scholarly journals Wind Farm Power Forecasting

2013 ◽  
Vol 2013 ◽  
pp. 1-5 ◽  
Author(s):  
Nabiha Haouas ◽  
Pierre R. Bertrand
Keyword(s):  
Wind Turbine ◽  
Wind Power ◽  
Wind Farm ◽  
Random Variable ◽  
Power Production ◽  
Energy Industry ◽  
Gaussian Random Variable ◽  
The Mean ◽  
The Relationship ◽  
Power Forecasting

Forecasting annual wind power production is useful for the energy industry. Until recently, attention has only been paid to the mean annual wind power energy and statistical uncertainties on this forecasting. Recently, Bensoussan et al. (2012) have pointed that the annual wind power produced by one wind turbine is a Gaussian random variable under a reasonable set of assumptions. Moreover, they can derive both mean and quantiles of annual wind power produced by one wind turbine. The novelty of this work is the obtainment of similar results for estimating the annual wind farm power production. Eventually, we study the relationship between the power production for each turbine of the farm in order to avoid interaction between them.

scholarly journals Evaluation of Wind Resources and the Effect of Market Price Components on Wind-Farm Income: A Case Study of Ørland in Norway

Energies ◽  
2018 ◽  
Vol 11 (11) ◽  
pp. 2955 ◽  
Author(s):  
Ali Marjan ◽  
Mahmood Shafiee
Keyword(s):  
Wind Turbine ◽  
Wind Power ◽  
Wind Turbines ◽  
Wind Farm ◽  
Market Price ◽  
Real Data ◽  
Power Production ◽  
Power Measurement ◽  
Farm Income ◽  
Wind Resources

This paper aims to present a detailed analysis of the performance of a wind-farm using the wind turbine power measurement standard IEC61400-12-1 (2017). Ten minutes averaged wind data are obtained from LIDAR over the period of twelve months and it is compared with the 38 years’ data from weather station with the objective of determining the wind resources at the wind-farm. The performance of one of the wind turbines located in the wind-farm is assessed by comparing the wind power potential of the wind turbine with its actual power production. Our analysis shows that the wind farm under study is rated as ‘good’ in terms of wind power production and has wind power density of 479 W/m2. The annual wind-farm’s income is estimated based on the real-data collected from the wind turbines. The effect of price of electricity and the spot prices of Norwegian-Swedish green certificate on the income will be illustrated by means of a Monte-Carlo Simulation (MCS) approach. Our study provides a different perspective of wind resource evaluation by analyzing LIDAR measurements using Windographer and combines it with the lesser explored effects of price components on the income using statistical tools.

scholarly journals Numerical Weather Prediction and Artificial Neural Network Coupling for Wind Energy Forecast

Energies ◽  
2021 ◽  
Vol 14 (2) ◽  
pp. 338
Author(s):  
Lorenzo Donadio ◽  
Jiannong Fang ◽  
Fernando Porté-Agel
Keyword(s):  
Machine Learning ◽  
Wind Energy ◽  
Wind Power ◽  
Numerical Weather Prediction ◽  
Wind Farm ◽  
Weather Prediction ◽  
Wind Power Forecasting ◽  
Numerical Weather ◽  
Artificial Neural ◽  
Power Forecasting

In the past two decades, wind energy has been under fast development worldwide. The dramatic increase of wind power penetration in electricity production has posed a big challenge to grid integration due to the high uncertainty of wind power. Accurate real-time forecasts of wind farm power outputs can help to mitigate the problem. Among the various techniques developed for wind power forecasting, the hybridization of numerical weather prediction (NWP) and machine learning (ML) techniques such as artificial neural networks (ANNs) are attracting many researchers world-wide nowadays, because it has the potential to yield more accurate forecasts. In this paper, two hybrid NWP and ANN models for wind power forecasting over a highly complex terrain are proposed. The developed models have a fine temporal resolution and a sufficiently large prediction horizon (>6 h ahead). Model 1 directly forecasts the energy production of each wind turbine. Model 2 forecasts first the wind speed, then converts it to the power using a fitted power curve. Effects of various modeling options (selection of inputs, network structures, etc.) on the model performance are investigated. Performances of different models are evaluated based on four normalized error measures. Statistical results of model predictions are presented with discussions. Python was utilized for task automation and machine learning. The end result is a fully working library for wind power predictions and a set of tools for running the models in forecast mode. It is shown that the proposed models are able to yield accurate wind farm power forecasts at a site with high terrain and flow complexities. Especially, for Model 2, the normalized Mean Absolute Error and Root Mean Squared Error are obtained as 8.76% and 13.03%, respectively, lower than the errors reported by other models in the same category.

