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Forecasting and Assessing Risk of Individual Electricity Peaks.

Bibliographic Details
Main Author: Jacob, Maria.
Other Authors: Neves, Cláudia., Vukadinović Greetham, Danica.
Format: eBook
Language:English
Published: Cham : Springer International Publishing AG, 2019.
Edition:1st ed.
Series:Mathematics of Planet Earth Series
Subjects:
Electronic books.
Online Access:Click to View
Table of Contents:
  • Intro
  • Preface
  • Contents
  • Acronyms
  • 1 Introduction
  • 1.1 Forecasting and Challenges
  • 1.2 Data
  • 1.2.1 Irish Smart Meter Data
  • 1.2.2 Thames Valley Vision Data
  • 1.3 Outline and Objectives
  • References
  • 2 Short Term Load Forecasting
  • 2.1 Forecasts
  • 2.1.1 Linear Regression
  • 2.1.2 Time Series Based Algorithms
  • 2.1.3 Permutation Based Algorithms
  • 2.1.4 Machine Learning Based Algorithms
  • 2.2 Forecast Errors
  • 2.2.1 Point Error Measures
  • 2.2.2 Time Shifted Error Measures
  • 2.3 Discussion
  • References
  • 3 Extreme Value Theory
  • 3.1 Basic Definitions
  • 3.2 Maximum of a Random Sample
  • 3.3 Exceedances and Order Statistics
  • 3.3.1 Exceedances
  • 3.3.2 Asymptotic Distribution of Certain Order Statistics
  • 3.4 Extended Regular Variation
  • References
  • 4 Extreme Value Statistics
  • 4.1 Block Maxima and Peaks over Threshold Methods
  • 4.2 Maximum Lq-Likelihood Estimation with the BM Method
  • 4.2.1 Upper Endpoint Estimation
  • 4.3 Estimating and Testing with the POT Method
  • 4.3.1 Selection of the Max-Domain of Attraction
  • 4.3.2 Testing for a Finite Upper Endpoint
  • 4.3.3 Upper Endpoint Estimation
  • 4.4 Non-identically Distributed Observations-Scedasis Function
  • References
  • 5 Case Study
  • 5.1 Predicting Electricity Peaks on a Low Voltage Network
  • 5.1.1 Short Term Load Forecasts
  • 5.1.2 Forecast Uncertainty
  • 5.1.3 Heteroscedasticity in Forecasts
  • References
  • Index.

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