Preliminary Assessment of Remote Wind Sites

Published version also available at http://dx.doi.org/10.1016/j.egypro.2015.07.481Wind energy is becoming a reliable and affordable source of clean energy and is rapidly expanding to remote places around the world. A crucial input for wind farming prospect is the assessment of potential wind sites. Sites, especially remotely located, often do not have a wind resource map and thus lack credible historical records of wind resources. Measurement campaigns to map these sites are costly and time consuming. In this paper, a method for preliminary wind resource assessment for remote sites is proposed. The method is a combination of interpolation and extrapolation of data from the surrounding sites to the potential wind farm site. Two interpolation techniques, viz., Inverse Distance Weighting (IDW) and Triangulated Irregular Network (TIN), are applied to the data set recorded by Sonic Detection and Ranging (SODAR) in West Texas, USA with the surrounding sites within 300 km radius of the potential site. Extrapolation is done by using a power law with the exponent equal to 1/7. The resulting values of the wind speeds are validated with the available 200 m meteorological tower measurements at the potential site in Reese, Lubbock West-Texas, USA. Root mean square error (RMSE) of daily averages of wind speed ranged from 1.5 to 3 meters per seconds

Examination of turbulence impacts on ultra-short-term wind power and speed forecasts with machine learning

Wind turbines’ economic and secure operation can be optimized through accurate ultra-short-term wind power and speed forecasts. Turbulence, considered as a local short-term physical wind phenomenon, affects wind power generation. This paper investigates the use of turbulence intensity for ultra-short-term predictions of wind power and speed with a wind farm in the Arctic, including and excluding wind turbulence, within three hours by employing several different machine learning algorithms. A rigorous and detailed statistical comparison of the predictions is conducted. The results show that the algorithms achieve reasonably accurate predictions, but turbulence intensity does not statistically contribute to wind power or speed forecasts. This observation illustrates the uncertainty of turbulence in wind power generation. Besides, differences between the types of algorithms for ultra-short-term wind forecasts are also statistically insignificant, demonstrating the unique stochasticity and complexity of wind speed and power

Data-augmented sequential deep learning for wind power forecasting

Accurate wind power forecasting plays a critical role in the operation of wind parks and the dispatch of wind energy into the power grid. With excellent automatic pattern recognition and nonlinear mapping ability for big data, deep learning is increasingly employed in wind power forecasting. However, salient realities are that in-situ measured wind data are relatively expensive and inaccessible and correlation between steps is omitted in most multistep wind power forecasts. This paper is the first time that data augmentation is applied to wind power forecasting by systematically summarizing and proposing both physics-oriented and data-oriented time-series wind data augmentation approaches to considerably enlarge primary datasets, and develops deep encoder-decoder long short-term memory networks that enable sequential input and sequential output for wind power forecasting. The proposed augmentation techniques and forecasting algorithm are deployed on five turbines with diverse topographies in an Arctic wind park, and the outcomes are evaluated against benchmark models and different augmentations. The main findings reveal that on one side, the average improvement in RMSE of the proposed forecasting model over the benchmarks is 33.89%, 10.60%, 7.12%, and 4.27% before data augmentations, and increases to 40.63%, 17.67%, 11.74%, and 7.06%, respectively, after augmentations. The other side unveils that the effect of data augmentations on prediction is intricately varying, but for the proposed model with and without augmentations, all augmentation approaches boost the model outperformance from 7.87% to 13.36% in RMSE, 5.24% to 8.97% in MAE, and similarly over 12% in QR90. Finally, data-oriented augmentations, in general, are slightly better than physics-driven ones

A southern, middle, and northern Norwegian offshore wind energy resources analysis by a transfer learning method for Energy Internet

As renewable energy sources offshore wind energy develop quickly, countries like Norway with long coastlines are exploring their potential. However, the diverse wind resources across different regions of Norway present challenges for study for effective utilization of offshore wind energy. This study proposes a novel method that utilizes transfer learning techniques to analyse the resource differences between these areas for optimum energy generation. The suggested approach is tested using real-world wind data from Norway’s southern, middle, and northern regions. The results show that transfer learning successfully bridges resource discrimination, boosting wind resource prediction precision in the target domains. The work can contribute to optimizing offshore wind energy utilization in Norway by addressing the resource disparities and forecasting between the different regions

Probability distributions for wind speed volatility characteristics: A case study of Northern Norway

