Short- and long-term forecasting of wind speed in Limpopo Province using machine learning algorithm and extreme value theory

Aug 5, 2026

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Numerous studies have applied Extreme Value Theory (EVT) to model environmental variables like wind speed, rainfall and temperature. Recent research increasingly integrates machine learning algorithms for similar tasks. This mini dissertation applies EVT and machine learning to model wind speed in Limpopo Province to evaluate wind power generation reliability. Wind-speed data from NASA covers 2016–2022. A Vanilla LSTM network achieved 86% training and 89% testing accuracy.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages69

Reading Time2 h 54 min

AccessFree to download

Introduction and background

Introduction

Background

Problem statement

Rationale

Aim and objectives

Contribution of the study

Structure of the dissertation

Literature review

Introduction

Overview of literature in South Africa

Worldwide overview of literature

Importance of renewable resources

Identification of existing research gaps

Summary of the chapter

Methodology

Research methodology

Data source and study area

Tests for stationarity

Trend analysis

Fundamental distributions

Feature scaling

Vanilla LSTM network

Extreme Value Theory model

Generalised Extreme Value Contribution (GEVD)

Parameter estimation

Return levels

Evaluations metrics for the Vanilla LSTM

Evaluations metrics for the GEVD

Results and discussion

Data wrangling

Descriptive statistics

Diagnostic plots

Test for stationarity

Nonparametric trend estimation

Machine learning results

SHORT-AND LONG-TERM FORECASTING OF WIND SPEED IN LIMPOPO PROVINCE USING MACHINE LEARNING ALGORITHM AND EXTREME VALUE THEORY by KGOTHATSO MAKUBYANE MINI DISSERTATION SUBMITTED IN PARTIAL FULFILMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE in E-SCIENCE in the FACULTY OF SCIENCE AND AGRICULTURE SCHOOL OF MATHEMATICAL AND COMPUTER SCIENCES at the UNIVERSITY OF LIMPOPO SUPERVISOR: PROF. D MAPOSA FEBRUARY 2024 Declaration I, Kgothatso Makubyane, therefore acknowledge that the mini dissertation titled ”Short- and long-term forecasting of wind speed in Limpopo Province using machine learning algorithm and extreme value theory” is my original work. All material from other sources including any works created by other persons or organisation that was used in this research study was appropriately attributed and referenced. I additionally testify that this research study has never been submitted by anybody from another university. Signature:........KM.............Date:......03 February 2024 ........... Makubyane, K. Copyright © 2023 University of Limpopo All rights reserved Abstract Numerous studies have applied Extreme Value Theory (EVT) to model environmental variables like wind speed, rainfall and temperature. Recently, academic focus has shifted to machine learning algorithms for the same variables. This research study demonstrates the practical use of EVT and machine learning techniques for modelling wind speed in the Limpopo Province, with the primary goal of assessing wind power generation reliability. The data used in this research study is obtained from National Aeronautics and Space Administration (NASA), spanning the time period from 2016 to 2022 . The Vanilla Long ShortTerm Memory (LSTM) network exhibited remarkable accuracy, achieving 86% training and 89% testing accuracy. Additionally, Generalised Extreme Value Distribution (GEVD) for block sizes (1 to 5) revealed GEV Dm=2 as the most suitable model based on low Akaike information criteria (AIC) and Bayesian information criteria (BIC) values. The model highlighted a rare event with a 300-year return period, indicating a wind speed of 22.893 meters. This study provides valuable insights for careful power planning, economic strategy and advancement in civilisation in South Africa, with implications for future energy planning and policy decisions in the region. Keywords: Akaike information criteria, Bayesian information criteria, Extreme Value Theory, Generalised Extreme Value Distribution, Long Short-Term Memory, National Aeronautics and Space Administration, and Wind Power Generation Dedication I dedicate this research study to my daughter Thatego Mahlangu and my parents, Doris Kanyane Makubyane, Thabo Chadwick Mogashoa, and to all my siblings, whose loyal love, affection, and inspiration have been the base foundation behind my academic journey. Acknowledgments I am grateful to the most high God who is above us all for the wisdom, fortitude and bravery he has given me throughout my academic path. My deepest thanks and gratitude goes out to my supervisor, Prof. D. Maposa, for his patience, encouragement, insightful remarks and ideas, not to mention the assistance provided by Dr. Helen Robertson from the University of Witwatersrand. Finally, I would want to thank the Department of Statistics and Operations Research, the academic staff who taught me from the undergraduate to postgraduate levels and the entire Team NEPTTP for granting me the funding to further my education. Contents Declaration i Abstract ii Dedication iii Acknowledgments iv List of Figures viii List of Tables ix 1 Introduction and background 1 1.1 Introduction ............................... 1 1.2 Background ............................... 3 1.3 Problem statement ........................... 4 1.4 Rationale ................................. 5 1.5 Aim and objectives ........................... 7 1.5.1 Aim ..............

FAQs

What is the main objective of the study on wind speed in Limpopo Province?

The study applies EVT and machine learning to model wind speed in Limpopo Province and assess wind power generation reliability.

Which dataset is used and what time period does it cover?

The research uses wind-speed data obtained from NASA, covering the period from 2016 to 2022.

How does the Vanilla LSTM model perform in training and testing?

The Vanilla LSTM network achieves 86% training accuracy and 89% testing accuracy.

Aug 5, 2026

Upload by Aurelia