Short-term wind power prediction based on nutrosophic clustering and GA-ELM
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Abstract Wind farm NWP data has regularity and difference, and making full use of the information contained in NWP data is the key to wind power prediction. A short-term forecasting method for wind power based on nutrosophic clustering and GA-ELM is proposed. Firstly, the wind farm NWP data is divided into several weather types by the Chinese wisdom clustering method, and then the GA-ELM model is established for different weather types. The Gaussian index method is used to classify the forecast data, and then the different types of forecast data are substituted into the corresponding model predictions. Taking a 14MW wind farm in Northeast China as an example, the experiment shows that the nutrosophic clustering method reduces the influence of boundary points and abnormal points on the clustering center. Compared with the traditional method, the method has higher precision and universality.As high-wind energy potential regions are less common now; it is becoming more crucial to generate wind energy in places where the wind velocity is light to moderate. This study uses the WERA model to estimate and compare the performances of 4 commercial wind turbines under low power density wind regimes. Wind turbines of 5 kW-rated capacity, from four prominent manufacturers, were considered in the study. The turbine's velocity power response and the site's Rayleigh probability density of wind velocity were used to model these turbines' performance at four typical sites with different average wind speeds in Kerala namely Thiruvananthapuram, Kollam, Kottayam, Pathanamthitta. The turbine's performances are quantified with the energy production and capacity factor at different locations. It was revealed that the turbine's velocity power response is a crucial factor influencing the system performance. Reduction in the cut-in and rated wind speeds seems to improve the system's output in areas with low wind velocity.
Rayleigh distribution
Small wind turbine
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Unsupervised learning is widely recognized as one of the most important challenges facing machine learning nowa- days. However, in spite of hundreds of papers on the topic being published every year, current theoretical understanding and practical implementations of such tasks, in particular of clustering, is very rudimentary. This note focuses on clustering. I claim that the most signif- icant challenge for clustering is model selection. In contrast with other common computational tasks, for clustering, dif- ferent algorithms often yield drastically different outcomes. Therefore, the choice of a clustering algorithm, and their pa- rameters (like the number of clusters) may play a crucial role in the usefulness of an output clustering solution. However, currently there exists no methodical guidance for clustering tool-selection for a given clustering task. Practitioners pick the algorithms they use without awareness to the implications of their choices and the vast majority of theory of clustering papers focus on providing savings to the resources needed to solve optimization problems that arise from picking some concrete clustering objective. Saving that pale in com- parison to the costs of mismatch between those objectives and the intended use of clustering results. I argue the severity of this problem and describe some recent proposals aiming to address this crucial lacuna.
Conceptual clustering
Implementation
Consensus clustering
Constrained clustering
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This chapter aims to provide forecasters with a brief review of current numerical weather prediction (NWP) techniques used in operational centres to produce daily weather forecasts for Africa and other regions around the globe. It examines the current capability of global operational NWP systems and what are the limitations of current NWP products. The fundamental elements of any NWP forecast are the observations, used to both initialise and evaluate NWP models; the data assimilation process used to derive the best initial state of atmosphere, land and ocean for starting the NWP forecasts; and the numerical models themselves. The chapter discusses these building blocks for a single deterministic NWP forecast. It provides the forecasting techniques for predicting the risk of severe weather, such as ensemble prediction and convective-scale modelling. Finally, the chapter reviews the ability of global NWP prediction systems to capture some of the major seasonal features and weather phenomena.
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Wind speed profile in the atmospheric layer is critical factor for wind turbine capacity factor estimation. Prior to the installation of wind farm, it is essential to estimate expected energy output in order to assess the economic viability of the project. Wind speeds measurements are generally carried out at 10 or 30 m whereas most turbines in commercial use at present have hub heights between 60 and 100 m. Therefore, wind speed measurements are extrapolated to the wind turbine hub height. In this paper, 16 different extrapolation methods were reviewed and compared to determine wind speed, power density and their energy generation estimation capability. Two different error analyses were used to determine the best method. Utilized wind data was gathered from Turkey for 10, 30 and 50 m.