scholarly journals A Compound Wind Power Forecasting Strategy Based on Clustering, Two-Stage Decomposition, Parameter Optimization, and Optimal Combination of Multiple Machine Learning Approaches

Energies ◽  
2019 ◽  
Vol 12 (18) ◽  
pp. 3586 ◽  
Author(s):  
Sizhou Sun ◽  
Jingqi Fu ◽  
Ang Li
Keyword(s):  
Wind Power ◽  
Large Scale ◽  
Wind Farm ◽  
Search Algorithm ◽  
Interpolation Method ◽  
Least Square ◽  
Support Vector ◽  
Two Stage ◽  
Wind Power Forecasting ◽  
Power Forecasting

Given the large-scale exploitation and utilization of wind power, the problems caused by the high stochastic and random characteristics of wind speed make researchers develop more reliable and precise wind power forecasting (WPF) models. To obtain better predicting accuracy, this study proposes a novel compound WPF strategy by optimal integration of four base forecasting engines. In the forecasting process, density-based spatial clustering of applications with noise (DBSCAN) is firstly employed to identify meaningful information and discard the abnormal wind power data. To eliminate the adverse influence of the missing data on the forecasting accuracy, Lagrange interpolation method is developed to get the corrected values of the missing points. Then, the two-stage decomposition (TSD) method including ensemble empirical mode decomposition (EEMD) and wavelet transform (WT) is utilized to preprocess the wind power data. In the decomposition process, the empirical wind power data are disassembled into different intrinsic mode functions (IMFs) and one residual (Res) by EEMD, and the highest frequent time series IMF1 is further broken into different components by WT. After determination of the input matrix by a partial autocorrelation function (PACF) and normalization into [0, 1], these decomposed components are used as the input variables of all the base forecasting engines, including least square support vector machine (LSSVM), wavelet neural networks (WNN), extreme learning machine (ELM) and autoregressive integrated moving average (ARIMA), to make the multistep WPF. To avoid local optima and improve the forecasting performance, the parameters in LSSVM, ELM, and WNN are tuned by backtracking search algorithm (BSA). On this basis, BSA algorithm is also employed to optimize the weighted coefficients of the individual forecasting results that produced by the four base forecasting engines to generate an ensemble of the forecasts. In the end, case studies for a certain wind farm in China are carried out to assess the proposed forecasting strategy.

Wind power forecasting for a real onshore wind farm on complex terrain using WRF high resolution simulations

2019 ◽  
Vol 135 ◽  
pp. 674-686 ◽  
Author(s):  
Miguel A. Prósper ◽  
Carlos Otero-Casal ◽  
Felipe Canoura Fernández ◽  
Gonzalo Miguez-Macho
Keyword(s):  
High Resolution ◽  
Wind Power ◽  
Complex Terrain ◽  
Wind Farm ◽  
Onshore Wind ◽  
Wind Power Forecasting ◽  
Power Forecasting

scholarly journals Assessment of Wind Characteristics and Wind Power Potential of Gharo, Pakistan

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Zahid Hussain Hulio
Keyword(s):  
Wind Turbine ◽  
Wind Power ◽  
Frequency Distribution ◽  
Cost Estimation ◽  
Wind Farm ◽  
Research Work ◽  
Economic Assessment ◽  
High Energy ◽  
Energy Yield ◽  
Wind Characteristics

The objective of this research work is to assess the wind characteristics and wind power potential of Gharo site. The wind parameters of the site have been used to calculate the wind power density, annual energy yield, and capacity factors at 10, 30, and 50 m. The wind frequency distribution including seasonal as well as percentage of seasonal frequency distribution has been investigated to determine accurately the wind power of the site. The coefficient of variation is calculated at three different heights. Also, economic assessment per kWh of energy has been carried out. The site-specific annual mean wind speeds were 6.89, 5.85, and 3.85 m/s at 50, 30, and 10 m heights with corresponding standard deviations of 2.946, 2.489, and 2.040. The mean values of the Weibull k parameter are estimated as 2.946, 2.489, and 2.040 while those of scale parameter are estimated as 7.634, 6.465, and 4.180 m/s at 50, 30, and 10 m, respectively. The respective mean wind power and energy density values are found to be 118.3, 92.20, and 46.10 W/m2 and 1036.6, 807.90, and 402.60 kWh/m2. As per cost estimation of wind turbines, the wind turbine WT-C has the lowest cost of US$ Cents 0.0346/kWh and highest capacity factors of 0.3278 (32.78%). Wind turbine WT-C is recommended for this site for the wind farm deployment due to high energy generation and minimum price of energy. The results show the appropriateness of the methodology for assessing the wind speed and economic assessment at the lowest price of energy.

