Core Concepts and Methods in Load Forecasting : With Applications in Distribution Networks

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ABOUT THE 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.

 TABLE OF CONTENTS

  1. Introduction

    • Stephen Haben, Marcus Voss, William Holderbaum
    Pages 1-11

  2. Primer on Distribution Electricity Networks

    • Stephen Haben, Marcus Voss, William Holderbaum
    Pages 13-22

  3. Primer on Statistics and Probability

    • Stephen Haben, Marcus Voss, William Holderbaum
    Pages 23-39

  4. Primer on Machine Learning

    • Stephen Haben, Marcus Voss, William Holderbaum
    Pages 41-53

  5. Time Series Forecasting: Core Concepts and Definitions

    • Stephen Haben, Marcus Voss, William Holderbaum
    Pages 55-66

  6. Load Data: Preparation, Analysis and Feature Generation

    • Stephen Haben, Marcus Voss, William Holderbaum
    Pages 67-88

  7. Verification and Evaluation of Load Forecast Models

    • Stephen Haben, Marcus Voss, William Holderbaum
    Pages 89-105

  8. Load Forecasting Model Training and Selection

    • Stephen Haben, Marcus Voss, William Holderbaum
    Pages 107-127

  9. Benchmark and Statistical Point Forecast Methods

    • Stephen Haben, Marcus Voss, William Holderbaum
    Pages 129-151

  10. Machine Learning Point Forecasts Methods

    • Stephen Haben, Marcus Voss, William Holderbaum
    Pages 153-199

  11. Probabilistic Forecast Methods

    • Stephen Haben, Marcus Voss, William Holderbaum
    Pages 201-227

  12. Load Forecast Process

    • Stephen Haben, Marcus Voss, William Holderbaum
    Pages 229-235

  13. Advanced and Additional Topics

    • Stephen Haben, Marcus Voss, William Holderbaum
    Pages 237-259

  14. Case Study: Low Voltage Demand Forecasts

    • Stephen Haben, Marcus Voss, William Holderbaum
    Pages 261-285

  15. Selected Applications and Examples

    • Stephen Haben, Marcus Voss, William Holderbaum
    Pages 287-304

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