Forecasting and Assessing Risk of Individual Electricity Peaks.

• Superior document: Mathematics of Planet Earth Series
• Author: Jacob, Maria.
• TeilnehmendeR: Neves, Cláudia.; Vukadinović Greetham, Danica.
• Place / Publishing House: Cham : : Springer International Publishing AG,, 2019.; ©2020.
• Year of Publication: 2019
• Edition: 1st ed.
• Language: English
• Series: Mathematics of Planet Earth Series
• Physical Description: 1 online resource (108 pages)

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.