A novel heuristic method for wind farm power prediction: A case study

Ghadi, Mojtaba Jabbari; Gilani, S. Hakimi; Afrakhte, Hossein; Baghramian, Alfred

doi:10.1016/j.ijepes.2014.07.008

Cited by 34 publications

(14 citation statements)

References 29 publications

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“…Kyoto Protocol with the aim of achieving the "stabilization of greenhouse gas concentrations in the atmosphere at a level that would prevent dangerous anthropogenic interference with the climate system" is an environmental convention [43]. By adopting this protocol, due to environmental benefits of renewable resources particularly wind power generation, utilization of these types of energy has acquired noticeable consideration in eye-catching number of countries, recently.…”

Section: Introductionmentioning

confidence: 99%

“…By adopting this protocol, due to environmental benefits of renewable resources particularly wind power generation, utilization of these types of energy has acquired noticeable consideration in eye-catching number of countries, recently. In comparison with the environmental damages of traditional sources of energy, the ecological influences of wind power is relatively negligible [43].…”

Section: Introductionmentioning

confidence: 99%

“…However, despite remarkable environmental benefits, the continuous and chaotic fluctuations of wind speed make the output power of wind farms completely stochastic and different from those of conventional units. However, instant electrical generation must be equal to the grid consumption to fulfill the network stability requirements and the spare capacity may be reduced in the power system [43].…”

Section: Introductionmentioning

confidence: 99%

“…In order to increase the economic efficiency and acceptability of the wind power and to permit a diminishing in the punishment of an instantaneous spot market coming from extra estimation or underrating of the production, the exact prediction of wind power as well as wind velocity is required. Surely, a reliable prediction system can help distribution system operators and power marketers to make a better decision on critical situation [43].…”

Section: Introductionmentioning

confidence: 99%

“…Statistical forecasting techniques are only based on one or more models. These approaches found coordination between predict and historical numerical quantity of meteorological parameters and wind power measurements associated with the power quantities coming from historical data [43].…”

Section: Introductionmentioning

confidence: 99%

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Short-Term Wind Power Forecasting Using Artificial Neural Networks for Resource Scheduling in Microgrids

Tesfaye¹,

Zhang²,

Zheng³

et al. 2016

IJSEA

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Use of wind power as one of renewable resources of energy has been growing quickly all over the world. Wind power generation is significantly vacillating due to the wind speed alteration. Therefore, assessment of the output power of this type of generators is always associated with some uncertainties. A precise wind power prediction aims to support the operation of large power systems or microgrids in the scope of the intraday resources scheduling model, namely with a time horizon of 5-10 minutes, and this can efficiently uphold transmission and distribution system operators to improve the power network control and management. A Shortterm ANN based wind power prediction model, for day-ahead energy management and scheduling in microgrids, is developed utilizing a real database of 12 months with 10 minutes time interval data from measured information of online supervisory control and data acquisition (SCADA) of Goldwind microgrid wind turbine system (Beijing, China) as well as Numerical Weather Prediction (NWP). The ANN predicted wind power has been compared with the actual power of Goldwind wind turbine system. The predicted wind power has shown acceptable agreement with the actual power output that has been recorded by the SCADA which confirms the robustness and accuracy of the ANN wind power forecasting model developed in this research paper.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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