Improvements in wind speed forecasts for wind power prediction purposes using Kalman filtering

Louka, P.; Galanis, George; Siebert, Nils; Kariniotakis, Georges; Katsafados, Petros; Pytharoulis, I.; Kallos, George

doi:10.1016/j.jweia.2008.03.013

Cited by 361 publications

(187 citation statements)

References 9 publications

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“…Similarly, Liu et al (2012) showed that downscaling ratios of 7 : 1, 5 : 1, and 3 : 1 in an WRF model perform better than smaller ratios in terms of rainfall. Resolution jumps (from the forcing data to the external model grid) higher than 5 : 1 and up to 10 : 1 have been successfully employed in various numerical studies (e.g., Galanis et al, 2006;Louka et al, 2008;Katsafados et al, 2011;Stathopoulos et al, 2013). The selection of the above-mentioned data sets was made for homogenization purposes of initial and boundary conditions for all cases.…”

Section: Data Sources and Model Setupmentioning

confidence: 99%

Numerical modeling and analysis of the effect of complex Greek topography on tornadogenesis

Matsangouras

Pytharoulis

2014

Nat. Hazards Earth Syst. Sci.

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Abstract. Tornadoes have been reported in Greece over recent decades in specific sub-geographical areas and have been associated with strong synoptic forcing. While it has been established that meteorological conditions over Greece are affected at various scales by the significant variability of topography, the Ionian Sea to the west and the Aegean Sea to the east, there is still uncertainty regarding topography's importance on tornadic generation and development.The aim of this study is to investigate the role of topography in significant tornadogenesis events that were triggered under strong synoptic scale forcing over Greece. Three tornado events that occurred over the last years in Thebes (Boeotia, 17 November 2007), Vrastema (Chalkidiki, 12 February 2010) and Vlychos (Lefkada, 20 September 2011) were selected for numerical experiments. These events were associated with synoptic scale forcing, while their intensities were T4-T5 (on the TORRO scale), causing significant damage. The simulations were performed using the non-hydrostatic weather research and forecasting model (WRF), initialized by European Centre for Medium-Range Weather Forecasts (ECMWF) gridded analyses, with telescoping nested grids that allow for the representation of atmospheric circulations ranging from the synoptic scale down to the mesoscale. In the experiments, the topography of the inner grid was modified by: (a) 0 % (actual topography) and (b) −100 % (without topography), making an effort to determine whether the occurrence of tornadoes -mainly identified by various severe weather instability indices -could be indicated by modifying topography. The principal instability variables employed consisted of the bulk Richardson number (BRN) shear, the energy helicity index (EHI), the storm-relative environmental helicity (SRH), and the maximum convective available potential energy (MCAPE, for parcels with maximum θ e ). Additionally, a model verification was conducted for every sensitivity experiment accompanied by analysis of the absolute vorticity budget.Numerical simulations revealed that the complex topography constituted an important factor during the

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Section: Data Sources and Model Setupmentioning

confidence: 99%

Numerical modeling and analysis of the effect of complex Greek topography on tornadogenesis

Matsangouras

Pytharoulis

2014

Nat. Hazards Earth Syst. Sci.

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“…Other machine learning techniques that are common for load prediction include Kalman filters [20], recurrent neural networks [49], hidden Markov models [11], parallel classifiers [5] and firefly colony algorithm [8].…”

Section: Other Machine Learning Techniquesmentioning

confidence: 99%

A pattern recognition approach for peak prediction of electrical consumption

Goodwin

Yazidi

2016

ICA

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Abstract.Demand peaks in electrical power consumptions pose serious challenges for energy companies as these are typically unforeseen and require the net to support abnormally high consumption levels. Such peaks can be regulated in smart energy grids with the introduction of relatively simple techniques such as load balancing and smart pricing strategies. This is, however, difficult in practice because it requires prediction of peaks prior to their actual occurrence.While most studies formulate the problem as an estimation problem, we take a radically different approach and formulate it as a classical pattern recognition problem. Further, the paper applies classification methods to solve the problem and applies these with real-life data from a Norwegian smart grid pilot project. Some of the key findings are that the algorithms can accurately detect 80% of energy consumption peaks up to one week ahead of time. Furthermore, we introduce a novel Learning Automata based approaches for selecting the optimal prediction model from a pool of models in an online fashion.

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“…If the finite set { } . Assume the distribution of the data at the high-dimensional manifold is uniform, the corresponding intrinsic correlation dimension is defined by 2 1…”

Section: Intrinsic Dimensionality Estimationmentioning

confidence: 99%

“…The wind speed forecasting has significant impact for security and stability of the large-capacity wind speed connected to the grid. Most forecasting methods such as time series [1], Kalman filtering [2], Artificial neural networks (ANNs) [3], numerical weather prediction [4], fuzzy logic method [5] and support vector machine [6], don't take into account the distribution characteristics of meteorological data. The main disadvantages of the outlined literature include that: they do not provide a complex analysis for the model structure design and selection, such as the model variables selection, model variable order estimation as well as the performance measurement criteria, typically, there are plenty of available variables which can be used for modeling; however, the proper variables with approximately model order are not provided, which results in lacking the ability of generalization and practicability.…”

Section: Introductionmentioning

confidence: 99%

Short-term Wind Speed Forecasting using Model Structure Selection and Manifold Algorithm

Shao

2016

Proceedings of the Fourth International Conference on Information Science and Cloud Computing — PoS(ISCC2015)

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Nonlinear model structure selection (MSS) is an important step in the modeling theory of the nonlinear system. The proper model structure and model parameters estimation can be used to reduce unnecessary computations and improve the model forecast accuracy. This paper mainly investigates the short-term wind speed forecasting (STWSF) by utilizing the comprehensive MSS technique based on real data of the wind speed plant in East China. The MSS and manifold algorithm are respectively used to design proper model structure and reduce the computational complexity to improve the forecasting accuracy and promote the computational efficiency. The experimental evaluation by using support vector regression based on the real data is given to demonstrate the performance of the proposed method. 2015 18-19, December, ISCC

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Improvements in wind speed forecasts for wind power prediction purposes using Kalman filtering

Cited by 361 publications

References 9 publications

Numerical modeling and analysis of the effect of complex Greek topography on tornadogenesis

Numerical modeling and analysis of the effect of complex Greek topography on tornadogenesis

A pattern recognition approach for peak prediction of electrical consumption

Short-term Wind Speed Forecasting using Model Structure Selection and Manifold Algorithm

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