The Norwegian Arctic is rich in wind resources. The development of wind power in this region can boost green energy and also promote local economies. In wind power engineering, it is a tremendous advantage to base projects on a sound understanding of the intrinsic properties of wind resources in an area. Wind speed volatility, a phenomenon that strongly affects wind power generation, has not received sufficient research attention. In this paper, a framework for studying short-term wind speed volatility with statistical analysis and probabilistic modeling is constructed for an existing wind farm in Northern Norway. It is found that unlike the characteristics of wind power volatility, wind speed volatility cannot be described by the normal distribution. The reason is that even though the probability distribution of wind speed volatility is centrally symmetric, it is much more centrally concentrated and has thicker tails. After comparing three distributions corresponding to different sampling periods, this paper suggests utilizing the t distribution, with average modeling RMSE less than 0.006 and R2 exceeding 0.995 and with the best modeling scenario of temporal resolution, the 30 mins has an RMSE of 0.0051 and an R2 of 0.997, to more accurately and effectively explore the fluctuating characteristics of wind speed

Multitaper Estimation Of Bicoherence

The statistical properties for bicoherence estimation are shown to be strongly connected to the properties of the power- and bispectral estimator used. Data tapering will reduce spectral leakage and frequency smoothing will reduce the variance. It is shown that correct normalization is essential to ensure unbiased results. The multitaper approach is shown to be superior to other non-parametric estimators for bicoherence estimation

Evaluation of the Weather Research and Forecasting (WRF) model with respect to wind in complex terrain

In this study the performance of the Weather Research and Forecast (WRF) model in a complex and coastal terrain has been evaluated with focus on wind resource assessment. The study area is a small community on the northern part of the island Senja, Norway. The community, with fishery and seafood as its main industry, is being limited by poor grid connection. One of the solutions is to increase the production of local power from wind energy. There are no in-situ wind measurements in the area, and therefore numerical weather prediction models, namely the WRF model, is being evaluated as a method for wind resource assessment. The WRF model has been run for the whole of 2017 with high resolution covering an area large enough to include the three closest weather stations. The model is compared to the observed wind speed and direction. It is found that the model is able to reproduce the average wind speed and wind direction quite well for two of the locations, while for the third location the average wind speed is considerably overestimated compared to the observations. The Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) found are larger than in other comparable studies

An Evaluation on Diverse Machine Learning Algorithms for Hourly Univariate Wind Power Prediction in the Arctic

Wind power forecasting is crucial for wind power systems, grid load balance, maintenance, and grid operation optimization. The utilization of wind energy in the Arctic regions helps reduce greenhouse gas emissions in this environmentally vulnerable area. In the present study, eight various models, seven of which are representative machine learning algorithms, are used to make 1, 2, and 3 step hourly wind power predictions for five wind parks inside the Norwegian Arctic regions, and their performance is compared. Consequently, we recommend the persistence model, multilayer perceptron, and support vector regression for univariate time-series wind power forecasting within the time horizon of 3 hours

Wind speed and direction predictions by WRF and WindSim coupling over Nygårdsfjell

In this study, the performance of the mesoscale meteorological Weather Research and Forecast (WRF) model coupled with the microscale computational fluid dynamics based model WindSim is investigated and compared to the performance of WRF alone. The two model set-ups, WRF and WRF-WindSim, have been tested on three high-wind events in February, June and October, over a complex terrain at the Nyg˚ardsfjell wind park in Norway. The wind speeds and wind directions are compared to measurements and the results are evaluated based on root mean square error, bias and standard deviation error. Both model set-ups are able to reproduce the high wind events. For the winter month February the WRF-WindSim performed better than WRF alone, with the root mean square error (RMSE) decreasing from 2.86 to 2.38 and standard deviation error (STDE) decreasing from 2.69 to 2.37. For the two other months no such improvements were found. The best model performance was found in October where the WRF had a RMSE of 1.76 and STDE of 1.68. For June, both model set-ups underestimate the wind speed. Overall, the adopted coupling method of using WRF outputs as virtual climatology for coupling WRF and WindSim did not offer a significant improvement over the complex terrain of Nyg˚ardsfjell. However, the proposed coupling method offers high degree of simplicity when it comes to its application. Further testing is recommended over larger number of test cases to make a significant conclusion

Machine learning forecasts of Scandinavian numerical weather prediction wind model residuals with control theory for wind energy

The quality of wind data from the numerical weather prediction significantly influences the accuracy of wind power forecasting systems for wind parks. Therefore, an in-depth investigation of these wind data themselves is essential to improve wind power generation efficiency and maintain grid reliability. This paper proposes a novel framework based on machine learning for concurrently analyzing and forecasting predictive errors, called residuals, of wind speed and direction from a numerical weather prediction model versus measurements over a while. The performance of the framework is testified by a wind farm inside the Arctic. It is demonstrated that the residuals still contain significant meteorological information and can be effectively predicted with machine learning and the linear autoregression works well for multi-timesteps predictions of overall, East-West, East–West,​ and North-South North–South wind speeds residuals by comparing the four forecast learning algorithms’ performance. The predictions may be applied to correct the NWP wind model, making quality feedback improvements for inputs for wind power forecasting systems.