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Accurate wind speed modeling is beneficial for the design of wind energy conversion systems. Models of wind speed are used to assess the adequacy and dependability of a power supply. However, precise wind speed modeling is challenging due to the sporadic availability of wind speed. In this note, we propose a wind speed model with an autoregressive (AR) structure. A hybrid model is developed under linear and nonlinear parts based on a quasi‐linear autoregressive exogenous neural network (Q‐ARX‐NN). The model's structure is composed of a regression vector and its coefficients. The coefficients are divided into linear and nonlinear coefficients. A set of linear coefficients is identified under the algorithm of least square error (LSE), and a set of nonlinear coefficients is modeled by using a neural network to refine the residual error of the nonlinear part. Some artificial neural network (ANN) models can be set as nonlinear part sub‐models to sharpen the model's accuracy. The proposed model is tested for wind speed modeling to estimate wind energy production. Various nonlinear parts of the sub‐model are tested, such as neural networks, radial basis function networks, and ANN networks. Moreover, we evaluate the effects of the order of the model by varying hidden and output nodes, which can be summarized as the number of coefficients of the regression vector. Using specific wind turbine performance data, prediction models estimate production power. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Renewable energy generation is increasingly employed nowadays for multitudes of reasons such as global warming, depletion of conventional sources of energy and emission constraints. Even though the wind generators constitute a potential source of energy, the uncertainties associated with them make the operation complex. As a consequence, the successful operation and planning of the present distributed generation dominated power systems requires exact estimate of wind power. Numerous wind power estimation techniques based on Machine Learning were available. This work attempts to compare the wind power estimation efficiency of a few machine learning approaches. At first, the performance of a Feed Forward Neural Network with different activation functions is considered. Next, Support Vector Regression Machine with different kernels is utilized for estimating the wind power. Then, deep Learning networks such as Long Short-Term Memory network, Convolutional Neural Network and Recurrent Neural Network are employed for assessing the future wind power and their ability is analyzed. Finally, a comparative chart is prepared to evaluate the efficacy and usefulness of the different machine learning techniques employed for estimating wind power.

This article presents 24-h wind speed forecasting for the city of La Serena in Chile and a methodology to explore forecasting effects on the production of wind turbine power. To that end, we used meteorological data from a weather station located in the southern zone of the hyper-arid Atacama Desert. In this area, energy resources are economically and environmentally important, and wind speed forecasting plays a vital role in the management and marketing processes of wind potential via wind farms. To contribute to the development of this energy, we propose carrying out the short-term prediction of 12 and 24 h ahead (identified as Ws(t + 12) and Ws(t + 24), respectively) using an artificial neural network with backpropagation approach. Hourly time series of wind speed, temperature, and relative humidity (from 2003 to 2006) were considered to characterize the artificial neural network in the training phase, while we used data from the year 2007 to check the efficiency of our prediction. For artificial neural network Ws(t + 12) and Ws(t + 24) models, we obtained similar performance of wind speed prediction with root mean square error of around 0.7 m s−1 and with maximum and minimum residuals of +4 and ‒4 m s−1, respectively. Based on the results, we gain a reliable tool to characterize wind speed properties in the range of 1 day within 20% of uncertainty. Moreover, this tool becomes useful to study the effects of our artificial neural network Ws(t + 12) and Ws(t + 24) models on the generation of wind energy from a wind power turbine parametrization. [ABSTRACT FROM AUTHOR]

The wind energy potential of a specified region can be estimated using the distribution of wind speed. Thus, finding appropriate wind speed distribution is essential. Weibull distribution (WD) is the most popular distribution in wind energy literature. Besides WD, various statistical distributions have been widely-used as reference distributions to characterize wind speed. However, it is observed that these distributions may not model all wind speed data observed in nature. Thus, many studies on different distributions are still being conducted to find better distributional models for use in wind energy estimates. In this study, we introduce for the first time the Extended Generalized Lindley distribution (EGLD) as an alternative wind speed distribution. EGLD is flexible enough to accommodate different shapes of wind speed data and includes other forms of Lindley distribution as special cases. In addition, we test the performance of EGLD on real wind speed data measured at various regions of Turkey. The results of the analyses indicate that EGLD is suitable for most of the examined wind speed data cases compared to the well-known WD, according to goodness-of-fit tests. Therefore, EGLD can be used as an alternative distribution for the assessment of wind energy potential. [ABSTRACT FROM AUTHOR]

