Wind Speed Estimation: Incorporating Seasonal Data Using Markov Chain Models

Karatepe, Selin; Corscadden, Kenneth

doi:10.1155/2013/657437

Cited by 22 publications

(24 citation statements)

References 27 publications

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“…Stochastic methods use Markov chain theory to capture a model of the input driving cycles and to generate a representative driving cycle. This machine learning method has also been used in other fields for prediction, e.g., in estimating the energy consumption in buildings [20] or for estimating wind speeds [21]. Using Markov chains, to improve the results, in [10] and [13], the acceleration is added as an extra dimension for the transition probability matrix (TPM).…”

Section: Existing Driving Cycle Synthesis Methodsmentioning

confidence: 99%

“…To this purpose, a Monte Carlo sampling method [24] based on a Poisson distribution of the probabilities is used. This Markov chain-Monte Carlo (MCMC) technique has been successfully used in earlier driving cycle synthesis methods [10] and in weather forecasting (e.g., wind speed estimation models [21]). Furthermore, the Poisson distribution used in the synthesis process is built from the measured (input) driving cycle.…”

Section: Selecting Synthesized Driving Cycle Samplesmentioning

confidence: 99%

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Synthesis of Realistic Driving Cycles With High Accuracy and Computational Speed, Including Slope Information

Silvas

Hereijgers

Peng

et al. 2016

IEEE Trans. Veh. Technol.

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This paper describes a new method to synthesize driving cycles, where not only the velocity is considered but the road slope information of the real-world measured driving cycle as well. Driven by strict emission regulations and tight fuel targets, hybrid or electric vehicle manufacturers aim to develop new and more energy-and cost-efficient powertrains. To enable and facilitate this development, short, yet realistic, driving cycles need to be synthesized. The developed driving cycle should give a good representation of measured driving cycles in terms of velocity, slope, acceleration, and so on. Current methods use only velocity and acceleration and assume a zero road slope. The heavier the vehicle is, the more important the road slope becomes in powertrain prototyping (as with component sizing or control design); hence, neglecting it leads to unrealistic or limited designs. To include the slope, we extend existing methods and propose an approach based on multidimensional Markov chains. The validation of the synthesized driving cycle is based on a statistical analysis (as the average acceleration or maximum velocity) and a frequency analysis. This new method demonstrates the ability of capturing the measured road slope information in the synthesized driving cycle. Furthermore, results show that the proposed method outperforms current methods in terms of accuracy and speed.Index Terms-Driving cycle compression, hybrid electric vehicle (HEV) design, multidimensional Markov chains, slope and velocity synthesis.

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Section: Existing Driving Cycle Synthesis Methodsmentioning

confidence: 99%

Section: Selecting Synthesized Driving Cycle Samplesmentioning

confidence: 99%

Synthesis of Realistic Driving Cycles With High Accuracy and Computational Speed, Including Slope Information

Silvas

Hereijgers

Peng

et al. 2016

IEEE Trans. Veh. Technol.

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“…for all states i, j and k, we can write a second order transition probability matrix as Similarly, then the CPM C = [C ijk ] can be calculated according to [18] as follows:…”

Section: Wind Speedmentioning

confidence: 99%

“…The MC simulation procedure for synthetic generation of wind speed time-series is accomplished following the following steps [18]:…”

Section: Wind Speedmentioning

confidence: 99%

Joint optimisation of generation and storage in the presence of wind

Liu

Giulietti²,

Chen

2016

IET Renewable Power Generation

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Abstract:We study an independent grid where the penetration of wind energy is high and exploit the joint planning of energy storage and a renewable energy source, as it can potentially result in a more economical and efficient energy system. More specifically, we consider an energy system that consists of a gas-fired plant, and a small wind farm with a capacity for energy storage. We assume that the gas-fired plant has a maximum generation capacity that is no more than the electricity demand. We first propose an optimization model with known wind speed and electricity demand. Then we gradually extend this deterministic model to take into account the stochastic nature of the renewable energy source and electricity demand. Furthermore, we consider the possibility of connecting our system to the National Grid, which we import from or export to when our system has an energy shortage or surplus in meeting the demand. Our results provide helpful insights in planning the joint deployment of generation capacity and energy storage, and show that the system operates more efficiently and economically when it is connected to the National Grid.

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“…Şen et al [32] consider an additional approach and combine the Weibull probability distribution with perturbation theory (which includes the standard deviations and covariance of wind speed at different elevations) to produce and extended power law, again incorporating time variations. A number of authors [35, 38, 41] have used advanced models to improve the characterization of wind speed and wind power estimates including neurofuzzy inference systems [42] and Markov Chain Models [43]. Bilgili et al [44] utilized ANN to predict mean monthly wind speed at a target site using local reference wind tower data with some success but concluded that there is a need to ensure that reference wind towers must have a reasonable correlation factor (0.59).…”

Section: Introductionmentioning

confidence: 99%

The Impact of Variable Wind Shear Coefficients on Risk Reduction of Wind Energy Projects

Corscadden

Thomson

Yoonesi

et al. 2016

International Scholarly Research Notices

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Estimation of wind speed at proposed hub heights is typically achieved using a wind shear exponent or wind shear coefficient (WSC), variation in wind speed as a function of height. The WSC is subject to temporal variation at low and high frequencies, ranging from diurnal and seasonal variations to disturbance caused by weather patterns; however, in many cases, it is assumed that the WSC remains constant. This assumption creates significant error in resource assessment, increasing uncertainty in projects and potentially significantly impacting the ability to control gird connected wind generators. This paper contributes to the body of knowledge relating to the evaluation and assessment of wind speed, with particular emphasis on the development of techniques to improve the accuracy of estimated wind speed above measurement height. It presents an evaluation of the use of a variable wind shear coefficient methodology based on a distribution of wind shear coefficients which have been implemented in real time. The results indicate that a VWSC provides a more accurate estimate of wind at hub height, ranging from 41% to 4% reduction in root mean squared error (RMSE) between predicted and actual wind speeds when using a variable wind shear coefficient at heights ranging from 33% to 100% above the highest actual wind measurement.

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Wind Speed Estimation: Incorporating Seasonal Data Using Markov Chain Models

Cited by 22 publications

References 27 publications

Synthesis of Realistic Driving Cycles With High Accuracy and Computational Speed, Including Slope Information

Synthesis of Realistic Driving Cycles With High Accuracy and Computational Speed, Including Slope Information

Joint optimisation of generation and storage in the presence of wind

The Impact of Variable Wind Shear Coefficients on Risk Reduction of Wind Energy Projects

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