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Lookup NU author(s): Dr Hannah BloomfieldORCiD
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0).
This study presents a robust framework for addressing systematic biases in the ERA5 wind speeds to model long-term, high-resolution wind energy and characterise wind power extremes in data-sparse regions. By integrating Weibull Quantile Mapping, hub-height extrapolation, and dynamic efficiency, the study models hourly output for three Kenyan wind farms: Lake Turkana Wind Power, Kipeto, and Ngong Hills. The model significantly reduced Mean Bias Error and Root Mean Square Error in the reanalysis while preserving temporal rank correlations. The reanalysis-driven model captures the fundamental variability of wind power generation. Persistence and ramp diagnostics using Threshold-Duration Frequency analysis reveal that: LTWP exhibits low variability, with high-output events (>80% Capacity Factor) sustained for durations exceeding 14 days, contrasting with the frequent multi-day droughts and pronounced ramping typical of mid-latitude wind turbine fleets. Kipeto and Ngong Hills sites exhibit strong diurnal cycling, necessitating short-term storage rather than seasonal balancing. While LTWP frequently undergoes large shifts (>60% Δ Capacity Factor) over diurnal cycles, extreme volatility at shorter timescales (3-h) is heavily damped. This framework demonstrates a transferable process for realistic wind power modelling in data-sparse environments, supporting regional energy planning and integration of renewables into developing power systems.
Author(s): Ang'u C, Bloomfield HC, Hirons LC, Woolnough SJ, Brayshaw DJ, Gitau W, Masukwedza GIT, Mutemi J, Ochieng W, Olago D, Oludhe C, Wainwright C
Publication type: Article
Publication status: Published
Journal: Renewable Energy
Year: 2026
Volume: 273
Print publication date: 01/10/2026
Online publication date: 11/06/2026
Acceptance date: 07/06/2026
Date deposited: 22/07/2026
ISSN (print): 0960-1481
ISSN (electronic): 1879-0682
Publisher: Elsevier Ltd
URL: https://doi.org/10.1016/j.renene.2026.126048
DOI: 10.1016/j.renene.2026.126048
Data Access Statement: The ERA5 reanalysis data used in this study are publicly available from the Copernicus Climate Data Store (https://cds.climate.copernicus.eu) under the Copernicus Climate Change Service license. Operational generation and in-situ wind speed measurements from the LTWP, Kipeto and Ngong Hills wind farms were provided by the respective operators under data-sharing agreements and are not publicly available due to confidentiality restrictions. Derived and aggregated datasets supporting the findings of this study are available from the corresponding author upon reasonable request. All data processing and modelling routines were implemented in Python (v3.10) using open-source scientific libraries. Example scripts and documentation supporting this workflow are available from the corresponding author upon reasonable request and will be deposited in a public repository (GitHub) upon publication.
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