An alternative methodology to evaluate sites using climatology criteria for hosting wind, solar, and hybrid plants

Álvarez-Alvarado, José Manuel; Ríos-Moreno, José Gabriel; Ventura-Ramos, Eusebio; Ronquillo-Lomelí, Guillermo; Trejo-Perea, Mario

doi:10.1080/15567036.2020.1772911

Cited by 9 publications

(4 citation statements)

References 50 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For instance, technical criteria such as solar radiation, wind speed, and temperature included in this study were present in all most similar studies indicated in Table 2. However, our system of criteria did not adopt factors that have been infrequently used in the literature, such as wind direction [11], sunny hours [18], [20], [34], air pressure [9], [14], [28], humidity [18], [38], etc. The reason for this discrepancy is the different methods used in designing the criteria systems and stakeholders' opinions.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Content Validity-Based Evaluation Criteria System for Siting Wind-Solar Plants

Sachit,

Shafri,

Abdullah

et al. 2024

Iraqi Journal of Science

View full text Add to dashboard Cite

The spatial assessment criteria system for hybridizing renewable energy sources, such as hybrid solar-wind farms, is critical in selecting ideal installation sites that maximize benefits, reduce costs, protect the environment, and serve the community. However, a systematic approach to designing indicator systems is rarely used in relevant site selection studies. Therefore, the current paper attempts to present an inclusive framework based on content validity to create an effective criteria system for siting wind-solar plants. To this end, the criteria considered in the related literature are captured, and the top 10 frequent indicators are identified. The Delphi technique is used to subject commonly used factors to expert judgments. Other factors are considered according to expert recommendations. In this context, the assessment tool was a combination of questionnaires and interviews with experts from scientific backgrounds that reflect the measurement target. The item-level content validity index (I-CVI) is applied along with the modified Kappa statistic (k*) to analyze expert ratings and suggestions. The results demonstrate the superiority of 9 and 4 commonly used factors and the suggested factors, respectively. The 13 criteria have achieved high agreement among experts at I-CVIs ≥ 0.78 and k*s > 0.76. The conclusion can be drawn that the modified Kappa statistic used in this analysis has a more significant effect on eliminating irrelevant factors. The current methodology and consequences might pave the way for making informed decisions to locate wind and solar farms.

show abstract

Section: Discussionmentioning

confidence: 99%

“…References █ [4], [9], [10], [14], [15], [16], [17], [18], [19], [20], [21], [22], [23], [24], [25],…”

Section: Criteria Selection Based Onmentioning

confidence: 99%

Content Validity-Based Evaluation Criteria System for Siting Wind-Solar Plants

Sachit,

Shafri,

Abdullah

et al. 2024

Iraqi Journal of Science

View full text Add to dashboard Cite

show abstract

“…R-squared, or the coefficient of determination, is the statistical measure that measures in percentage how close the data are to the regression line [42]:…”

Section: Statistical Metrics For Data Validationmentioning

confidence: 99%

Optimized Random Forest for Solar Radiation Prediction Using Sunshine Hours

et al. 2022

View full text Add to dashboard Cite

Knowing exactly how much solar radiation reaches a particular area is helpful when planning solar energy installations. In recent years the use of renewable energies, especially those related to photovoltaic systems, has had an impressive up-tendency. Therefore, mechanisms that allow us to predict solar radiation are essential. This work aims to present results for predicting solar radiation using optimization with the Random Forest (RF) algorithm. Moreover, it compares the obtained results with other machine learning models. The conducted analysis is performed in Queretaro, Mexico, which has both direct solar radiation and suitable weather conditions more than three quarters of the year. The results show an effective improvement when optimizing the hyperparameters of the RF and Adaboost models, with an improvement of 95.98% accuracy compared to conventional methods such as linear regression, with 54.19%, or recurrent networks, with 53.96%, without increasing the computational time and performance requirements to obtain the prediction. The analysis was successfully repeated in two different scenarios for periods in 2020 and 2021 in Juriquilla. The developed method provides robust performance with similar results, confirming the validity and effectiveness of our approach.

show abstract

“…The use of these fossil fuels has been one of the main causes of the greenhouse effect on earth [2][3][4]. Nowadays, the scientific community has set the task of developing new technologies focused on the generation and use of electricity through solar power [5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Hybrid Techniques to Predict Solar Radiation Using Support Vector Machine and Search Optimization Algorithms: A Review

et al. 2021

Self Cite

View full text Add to dashboard Cite

The use of intelligent algorithms for global solar prediction is an ideal tool for research focused on the use of solar energy. Forecasting solar radiation supports different applications focused on the generation and transport of energy in places where there are no meteorological stations. Different solar radiation prediction techniques have been applied in different time horizons, such as neural networks (ANN) or machine learning (ML), with the latter being the most important. The support vector machine (SVM) is a classification method of the ML that is used to predict solar radiation. To obtain a better accuracy of prediction data, search optimization algorithms (SOA) such as genetic algorithms (GA) and the particle swarm optimization algorithm (PSO) were used to optimize the prediction accuracy by searching the model parameters. This article presents a review of different hybrid SVM models with SOA applied to obtain the best parameters to reduce the prediction error of solar radiation using meteorological variables. Research articles from the last 5 years on solar radiation prediction using SVM models and hybrid SMV optimized models with SOA were studied. The results show that SVM with GA presents a better performance than the classical SVM models using the Radial basis kernel function for prediction parameters.

show abstract

An alternative methodology to evaluate sites using climatology criteria for hosting wind, solar, and hybrid plants

Cited by 9 publications

References 50 publications

Content Validity-Based Evaluation Criteria System for Siting Wind-Solar Plants

Content Validity-Based Evaluation Criteria System for Siting Wind-Solar Plants

Optimized Random Forest for Solar Radiation Prediction Using Sunshine Hours

Hybrid Techniques to Predict Solar Radiation Using Support Vector Machine and Search Optimization Algorithms: A Review

Contact Info

Product

Resources

About