Voltage Profile Enhancement for Remote Areas Through Renewable Energy Resources Integration

Ali, Ziad M.; Abusief, Fatihe; Kamel, Salah

doi:10.1109/mepcon.2018.8635236

Cited by 2 publications

(1 citation statement)

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“…Effective energy production planning is an essential function of energy companies, especially in the area of forecasting. As the grid evolves, planning and procedures must adapt to these changes [28,29]. Therefore, in the context of economic operation, accurate forecasting techniques should be used to predict wind power generation.…”

Section: Introductionmentioning

confidence: 99%

Wind Generation Forecasting Methods and Proliferation of Artificial Neural Network: A Review of Five Years Research Trend

et al. 2020

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To sustain a clean environment by reducing fossil fuels-based energies and increasing the integration of renewable-based energy sources, i.e., wind and solar power, have become the national policy for many countries. The increasing demand for renewable energy sources, such as wind, has created interest in the economic and technical issues related to the integration into the power grids. Having an intermittent nature and wind generation forecasting is a crucial aspect of ensuring the optimum grid control and design in power plants. Accurate forecasting provides essential information to empower grid operators and system designers in generating an optimal wind power plant, and to balance the power supply and demand. In this paper, we present an extensive review of wind forecasting methods and the artificial neural network (ANN) prolific in this regard. The instrument used to measure wind assimilation is analyzed and discussed, accurately, in studies that were published from May 1st, 2014 to May 1st, 2018. The results of the review demonstrate the increased application of ANN into wind power generation forecasting. Considering the component limitation of other systems, the trend of deploying the ANN and its hybrid systems are more attractive than other individual methods. The review further revealed that high forecasting accuracy could be achieved through proper handling and calibration of the wind-forecasting instrument and method.

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Section: Introductionmentioning

confidence: 99%