A novel deep learning intelligent clustered hybrid models for wind speed and power forecasting

Aly, Hamed H.

doi:10.1016/j.energy.2020.118773

Cited by 75 publications

(16 citation statements)

References 24 publications

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“…Wind sensor ▪ Measurement of wind speed and direction to predict the potential wind power in a plant [279] Power meter ▪ Measuring real power and calculation of the estimated wind power [280] Weather sensor ▪ Measuring temperature and pressure to enhance prediction model [281] Turbine sensor ▪ To acknowledge blade pitch angle and turbulence intensity in a wind turbine [282] Anemometer ▪ A measurement instrument to gather wind information [283] Software and control Input data evaluation ▪ Classification of data obtained from measurement sensors [133] Weather prediction algorithm ▪ To predict current and future weather conditions (i.e., wind speed, temperature, pressure, air density, etc.) [134] Output power estimation ▪ To calculate the possible power generation amount of the wind turbine [135] Communication protocols TCP/IP ▪ To transfer the measured and calculated data through internet [140] WAP ▪ Performing wireless communication to provide achievement to the data from a personal computer [141] SMTP ▪ Enabling the transfer of the measured data via an electronic mail [142] HTTP ▪ An application layer in TCP/IP protocol for the data transfer [143] standards.…”

Section: Measurementmentioning

confidence: 99%

A Review on Wind Power Forecasting Regarding Impacts on the System Operation, Technical Challenges, and Applications

et al. 2022

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Wind energy generation is a fast developing efficient renewable source widely used for modern power generation. [1] Countries and energy companies have increased their interest in electricity generation from wind energy in the last decades, thanks to its natural, cost-free, and environmentally friendly features. [2] Wind power plants can generate electrical energy at any time of the day and are distributed energy sources for systems that need permanent energy. [3] Nonetheless, due to the disordered nature of the earth's atmosphere, there is a major uncertainty in the electrical power produced by the wind, which causes complexities in the utilization and management of wind energy sources. [4] The changes in weather conditions depending on time and location also create uncertainty in managing the supply-demand balance controlled by the grid operators. [5] In cases where the electrical energy produced by wind power plants is insufficient, auxiliary energy sources provide operational support to the electric grid. [6] Ensuring the supply-demand balance in the electricity grid is also an important condition for the electrical power quality and energy supply in the transmission system, and wind power plant operators care about the follow-up of wind power in terms of economy, operation, and planning. [7] For this, the presence of wind power forecasting (WPF) is an essential factor in the optimization of wind power plants. [8] For consumers to use electrical energy uninterruptedly, efficiently, and economically, wind power plant operators need to estimate the generated electrical energy smoothly. [9] The smooth estimation of electrical power generated by wind turbines increases the operational efficiency for system maintenance, planning, investment, and energy traders. [10,11] It is a precious procedure for electrical grids to provide a respectable commercial yield on the electrical energy market. [12] The capability of advanced WPF provides the contributions like balancing the power system and mitigation of penalties, scheduling the maintenance of wind power plants, investment regarding site location, and technoeconomic and socioeconomic benefits. [13,14] In this way, it is possible to reduce the energy cost and obtain high revenue in the energy market as a result of the contributions obtained for wind power generation systems. [15]

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Section: Measurementmentioning

confidence: 99%

A Review on Wind Power Forecasting Regarding Impacts on the System Operation, Technical Challenges, and Applications

et al. 2022

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“…In the growing developing field of wave energy, wind energy has been expanded and has become a more mature form of renewable energy application. The application of wind speed prediction on smart power systems plays a significant role since the wind power integration could be impact by the prediction accuracy [2]. Similar to wave energy, wind energy fluctuates and is unstable.…”

Section: Introductionmentioning

confidence: 99%

Review on Deep Learning Research and Applications in Wind and Wave Energy

2022

Energies

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Wind energy and wave energy are considered to have enormous potential as renewable energy sources in the energy system to make great contributions in transitioning from fossil fuel to renewable energy. However, the uncertain, erratic, and complicated scenarios, as well as the tremendous amount of information and corresponding parameters, associated with wind and wave energy harvesting are difficult to handle. In the field of big data handing and mining, artificial intelligence plays a critical and efficient role in energy system transition, harvesting and related applications. The derivative method of deep learning and its surrounding prolongation structures are expanding more maturely in many fields of applications in the last decade. Even though both wind and wave energy have the characteristics of instability, more and more applications have implemented using these two renewable energy sources with the support of deep learning methods. This paper systematically reviews and summarizes the different models, methods and applications where the deep learning method has been applied in wind and wave energy. The accuracy and effectiveness of different methods on a similar application were compared. This paper concludes that applications supported by deep learning have enormous potential in terms of energy optimization, harvesting, management, forecasting, behavior exploration and identification.

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“…Aly et al [36] developed a model to forecast wind power and speed using various combinations, including a wavelet neural network (WNN), artificial neural network (ANN), Fourier series (FS) and recurrent Kalman filter (RKF). Bo et al [37] proposed nonparametric kernel density estimation (NPKDE), least square support vector machine (LSSVM), and whale optimization approaches for predicting short-term wind power.…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning-Based Gradient Boosting Regression Approach for Wind Power Production Forecasting: A Step towards Smart Grid Environments

et al. 2021

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In the last few years, several countries have accomplished their determined renewable energy targets to achieve their future energy requirements with the foremost aim to encourage sustainable growth with reduced emissions, mainly through the implementation of wind and solar energy. In the present study, we propose and compare five optimized robust regression machine learning methods, namely, random forest, gradient boosting machine (GBM), k-nearest neighbor (kNN), decision-tree, and extra tree regression, which are applied to improve the forecasting accuracy of short-term wind energy generation in the Turkish wind farms, situated in the west of Turkey, on the basis of a historic data of the wind speed and direction. Polar diagrams are plotted and the impacts of input variables such as the wind speed and direction on the wind energy generation are examined. Scatter curves depicting relationships between the wind speed and the produced turbine power are plotted for all of the methods and the predicted average wind power is compared with the real average power from the turbine with the help of the plotted error curves. The results demonstrate the superior forecasting performance of the algorithm incorporating gradient boosting machine regression.

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A novel deep learning intelligent clustered hybrid models for wind speed and power forecasting

Cited by 75 publications

References 24 publications

A Review on Wind Power Forecasting Regarding Impacts on the System Operation, Technical Challenges, and Applications

A Review on Wind Power Forecasting Regarding Impacts on the System Operation, Technical Challenges, and Applications

Review on Deep Learning Research and Applications in Wind and Wave Energy

A Machine Learning-Based Gradient Boosting Regression Approach for Wind Power Production Forecasting: A Step towards Smart Grid Environments

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