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Correct placement of turbines in a wind farm is a critical issue in wind farm design optimisation. While traditional "trial and error"-based approaches suffice for small layouts, automated approaches are required for larger wind farms with turbines numbering in the hundreds. In this paper we propose an evolutionary strategy with a novel mutation operator for identifying wind farm layouts that minimise expected velocity deficit due to wake effects. The mutation operator is based on constructing a predictive model of velocity deficits across a layout so that mutations are inherently biased towards better layouts. This makes the operator informed rather than randomised. We perform a comprehensive evaluation of our approach on five challenging simulated scenarios using a simulation approach acceptable to industry [1]. We then compare our algorithm against two baseline approaches including the Turbine Displacement Algorithm [2]. Our results indicate that our informed mutation approach works effectively, with our approach identifying layouts with the lowest aggregate velocity deficits on all five test scenarios.

In this paper, we are interested in the statistical modeling and forecasting of the daily maximum ozone concentration in three monitoring stations from Tunisia. A large number of explicative variables has been considered in our study. We have focused our attention on the problem of variable selection in order to improve the forecasting performance. To achieve our goal, we have used Support Vector Regression (SVR) and Random Forests (RF). The main novelties of this paper are: the variety and originality of the approaches for variable selection in regression, and the audaciousness to deal with a sticky situation characterized by a relatively big pannier of explicative variables compared to the number of observations. The experimental results demonstrate that Random Forests outperform Support Vector Regression in variable ranking and selection. Finally, it was shown that the forecasting accuracy is at least preserved, for the three stations, when using only the selected variables.

Content-Based Image Retrieval (CBIR) from a large database is becoming a necessity for many applications such as medical imaging, Geographic Information Systems (GIS), space search and many others. However, the process of retrieving relevant images is usually preceded by extracting some discriminating features that can best describe the database images. Therefore, the retrieval process is mainly dependent on comparing the captured features which depict the most important characteristics of images instead of comparing the whole images. In this paper, we propose a CBIR method by extracting both color and texture feature vectors using the Discrete Wavelet Transform (DWT) and the Self Organizing Map (SOM) artificial neural networks. At query time texture vectors are compared using a similarity measure which is the Euclidean distance and the most similar image is retrieved. In addition, other relevant images are also retrieved using the neighborhood of the most similar image from the clustered data set via SOM. The proposed method demonstrated promising retrieval results on the Wang Database compared to the existing methods in literature.

Abstract:The visual impact of wind farm layouts has seen little consideration in the literature on the wind farm layout optimisation problem to date. Most existing algorithms focus on optimising layouts for power or the cost of energy alone. In this paper, we consider the geometry of wind farm layouts and whether it is possible to bi-optimise a layout for both energy efficiency and the degree of visual impact that the layout exhibits. We develop a novel optimisation approach for solving the problem which measures mathematically the degree of visual impact of a layout. The approach draws inspiration from the field of architecture. To evaluate our ideas, we demonstrate them on three benchmark problems for the wind farm layout optimisation problem in conjunction with two recently-published stochastic local search algorithms. Optimal patterned layouts are shown to be very close in terms of energy efficiency to optimal non-patterned layouts.

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Abstract. The BlockCopy stochastic local search algorithm is a stateof-the-art optimiser for the Wind Farm Layout Optimisation problem. Unlike many other metaheuristics-based optimisers, BlockCopy requires the specification of only one key parameter, namely a block size. In this paper, we investigate the effect on different block sizes on the optimisation results. Using standard benchmarks for the Wind Farm Layout Optimisation problem, we show that smaller fixed block sizes (relative to overall layout size) produce better optimised layouts than larger fixed block sizes. More interestingly, we also show that randomising the block size parameter results in optimisation performance at the same or a better level than that produced by the best algorithm with a fixed block size. Effectively, this means that the user can ignore the need to tune the block size parameter and simply randomise it instead. Such a strategy results in what is effectively a parameterless, but none-the-less effective, optimisation algorithm for the Wind Farm Layout Optimisation problem.

A novel mutation operator for the wind farm layout optimisation problem is proposed and tested. When a wind farm layout is simulated, statistics such as an individual turbine's wake free ratio can be computed. These statistics are in addition to the global measure being optimised, for example the overall cost of energy extraction of the farm. We present algorithms that first of all build a predictive model of the wake free ratio across an entire wind farm. This model is then used inside a mutation operator to perturb turbines towards positions of high predicted wake free ratio. We evaluate our approach by comparing a 1+1 Evolutionary Strategy using this new mutation operator vs. the same algorithm with a more standard random mutation operator, and show that our new operator leads to the discovery of wind farm layouts having a statistically significantly lower cost of energy extraction.