Analysis Scripts and Derived Data for: Seasonal Complementarity and Machine Learning-Based Wind Power Potential Assessment Across Diverse Climatic Zones of Ethiopia Using Ground-Based Measurement Data

Domaine:

environment and energy

Type de record:

dataset

Créateur:

Fil

Éditeur:

Hôte:

This repository contains all Python analysis scripts and derived data files supporting the manuscript "Seasonal Complementarity and Machine Learning-Based Wind Power Potential Assessment Across Diverse Climatic Zones of Ethiopia Using Ground-Based Measurement Data" submitted to Discover Energy (Springer Nature). The raw wind measurement data used as input are publicly available from the World Bank energydata.info repository under a Creative Commons Attribution 4.0 International licence at energydata.info. Contents:- Python scripts for all analysis stages (data cleaning, Weibull fitting, capacity factor estimation, ML pipeline, clustering, ranking, sensitivity analysis, site map generation)- QC-cleaned site CSV files derived from the ESMAP raw data- Derived results CSVs (Weibull parameters, capacity factors, ML results, sensitivity analysis, site ranking) Requirements: Python 3.9+, pandas, numpy, scipy, scikit-learn, xgboost, shap, matplotlib, geopandas, contextily Author: Sena Gemechis FileORCID: 0009-0007-9742-9530Institution: Faculty of Electrical and Computer Engineering, Jimma Institute of Technology, Jimma University, Ethiopia

A Machine Learning–Based Adaptive Framework for Wind Energy Potential Assessment across Nigeria’s Climatic Zones

The current focus on transitioning of energy to cleaner and more sustainable energy systems has led

A data-driven geographic information system and machine learning based multi-criteria framework for strategic wind power plant siting

Abstract Wind energy site selection requires robust framewo

Wind turbine power output prediction model design based on artificial neural networks and climatic spatiotemporal data

International audience This paper deals with the prediction of wind turbines power ou

Modelling Potential Fishing Zones in Kenya's Exclusive Economic Zones Using Machine Learning and Satellite-Derived Oceanographic Variables

Kenya's Exclusive Economic Zone (EEZ) in the Indian Ocean spans approximately 142,400 km², of which

Machine Learning-Based Evaluation of Seasonal and Spatial Variations in Soil Quality Across Agroecological Zones of Lagos State, Nigeria

This study assesses seasonal and spatial variations in soil quality across four agroecological zones

Application of Machine Learning for Radio Refractivity Prediction across Climatic Zones in West Africa

International audience This research explores the use of machine learning (ML) models