Optimal Operation Planning of Wind Farm Installed BESS Using Wind Power Forecast Data of Wind Turbine Generators Considering Forecast Error

2013 ◽  
Vol 14 (3) ◽  
pp. 207-218 ◽  
Author(s):  
Kazuki Ogimi ◽  
Shota Kamiyama ◽  
Michael Palmer ◽  
Atsushi Yona ◽  
Tomonobu Senju ◽  
...  
Keyword(s):  
Wind Turbine ◽  
Wind Power ◽  
Wind Farm ◽  
Forecast Error ◽  
Energy Resource ◽  
Optimal Operation ◽  
Turbine Generators ◽  
Wind Turbine Generators ◽  
Wind Power Forecast ◽  
Forecast Data

Abstract In order to solve the problems of global warming and depletion of energy resource, renewable energy systems such as wind generation are getting attention. However, wind power fluctuates due to variation of wind speed, and it is difficult to perfectly forecast wind power. This paper describes a method to use power forecast data of wind turbine generators considering wind power forecast error for optimal operation. The purpose in this paper is to smooth the output power fluctuation of a wind farm and to obtain more beneficial electrical power for selling.

Comparison of Different Time Series Methods for Short-Term Forecasting of Wind Power Production

2010 ◽  
Author(s):  
Gong Li ◽  
Jing Shi
Keyword(s):  
Wind Power ◽  
Wind Farm ◽  
Mean Squared Error ◽  
Network Models ◽  
Absolute Error ◽  
Power Production ◽  
Model Parameters ◽  
Short Term ◽  
Bp Network ◽  
Persistence Model

Reliable short-term predictions of the wind power production are critical for both wind farm operations and power system management, where the time scales can vary in the order of several seconds, minutes, hours and days. This comprehensive study mainly aims to quantitatively evaluate and compare the performances of different Box & Jenkins models and backpropagation (BP) neural networks in forecasting the wind power production one-hour ahead. The data employed is the hourly power outputs of an N.E.G. Micon 900-kilowatt wind turbine, which is installed to the east of Valley City, North Dakota. For each type of Box & Jenkins models tested, the model parameters are estimated to determine the corresponding optimal models. For BP network models, different input layer sizes, hidden layer sizes, and learning rates are examined. The evaluation metrics are mean absolute error and root mean squared error. Besides, the persistence model is also employed for purpose of comparison. The results show that in general both best performing Box & Jenkins and BP models can provide better forecasts than the persistence model, while the difference between the Box & Jenkins and BP models is actually insignificant.

Dynamic Simulation of a Wind Farm With Variable Speed Wind Turbines

2003 ◽  
Author(s):  
E. Muljadi ◽  
C. P. Butterfield
Keyword(s):  
Wind Turbine ◽  
Wind Power ◽  
Wind Turbines ◽  
Wind Farm ◽  
Reactive Power ◽  
Power Network ◽  
Variable Speed ◽  
Power Capacity ◽  
Advantages And Disadvantages ◽  
The Impact

Wind power generation has increased very rapidly in the past few years. The total U.S. wind power capacity by the end of 2001 was 4,260 megawatts. As wind power capacity increases, it becomes increasingly important to study the impact of wind farm output on the surrounding power networks. In this paper, we attempt to simulate a wind farm by including the properties of the wind turbine, the wind speed time series, the characteristics of surrounding power network, and reactive power compensation. Mechanical stress and fatigue load of the wind turbine components are beyond the scope this paper. The paper emphasizes the impact of the wind farms on the electrical side of the power network. A typical wind farm with variable speed wind turbines connected to an existing power grid is investigated. Different control strategies for feeding wind energy into the power network are investigated, and the advantages and disadvantages are presented.

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