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When it comes to search engine optimization, you have keep up with the latest changes to what you optimize your sites. Earlier today I came across a free online tool every website owner and SEO consultant could use to help them build a more search engine friendly site.
The uncertainty and regularity of wind power generation are caused by wind resources’ intermittent and randomness.
Against the background of global economic development, the global wind power industry is growing rapidly. Physical methods are based on numerical weather forecast models (Numerical Weather Prediction—NWP). If you are having issues getting the search engines to find your website, then you need to concentrate on SEO (Search Engine Optimization) in the new year. As a blogger, I am constantly looking for ways to streamline my content creation and find the multimedia needed for each article. Hubspot is offering to help analyze your website for issues and grades your site from zero to hundred over your site’s marketing effectiveness. I left for Houston, Texas Saturday morning and did not  have much time to read a lot of blogs this past week.  All of the articles listed below will help any webmaster, web designer, or blogger build a better website and gain more organic search engine traffic.
I did not get to read to many blogs this week but I did manage to get some reading accomplished. IntroductionThe energy crisis is becoming more and more obvious with the continuous increase in energy consumption, overexploitation of traditional energy and exhaustion of fossil fuels. However, the intermittent nature and randomness of wind power determines the strong volatility of wind power. The wind turbine, wind speed, direction and other information about the hub height of the wind turbine are obtained, combined with physical information, and the power curve of the wind turbine is used to simulate the actual output power [8]. In this article I go over the steps for proper on-page SEO that every website should practice on their site. The requirements for ultrashort-term and short-term wind power forecasting with high prediction accuracy of the model used, have great significance for reducing the phenomenon of abandoned wind power , optimizing the conventional power generation plan, adjusting the maintenance schedule and developing real-time monitoring systems. In order to ease the crisis, countries around the world are increasing the utilization of new energy sources and realizing the sustainable development of energy. With the increasing number of wind farms and the installed capacity, the wind power volatility represents a huge challenge to economic security and increases the difficulty of the operation and management of power network dispatching companies once incorporated into the power grid. This method has no historical data support and is suitable for forecasting a new wind farm, but it has high accuracy requirements and data collection is difficult with high calculation demands and implementation costs.Statistical methods establish a forecast model by finding the relationships between historical data and wind speed or power without considering the physical process of wind speed change.
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Therefore, accurate forecasting of wind power generation is important in electric load forecasting.
As an important category of renewable energy, wind has the characteristics of huge reserves, renewability, wide distribution and non-polluting nature [1].
Accurate prediction of the wind power in advance can relieve the pressure of peaking power systems, which can effectively improve the ability to include wind power in the grid [4].Many experts and scholars both at home and abroad have performed lots of theoretical studies and practical simulations.
This relationship can be expressed in function form, such as Markov chains [4], regression analysis [9,10], exponential smoothing method [11], Kalman filtering method [12], ARMA model [13] and so on.
The echo state network (ESN) is a new recurrent neural network composed of input, hidden layer and output layers. In recent years, as an important aspect of renewable energy utilization and the most mature new energy use technology, the penetration of wind energy in the power grid is increasing rapidly [2].China has abundant wind energy resources. From the time scale point of view, wind power forecasting can be divided into long-term forecasting, short-term and ultrashort-term forecasting. Among them, the ARMA (p, q) model, used as the common statistical model, has high accuracy of analysis and prediction for stationary time series, however, due to the impact of the uncertain natural climate, wind energy has obvious trends, diversity and periodicity, which show non-stationary time series.
This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.AbstractA backtracking search optimization algorithm (BSA) is proposed for the synthesis of concentric circular antenna arrays (CCAAs) with the low sidelobe levels at a fixed beamwidth. It can approximate well the nonlinear system and achieves great results in nonlinear chaotic time series forecasting.


Besides, the government has also attached great importance to the development of wind power industry. Long-term forecasts take “week”, “month”, “year” as the units, and are mainly used for the wind farm design feasibility studies and the maintenance and commissioning of wind turbines. Several numerical examples of CCAA patterns with the single, multiple, and broad nulls imposed at the directions of interference are also given to illustrate the performance and flexibility of the proposed algorithm.
Besides, the ESN is simpler and less computationally demanding than the traditional neural network training, which provides more accurate training results. Different from the European wind power distributed access, most of China’s wind power is connected through a centralized grid connection, so any power fluctuation has a greater impact on the power grid scheduling.
Compared with physical models, statistical models are relatively simple and have strong applicability, but they need a large amount of data support, which is not suitable for the prediction of new wind farms.Learning methods extract the relationship between the input and output using artificial intelligence methods. Aiming at addressing the disadvantages of standard ESN, this paper has made some improvements.
China’s wind power installed capacity doubled for four consecutive years from 2006 to 2009.
The ultrashort-term prediction has been widely used in the optimization of spare capacity [5,6] and the economic load dispatch and control [7]. The numerical results show that the design of CCAA using BSA provides good sidelobe levels with a fixed beamwidth. Combined with the complementary advantages of particle swarm optimization and tabu search, the generalization of ESN is improved. The new installed capacity of wind power in China exceeds 16 GW and the total installed capacity reached 41.83 GW in 2010, which surpassed the United States for the first time, ranking first in the World [3]. Commonly used models are neural network [14,15], wavelet analysis [16], support vector machine [17,18], etc.. To verify the validity and applicability of this method, case studies of multitime scale forecasting of wind power output are carried out to reconstruct the chaotic time series of the actual wind power generation data in a certain region to predict wind power generation.
Short-term and ultrashort-term forecasting which take “hour”, “minute” as the units are particularly important.Depending on the research method, the short-term wind power forecasting method can be roughly divided into physical, statistical and learning methods [3], seen in Figure 2. Learning methods have strong adaptive ability, and the accuracy of the models can be improved by error correction.
The quality of results obtained by the BSA is checked by comparing with that of several metaheuristic algorithms in the literature.
By the end of 2012, the national rankings of the top ten countries according to their total installed capacity of wind power was as shown in Figure 1. BSA is also compared with differential search (DS) and bacterial foraging algorithms (BFA) in terms of iterative performances.1.
Compared with the classical ESN and the conventional Back Propagation (BP) neural network, the results verify the superiority of the proposed method.
IntroductionIt is well-known that the planar arrays have better control over the radiation pattern than the linear arrays.
Probability forecasting methods can give the probability of all the wind power generation in the next moment, providing more comprehensive forecast information, but they require a great quantity of data. Besides other planar arrays such as circular and rectangular arrays, concentric circular antenna array (CCAA) has also become favourable in the radar and wireless communication systems.
The analysis and calculation process is more complex, and some data depends on the prior probability [19].Many studies have judged the chaotic characteristics before prediction [20,21,22]. CCAA is capable of scanning electronically its main beam in the plane of the array without significant degradation of the array pattern. Literatures [20,21] verified that wind power output and wind speed time series have chaotic properties.
CCAA also has a good flexibility in terms of array pattern synthesis both in narrowband and in broadband applications [1–18].For the antenna array synthesis problems, traditional optimization techniques involve particular disadvantages, owing to the nonlinearity.
According to the theory of nonlinear dynamics, chaotic time series in the short term are predictable, indicating that the wind power prediction is conducted with the chaos method. Starting points of optimization process become important to obtain good solutions if the dimension of the solution space is increased. The traditional prediction methods for chaotic time series include the global method and local method.


