A versatile multi-method ensemble for wind farm layout optimization

Pérez-Aracil, Jorge; Casillas-Pérez, David; Jiménez-Fernández, Silvia; Prieto-Godino, Luis; Salcedo‐Sanz, Sancho

doi:10.1016/j.jweia.2022.104991

Cited by 12 publications

(4 citation statements)

References 99 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, the sequential search strategies are characterized by a major drawback; the order of parameter entry (or deletion) affects the selected model [32]. To overcome this issue, the use of EA-based approaches [33], such as genetic algorithms (GAs) [34], the BDE algorithm [21], particle swarm optimization (PSO) [35], the coral reef optimization (CRO) algorithm [36], or a combination of these techniques, have been shown to be effective even if they are computationally more demanding. In practice, the main advantages of EAs are (i) their fast convergence to a near-global optimum, (ii) their superior global searching capability in complicated search spaces, and (iii) their applicability even when gradient information is not readily achievable.…”

Section: The Motivation For Feature Selectionmentioning

confidence: 99%

Feature Selection by Binary Differential Evolution for Predicting the Energy Production of a Wind Plant

Al-Dahidi,

Baraldi,

Fresc

et al. 2024

Energies

View full text Add to dashboard Cite

We propose a method for selecting the optimal set of weather features for wind energy prediction. This problem is tackled by developing a wrapper approach that employs binary differential evolution to search for the best feature subset, and an ensemble of artificial neural networks to predict the energy production from a wind plant. The main novelties of the approach are the use of features provided by different weather forecast providers and the use of an ensemble composed of a reduced number of models for the wrapper search. Its effectiveness is verified using weather and energy production data collected from a 34 MW real wind plant. The model is built using the selected optimal subset of weather features and allows for (i) a 1% reduction in the mean absolute error compared with a model that considers all available features and a 4.4% reduction compared with the model currently employed by the plant owners, and (ii) a reduction in the number of selected features by 85% and 50%, respectively. Reducing the number of features boosts the prediction accuracy. The implication of this finding is significant as it allows plant owners to create profitable offers in the energy market and efficiently manage their power unit commitment, maintenance scheduling, and energy storage optimization.

show abstract

Section: The Motivation For Feature Selectionmentioning

confidence: 99%

Feature Selection by Binary Differential Evolution for Predicting the Energy Production of a Wind Plant

Al-Dahidi,

Baraldi,

Fresc

et al. 2024

Energies

View full text Add to dashboard Cite

show abstract

“…Recently, a multi-method ensemble known as coral reef optimization with substrate layers (CRO-SL) was proposed [27][28][29] and successfully applied to very different optimization problems in science and engineering, such as energy grid and microgrid design [30][31][32], mechanical and structural design [33][34][35][36][37], and electrical engineering [38][39][40]. The CRO-SL is a low-level, evolutionary-based multi-method ensemble which combines different types of search operators within a single population (reef) by dividing it in different zones (substrates), in which a different operator is applied.…”

Section: Contribution and Structurementioning

confidence: 99%

“…The wind characteristics considered for this challenge are specified in [55] and also described in [32]. Briefly, the wind distribution frequency and wind speed are the same for all wind-farm scenarios.…”

Section: Comparison In Benchmark Functionsmentioning

confidence: 99%

New Probabilistic, Dynamic Multi-Method Ensembles for Optimization Based on the CRO-SL

et al. 2023

Self Cite

View full text Add to dashboard Cite

In this paper, new probabilistic and dynamic (adaptive) strategies for creating multi-method ensembles based on the coral reef optimization with substrate layers (CRO-SL) algorithm are proposed. CRO-SL is an evolutionary-based ensemble approach that is able to combine different search procedures for a single population. In this work, two different probabilistic strategies to improve the algorithm are analyzed. First, the probabilistic CRO-SL (PCRO-SL) is presented, which substitutes the substrates in the CRO-SL population with tags associated with each individual. Each tag represents a different operator which will modify the individual in the reproduction phase. In each generation of the algorithm, the tags are randomly assigned to the individuals with similar probabilities, obtaining this way an ensemble that sees more intense changes with the application of different operators to a given individual than CRO-SL. Second, the dynamic probabilistic CRO-SL (DPCRO-SL) is presented, in which the probability of tag assignment is modified during the evolution of the algorithm, depending on the quality of the solutions generated in each substrate. Thus, the best substrates in the search process will be assigned higher probabilities than those which showed worse performance during the search. The performances of the proposed probabilistic and dynamic ensembles were tested for different optimization problems, including benchmark functions and a real application of wind-turbine-layout optimization, comparing the results obtained with those of existing algorithms in the literature.

show abstract

“…Despite its incredibly high computational cost, the authors of [30] have offered a novel method of building the wind farm optimally using Computational Fluid Dynamics (CFD) model software. The Coral Reefs Optimization algorithm with Substrate Layer (CRO-SL), which can combine various search algorithms in a single population, is employed in a fairly recent work [31], which proposes an ensemble strategy for tackling the WFLO issue. In [32], a multi-objective Elitist Teaching-Learning Based Optimization (ETLBO) algorithm is proposed for the WFLO problem where the objective is to produce maximum power while minimizing the cost.…”

Section: Introductionmentioning

confidence: 99%

Wind Farm Layout Optimization/Expansion with Real Wind Turbines Using a Multi-Objective EA Based on an Enhanced Inverted Generational Distance Metric Combined with the Two-Archive Algorithm 2

et al. 2023

View full text Add to dashboard Cite

In this paper, the Wind Farm Layout Optimization/Expansion (WFLO/E) problem is formulated in a multi-objective optimization way with specific constraints. Furthermore, a new approach is proposed and tested for the variable reduction technique in the WFLO/E problem. To solve this problem, a new method based on the hybridization of the Multi-Objective Evolutionary Algorithm Based on An Enhanced Inverted Generational Distance Metric (MOEA/IGD-NS) and the Two-Archive Algorithm 2 (Two Arch2) is developed. This approach is named (MOEA/IGD-NS/TA2). The performance of the proposed approach is tested against six case studies. For each case study, a set of solutions represented by the Pareto Front (PF) is obtained and analyzed. It can be concluded from the obtained results that the designer/planner has the freedom to select several configurations based on their experience and economic and technical constraints.

show abstract

A versatile multi-method ensemble for wind farm layout optimization

Cited by 12 publications

References 99 publications

Feature Selection by Binary Differential Evolution for Predicting the Energy Production of a Wind Plant

Feature Selection by Binary Differential Evolution for Predicting the Energy Production of a Wind Plant

New Probabilistic, Dynamic Multi-Method Ensembles for Optimization Based on the CRO-SL

Wind Farm Layout Optimization/Expansion with Real Wind Turbines Using a Multi-Objective EA Based on an Enhanced Inverted Generational Distance Metric Combined with the Two-Archive Algorithm 2

Contact Info

Product

Resources

About