Technical analysis of wind energy potentials using a modified Weibull and Raleigh distribution model parameters approach in the Gambia

Ayua, Tyoyima John; Emetere, Moses Eterigho

doi:10.1016/j.heliyon.2023.e20315

Cited by 4 publications

(1 citation statement)

References 22 publications

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“…Various algorithms have been utilized for short-term wind energy potential and power generation prediction [4][5][6][7][8], demonstrating high precision and accuracy. Additionally, methods such as the Weibull and Rayleigh distribution functions [9][10][11][12], nonparametric copula models [13,14], basic statistical prediction methods like the fast filtering algorithm and variational modal decomposition [15], the autoregressive error-compensated hybrid wind power prediction model [16], and multi-model hybrid methods [17][18][19] are commonly employed for wind speed and wind energy prediction. For predicting solar energy, methods based on Geographic Information System (GIS) technology [13] and empirical models utilizing insolation data [20] are prevalent.…”

Section: Introductionmentioning

confidence: 99%

A Review of Solar and Wind Energy Resource Projection Based on the Earth System Model

Chen,

2024

Sustainability

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Many countries around the world are rapidly advancing sustainable development (SD) through the exploitation of clean energy sources such as solar and wind energy, which are becoming the core of the sustainable energy transition. In recent years, the continuous advancement of Earth system models (ESMs) has facilitated numerous studies utilizing them to predict long-term and large-scale meteorological elements, consequently enabling forecasts of wind and solar energy. These forecasts provide critical guidance for formulating national renewable energy policies. Nevertheless, the current literature on ESMs predicting wind and solar energy lacks sufficient integration. Hence, to comprehend the focal points and future research prospects, we conducted this systematic review, employing four academic search tools to comprehensively analyze the relevant literature from the past five years. We summarized the general analytical process and compared the content and conclusions of the literature. The study reveals that future photovoltaic (PV) potential for electricity generation may increase in certain regions but decrease in others, while the global potential for concentrated solar power (CSP) may diminish, influenced by diverse factors and displaying significant regional disparities. In addition, wind resource trends vary in different regions, and forecasts exhibit considerable uncertainty. Therefore, many studies have corrected wind speeds prior to predicting wind energy. Subsequent research endeavors should concentrate on optimizing ESMs, investigating the impacts of technological innovation, and enhancing the prediction and analysis of extreme weather events.

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