Application of NBTree to selection of meteorological variables in wind speed prediction

Mori, H.; Umezawa, Yasushi

doi:10.1109/td-asia.2009.5356831

Cited by 11 publications

(9 citation statements)

References 5 publications

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“…On the other hand, using a genetic algorithm (GA) to optimize a fuzzy inference system (FIS) model as an improvement, the result was between 9.5% and 28.4% over persistence depending on the forecast horizon [17]. And researchers have started to use decision tree techniques in data mining [18]. The results indicate that the predictive power of individual variables is dependent on the seasons.…”

Section: Classical Prediction Modelsmentioning

confidence: 99%

A Survey of Wind Power Ramp Forecasting

Ouyang¹,

Zha²,

Qin³

2013

EPE

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At home and broad, more wind power is being installed in electricity markets, the influence brought by wind power become more important on power system stability, especially the fluctuation, the uncertainty in wind power production and multi-time scale of the wind. In order to forecast ramp events before the power system encountering failure, so that the operator can adopt some limited controlling strategy. This paper introduces the present status of the wind power ramp prediction at home and abroad. And it gives out four kinds of definitions of ramp events, which are used by many scholars, then provides various forecasting error algorithm. In the aspect of prediction models, it comes up with physical models and statistical models, and enumerates various examples of different models. Finally, it prospects the tendency of the model improvement about the wind power ramp events forecasting, which would be significant for ramp research.

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Section: Classical Prediction Modelsmentioning

confidence: 99%

A Survey of Wind Power Ramp Forecasting

Ouyang¹,

Zha²,

Qin³

2013

EPE

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“…Pressure gradients have been used recently to predict [42] wind speed using hidden Markov models (HMMs) to identify cross dependencies between barometric pressure and wind speed. Barometric pressure has also been found to be the most important variable when predicting step ahead wind speed using data mining methods with NPTree as the prediction model [43]. Their motivations are analogous to those in this work in that they are striving to provide a solution to users who were frustrated with the use of black-box-like expressions that do not explain the relationship between input and output.…”

Section: Battery Charging and Wind Prediction Methodologymentioning

confidence: 88%

Using Atmospheric Pressure Tendency to Optimise Battery Charging in Off-Grid Hybrid Wind-Diesel Systems for Telecoms

2013

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Off grid telecom base stations in developing nations are powered by diesel generators. They are typically oversized and run at a fraction of their rated load for most of their operating lifetime. Running generators at partial load is inefficient and, over time, physically damages the engine. A hybrid configuration uses a battery bank, which powers the telecoms' load for a portion of the time. The generator only operates when the battery bank needs to be charged. Adding a wind turbine further reduces the generator run hours and saves fuel. The generator is oblivious to the current wind conditions, which leads to simultaneous generator-wind power production. As the batteries become charged by the generator, the wind turbine controller is forced to dump surplus power as heat through a resistive load. This paper details how the relationship between barometric pressure and wind speed can be used to add intelligence to the battery charger. A Simulink model of the system is developed to test the different battery charging configurations. This paper demonstrates that if the battery charger is aware of upcoming wind conditions, it will provide modest fuel savings and reduce generator run hours in small-scale hybrid energy systems.

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“…The determination of leave for each established rule is conducted by using the Naive Bayes technique. The NBTree algorithm is shown according to the following stages (Mori and Umezawa, 2009):…”

Section: Nbtree Algorithmmentioning

confidence: 99%

The Classification of Cropping Patterns Based on Regional Climate Classification Using Decision Tree Approach

Munandar¹,

Sumiati²

2017

Journal of Computer Science

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Nowadays, agricultural field is experiencing problems related to climate change that result in the changing patterns in cropping season, especially for paddy and coarse grains, pulses roots and Tuber (CGPRT/Palawija) crops. The cropping patterns of rice and CGPRT crops highly depend on the availability of rainfall throughout the year. The changing and shifting of the rainy season result in the changing cropping seasons. It is important to find out the cropping patterns of paddy and CGPRT crops based on monthly rainfall pattern in every area. The Oldeman's method which is usually used in the classification of of cropping patterns of paddy and CGPRT crops is considered less able to determine the cropping patterns because it requires to see the rainfall data throughout the year. This research proposes an alternative solution to determine the cropping pattern of paddy and CGPRT crops based on the pattern of rainfall in the area using decision tree approach. There were three algorithms, namely, J48, RandomTree and REPTree, tested to determine the best algorithm used in the process of the classification of the cropping pattern in the area. The results showed that J48 algorithm has a higher classification accuracy than RandomTree and REPTree for 48%, 42.67% and 38.67%, respectively. Meanwhile, the results of data testing into the decision tree rule indicate that most of the areas in DKI Jakarta are suggested to apply the cropping pattern of 1 paddy cropping and 1 CGRPT cropping (1 PS + 1 PL). While in Banten, there are three cropping patterns that can be applied, they are; 1 paddy cropping and 1 CGPRT cropping (1 PS + 1 PL), 3 short-period paddy croppings or 2 paddy croppings and 1 CGPRT cropping (3 short-period PS or 2 PS + 1 PL) and 2 paddy croppings and 1 CGPRT cropping (2 PS + 1 PL).

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Application of NBTree to selection of meteorological variables in wind speed prediction

Cited by 11 publications

References 5 publications

A Survey of Wind Power Ramp Forecasting

A Survey of Wind Power Ramp Forecasting

Using Atmospheric Pressure Tendency to Optimise Battery Charging in Off-Grid Hybrid Wind-Diesel Systems for Telecoms

The Classification of Cropping Patterns Based on Regional Climate Classification Using Decision Tree Approach

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