
  • ISBN: 978-3-031-27851-8
  • Dispatched in 3 to 5 business days
  • Exclusive offer for individuals only
  • Free shipping worldwide
    See shipping information.
  • Tax calculation will be finalised during checkout