The global predictive method takes all state points into account and makes them the fitted objects to find out the rules to fit the prediction model.
Additionally, gradient search methods also need continuous and differentiable cost functions [1]. For this reason, in recent years, the metaheuristic techniques [1–3, 19–34] based on the genetic algorithm, ant colony optimization, particle swarm optimization (PSO), differential evolution, tabu search, clonal selection, bees algorithm, harmony search, plant growth simulation, seeker optimization, evolutionary programming (EP), and bacteria foraging optimization (BFO) have become more popular, and they have been used in solving antenna array pattern synthesis problems.
It is difficult to calculate the phase space with high embedding dimensions, which limits its application. In this paper, an alternative metaheuristic technique based on a backtracking search optimization algorithm (BSA) [35] is employed for the synthesis of CCAA with the low sidelobe levels at a fixed beamwidth. Furthermore, several numerical examples of CCAA patterns with the single, multiple, and broad nulls imposed at the directions of interference are also presented. Literature [21] proposes an improved weighted zero order local area prediction method, using the correlation degree of the phase point to find the near phase point, which improves the forecasting precision. Additionally, in order to examine the computational performance of BSA, the convergence curve of BSA is compared with those of differential search (DS) [36] and bacterial foraging algorithm (BFA) [37].The main motivation for the development of BSA is to achieve a simpler and more effective search algorithm [35].
Based on the principle of phase relation, the multivariable time series data are screened to construct the multivariable phase space.
Moreover, the problem solving performance of BSA is not oversensitive to the initial value of this parameter.
Neural networks have been widely used in the prediction of chaotic sequences in the last few years [23,24,25,26,27,28]. BSA has a simple structure that is effective, fast, and capable of solving multimodal problems and that enables it to easily adapt to different numerical optimization problems. Back Propagation (BP) neural network [23,24], radial basis function neural network [25], Elman neural network [26], GMDH type neural network [27] and wavelet neural network [28] have all been used to forecast chaotic sequences and have achieved different prediction effects in different areas.
In [35], BSA was statistically compared with PSO, artificial bee colony algorithm (ABC), covariance matrix adaptation evolution strategy (CMAES), adaptive differential evolution algorithm (JDE), comprehensive learning particle swarm optimizer (CLPSO), and self-adaptive differential evolution algorithm (SADE).
With its good nonlinear forecasting ability, the recurrent neural network has been the main tool for the prediction of chaotic time series, and has been widely recognized by the academic community [29].
75 boundary-constrained and three real-world benchmark problems were employed for the comparisons. However, the mathematical analysis is difficult in practical use, for it has the disadvantages of being time-consuming, with large computational demands and low training efficiency.
In general, the simulations and comparisons in [35] showed that BSA can solve the benchmark problems more successfully than the compared algorithms. As a new recurrent neural network, echo state network (ESN) has greatly improved stability, global optimality and training process complexity phase compared with the traditional neural network [30], attracting wide attention of scholars at home and abroad.
BSA was also used in [38] for circular antenna array design.The rest of the paper is organized as follows. It has been widely used in speech recognition, traffic control [26] and communication forecasting [31], but is rarely used in power system forecasting. In addition, the classical ESN has limited expression ability for nonlinear dynamic systems, and it is difficult to meet the requirement of precision of various forecasting problems in real situations, so its generalization ability is not strong.In view of this, this paper presents a short-term wind power intelligent forecasting model for dealing with the characteristics of nonlinear power generation. Considering the uncertainties of wind power generation, the time series chaotic sequence is judged. The delay time and the embedding dimension are computed to reconstruct phase space; then, to overcome the shortcomings of the ESN network itself, this paper designs and trains an ESN to forecast short-term wind power by combining the two complementary optimization methods of particle swarm optimization and tabu search to improve the generalization ability of the model. Problem FormulationLet us assume that a number of isotropic elements are placed in multiple concentric circular rings which differ in radius as illustrated in Figure 1. There are concentric circular rings in - plane and the th ring has a radius and the number of elements is , where .



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