The uncertainties involved in measuring national solar photovoltaic electricity generation

Huxley, O.T.; Taylor, Jamie; Everard, Aldous; Briggs, Justin A.; Tilley, K.; Harwood, J. A. C.; Buckley, Alastair

doi:10.1016/j.rser.2021.112000

Cited by 13 publications

(5 citation statements)

References 25 publications

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“…The OLS model has the highest global error of 36.1%, and despite having a higher test R 2 score compared to the polynomial and neural network models, its validation R 2 score is −1.57, which shows that its predictions are worse than a constant function that predicts the mean of PV capacity additions, deeming it unsuitable to model capacity additions. Considering that the error in measuring national PV capacity is at least 5% [65], the combined model's prediction error of 9.7% provides a reliable estimate of the actual capacity. Table A5 shows the importance of each feature in the combined model, the correlation between the features and the PV capacity additions, and how much variation each feature explains in the PV capacity additions.…”

Section: Resultsmentioning

confidence: 99%

Global Benchmarking and Modelling of Installed Solar Photovoltaic Capacity by Country

Alghanem,

Buckley

2024

Energies

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Setting solar photovoltaic capacity targets and implementing supportive policies is a widespread strategy among nations aiming to achieve decarbonisation goals. However, policy implementation without a thorough understanding of the intricate relationship between social, economic, and land-use factors and solar photovoltaic deployment can lead to unintended consequences, including over- or underdeployment and failure to reach targets. To address this challenge, an investigation was conducted into the relationship between 36 factors and solar photovoltaic deployment across 143 countries from 2001 to 2020 using correlation analysis and principal component analysis. From these factors, five key variables were identified that collectively explain 79% of the year-to-year variation in photovoltaic capacity. Using these variables, a neural network model was constructed, enabling the estimation of capacity additions by country with an error of less than 10%. Additionally, a solar photovoltaic deployment index was developed, serving as a benchmark for comparing a country’s actual historical photovoltaic deployment to similar nations. Furthermore, the model’s utility in evaluating the impact of solar photovoltaic policies was explored. Through three distinct use cases—forecasting solar photovoltaic capacity additions, developing a solar photovoltaic deployment index, and assessing the impact of solar photovoltaic policies—the model emerges as a potentially powerful tool for governments and policy makers to assess solar photovoltaic deployment effectively and formulate strategies to promote sustainable solar energy growth.

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Section: Resultsmentioning

confidence: 99%

Global Benchmarking and Modelling of Installed Solar Photovoltaic Capacity by Country

Alghanem,

Buckley

2024

Energies

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“…The numerical weather projections serve as a source of irradiation forecasts for national and regional PV forecasts. For solar energy applications, the standard deviation, which roughly equates to an interval of 68.27% of occurrences around the mean value, is expected to be used to convey uncertainty [28]. The amount of solar radiation and component accessibility that a PV panel can generate at a given location determines its output power.…”

Section: Res and Ev Load Uncertaintymentioning

confidence: 99%

A probabilistic approach on uncertainty modelling and their effect on the optimal operation of charging stations

K. K.,

N. S.,

Jadoun

2024

IET Generation Trans & Dist

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Uncertainty analysis deals with the fluctuations and unpredictability of the electrical power generated from renewable resources (RRs), such as solar PV and wind energy systems. This paper gives an insight into various techniques used for the uncertainty analysis and a probabilistic Monte Carlo Simulation is applied for modelling the uncertainties concerned with RRs and electric vehicle (EV) load in the MATLAB platform. The uncertainty associated with the price sensitivity of EV charging and the state of charge of EVs is taken as a prime factor for analysis in the present work. Despite the fluctuations and unpredictability of electricity generation and consumption, the considered system ensures that the total amount of electricity supplied by solar PV, wind and grid matches the total amount of electricity demanded by EV load. Rao‐1, Rao‐2 and Rao‐3 algorithms are applied in this work to optimize the operation cost of charging stations under uncertain conditions and without any uncertainties. The results obtained without uncertainties by Rao algorithms are compared with the existing particle swarm optimisation method. In the presence of uncertainties, Rao‐1 and Rao‐2 algorithms are compared with Rao‐3 and it is found that the Rao‐3 algorithm performed better.

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“…ANNs possess the ability to learn intricate patterns and relationships within vast datasets, allowing them to capture the nonlinear and time-varying behaviors inherent in solar energy systems [8][9][10][11]. However, the large dependency of PV power on weather conditions brings a major challenge of uncertainty to system operation and efficiency [12]. To address this dilemma, an accurate and reliable forecast of PV power production is essential to stabilize and secure the PV electricity supply.…”

Section: Introductionmentioning

confidence: 99%

Time-Dependent Photovoltaic Performance Assessment on a Global Scale Using Artificial Neural Networks

Matera,

Longo,

Leva

2024

Preprint

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The uncertainties involved in measuring national solar photovoltaic electricity generation

Cited by 13 publications

References 25 publications

Global Benchmarking and Modelling of Installed Solar Photovoltaic Capacity by Country

Global Benchmarking and Modelling of Installed Solar Photovoltaic Capacity by Country

A probabilistic approach on uncertainty modelling and their effect on the optimal operation of charging stations

Time-Dependent Photovoltaic Performance Assessment on a Global Scale Using Artificial Neural Networks

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