Despite the rapid and extensive growth of wind power, a challenge still faced by wind power operators is the intermittency of wind speed and thus wind power. On different time scales, the stochastic nature of wind speed has impacts on the energy yield from wind turbines. While long term estimates of wind power are ideal for energy mix planning and policy, knowledge of variations in very short-term output is essential for energy grid management, turbine control system and wind power optimization. This study analyzed energy yield from an 800kW operational wind turbine using 15-seconds time-series data averaged into 1, 5, and 15 minutes time steps for cumulative periods of 1 hour, 4 hours and 12 hours respectively. In the three case scenarios explored, theoretical output under-estimated turbine yield within a range of 5% - 48%. The lowest errors recorded were 5%, 14% and - 0.34% for the time series considered although the estimation errors were larger for the higher time intervals generally. Cases of 0 kW turbine output despite wind speed value above the cut-in speed (i.e u > 3m/s) suggest that wind direction may impact turbine yield. The effect of turbine blade momentum was also more evident in some time-steps where yield was sustained despite fall in wind speed to below 3m/s. The results show that the 15 minutes time steps series is more beneficial in short-term energy yield estimations and show the usefulness of short-term yield estimations for wind power operators, grid management and wind power optimization. [ABSTRACT FROM AUTHOR]

Abstract: In the scientific literature concerning renewable energies there have been proposals for the use of Artificial Neural Networks (ANNs) as a tool to estimate the mean wind speed at a target station for which only incomplete data series are available. In general terms, the wind speeds recorded at neighbouring reference stations are used as signals of the input layer of multilayer perceptron (MLP) architectures. An analysis is undertaken in this paper of the extent to which estimation of the wind speed, the wind power density and the power output of an installed wind turbine at a target site is affected by the number of reference stations, the degree of correlation between the wind speeds of the reference stations and the target station, the wind direction and the manner in which the direction signal is introduced into the input layer. For the purposes of this study mean hourly wind speeds and directions were used which had been recorded at twenty two weather stations located in the Canary Archipelago (Spain) in 2002 and 2006. The power-wind speed characteristic curves of five wind turbines of different rated power were also used. The MLP architectures were trained using the backpropagation algorithm. Amongst other conclusions reached with the tests performed with the set of data not used in the training nor in the validation of the models is that the estimation errors tend to decrease as the number of reference stations used increases, independently of the existing correlation coefficient values between the reference stations and the candidate station. It was also observed that if input signals of angular wind direction and wind speed are used then the number of reference stations required to achieve a certain decrease in the error is lower than the number required if only wind speed input signals are used. By using multiple reference stations with input signals of angular wind direction and speed, error reductions have been achieved for some stations in the estimation of the wind power density and the power output of wind turbines, with respect to the case of a single reference station and identical input signals, of 75% and 62%, respectively. [Copyright &y& Elsevier]

Abstract: In this paper an extended stochastic multi-objective model for economic dispatch (ED) is proposed, that incorporates in the optimization process heat and power from CHP units and expected wind power. Stochastic restrictions for the CO2, SO2 and NO x emissions are used as inequality constraints. The ED problem is solved using a multi-objective particle swarm optimization technique. The available wind power is estimated from a transformation of the wind speed considered as a random variable to wind power. Simulations are performed on the modified IEEE 30 bus network with 2 cogeneration units and actual wind data. Results concerning minimum cost and emissions reduction options are finally drawn. [ABSTRACT FROM AUTHOR]

• A refined wind farm parameterization in the WRF model is conducted. • The power coefficients of the rotor equivalent wind speed under real air density are modified. • The average power is notably decreased by the reduced variable air density. • The impact of atmospheric stability on the power is compared between original and refined models. In the original wind farm parameterization of the Weather Research and Forecasting model, the standard constant air density is a source of errors in power output estimation, and the hub-height wind speed is inappropriate to represent the wind speed through the whole rotor area. To overcome these issues, a refined model incorporating variable air density is developed, and the hub-height wind speed is replaced with the rotor equivalent wind speed (REWS) in this study. To keep consistency, the power coefficients corresponding to the REWS under variable air density conditions are modified. The influences of atmospheric stability are investigated to evaluate the performance of wind power estimation between the original and the current refined models. The refined wind farm parameterization has few impacts on the flow field, however, the average power generation becomes more scattered and is notably decreased by nearly 7% due to the reduced variable air density. Meanwhile, the REWS slightly increases the power estimation by around 1.1%. Both REWS and variable air density can decrease the estimation uncertainty of the average power induced by complex atmospheric stability. Moreover, atmospheric stability plays a more significant role in power outputs, and the refined model can capture a stronger correlation between the atmospheric condition and wind power generation. [ABSTRACT FROM AUTHOR]