Capacity factor
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Wind speed variations are influenced by both natural climate and human activities. It is important to understand the spatial and temporal distributions of wind speed and to analyze the cause of its changes. In this study, data from 26 meteorological stations in the Jing-Jin-Ji region of North China from 1961 to 2017 are analyzed by using the Mann-Kendall (MK) test. Over the study period, wind speed first decreased by −0.028 m s−1 yr−1 (p < 0.01) in 1961-1991, and then increased by 0.002 m s−1 yr−1 (p < 0.05) in 1992-2017. Wind speed was the highest in spring (2.98 m s−1), followed by winter, summer, and autumn. The largest wind speed changes for 1961-1991 and 1992-2017 occurred in winter (-0.0392 and 0.0065 m s−1 yr−1, respectively); these values represented 36% and 58% of the annual wind speed changes. More than 90.4% of the wind speed was concentrated in the range of 1-5 m s−1, according to the variation in the number of days with wind speed of different grades. Specifically, the decrease in wind speed in 1961-1991 was due to the decrease in days with wind speed of 3-5 m s−1, while the increase in wind speed in 1992-2017 was mainly due to the increase in days with wind speed of 2-4 m s−1. In terms of driving factors, variations in wind speed were closely correlated with temperature and atmospheric pressure, whereas elevation and underlying surface also influenced these changes.
Maximum sustained wind
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Based on wind speed data of 13 meteorological stations in 1958-2012,Mann-Kendall nonparametric test methods was been used to study on wind speed changes in Hexi Corridor.Spatial and temporal characteristics of seasonal and monthly wind speed changes was examined. (1) The maximum wind speed appeared in the higher elevations of study area, such as Wushaoling and Mazongshan station. From east to west mean wind speed increased in Hexi Corridor.For nearly 50 years wind speed had showed decreasing trend. (2)In each season Spring with an maximum mean wind speed was 3.4m/s,the Summer mean wind speed was 2.9 m/s,Autumn mean wind speed was 2.6 m/s,the mean Winter wind speed was 2.8m/s.The seasonal wind speed mainly had decline trend, each station.has different characteristics trends (3) Mean wind speed in each month was greater than 2.5m/s,maximum monthly wind speed appeared in April was 3.5m/s,the minimum wind speed appeared in the September-October was 2.53m/s,the wind speed in March,April and May was greater than the November month,December,January.In addition to Mazongshan and Wushaoling,other station monthly wind speed showed a decreasing trend.Monthly mean wind speed in Jiuquan,Dingxin and Zhangye was slow decreasing trend.Anxi,Yumen wind decreasing trend were more obvious.(4)Wind decreasing trend will have a significant impact on wind energy, wind speed changes and wind energy should be evaluated in the future.
Maximum sustained wind
Prevailing winds
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Heipang in Plateau State is classified under moderate wind speed regime in Nigeria, thus, has high potential for wind electricity generation. Due to high cost, it is difficult to design a wind turbine for a particular site; therefore, the designer of the wind energy project has to choose from the available options in markets, which come in different sizes and speed characteristics. This paper is aimed at evaluating the performance of some selected wind turbines in Heipang wind speed regime. The method used is based on wind speed analysis and computation of the capacity factors of wind turbines expressed as a product of wind turbines’ power output models and probability distribution of wind speed regime of Heipang; and the total annual energy generation of the wind turbines using Wind Energy Resources Analysis (WERA) software. Results showed that Heipang has an annual mean wind speed of 6.3 m/s and its wind speed regime best fitted into Weibull probability distribution function with average Weibull shape and scale parameters of 3.05 and 7.03 m/s respectively at 10 m height. For small (<10kW), medium (10kW-250kW) and large (>250kW) wind turbines classifications; WT4, WT14 and WT25 have the highest capacity factors of 0.61, 0.7 and 0.53 respectively and WT2, WT22, WT27 have the highest total annual energy generation of 0.0054, 1.12 and 4.03 GWh/year respectively. In conclusion, Heipang has high wind speed potential for wind power technology and wind turbines with higher annual energy generation are better options for selections for wind power generation applications
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Increasing the accuracy of wind speed prediction lays solid foundation to the reliability of wind power forecasting. Most traditional correction methods for wind speed prediction establish the mapping relationship between wind speed of the numerical weather prediction (NWP) and the historical measurement data (HMD) at the corresponding time slot, which is free of time-dependent impacts of wind speed time series. In this paper, a multi-step-ahead wind speed prediction correction method is proposed with consideration of the passing effects from wind speed at the previous time slot. To this end, the proposed method employs both NWP and HMD as model inputs and the training labels. First, the probabilistic analysis of the NWP deviation for different wind speed bins is calculated to illustrate the inadequacy of the traditional time-independent mapping strategy. Then, support vector machine (SVM) is utilized as example to implement the proposed mapping strategy and to establish the correction model for all the wind speed bins. One Chinese wind farm in northern part of China is taken as example to validate the proposed method. Three benchmark methods of wind speed prediction are used to compare the performance. The results show that the proposed model has the best performance under different time horizons.
Benchmark (surveying)
Probabilistic Forecasting
Wind Power Forecasting
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