Wind power generation forecast by coupling numerical weather prediction model and gradient boosting machines in Yahyalı wind power plant

2020 ◽  
pp. 0309524X2097211
Author(s):  
Cem Özen ◽  
Umur Dinç ◽  
Ali Deniz ◽  
Haldun Karan
Keyword(s):  
Power Generation ◽  
Wind Power ◽  
Hybrid Model ◽  
Spatial Resolution ◽  
Numerical Weather Prediction ◽  
Wind Farm ◽  
Weather Prediction ◽  
Numerical Weather Prediction Model ◽  
Wind Power Generation ◽  
Gradient Boosting

Forecasting of the wind speed and power generation for a wind farm has always been quite challenging and has importance in terms of balancing the electricity grid and preventing energy imbalance penalties. This study focuses on creating a hybrid model that uses both numerical weather prediction model and gradient boosting machines (GBM) for wind power generation forecast. Weather Research and Forecasting (WRF) model with a low spatial resolution is used to increase temporal resolutions of the computed new or existing variables whereas GBM is used for downscaling purposes. The results of the hybrid model have been compared with the outputs of a stand-alone WRF which is well configured in terms of physical schemes and has a high spatial resolution for Yahyalı wind farm over a complex terrain located in Turkey. Consequently, the superiority of the hybrid model in terms of both performance indicators and computational expense in detail is shown.

scholarly journals Day-Ahead Wind Power Forecasting in Poland Based on Numerical Weather Prediction

Energies ◽  
2021 ◽  
Vol 14 (8) ◽  
pp. 2164
Author(s):  
Bogdan Bochenek ◽  
Jakub Jurasz ◽  
Adam Jaczewski ◽  
Gabriel Stachura ◽  
Piotr Sekuła ◽  
...  
Keyword(s):  
Power Generation ◽  
Power System ◽  
Wind Power ◽  
Numerical Weather Prediction ◽  
Weather Prediction ◽  
Wind Power Generation ◽  
Mean Absolute Percentage Error ◽  
Percentage Error ◽  
Numerical Weather ◽  
Absolute Percentage Error

The role of renewable energy sources in the Polish power system is growing. The highest share of installed capacity goes to wind and solar energy. Both sources are characterized by high variability of their power output and very low dispatchability. Taking into account the nature of the power system, it is, therefore, imperative to predict their future energy generation to economically schedule the use of conventional generators. Considering the above, this paper examines the possibility to predict day-ahead wind power based on different machine learning methods not for a specific wind farm but at national level. A numerical weather prediction model used operationally in the Institute of Meteorology and Water Management–National Research Institute in Poland and hourly data of recorded wind power generation in Poland were used for forecasting models creation and testing. With the best method, the Extreme Gradient Boosting, and two years of training (2018–2019), the day-ahead, hourly wind power generation in Poland in 2020 was predicted with 26.7% mean absolute percentage error and 4.5% root mean square error accuracy. Seasonal and daily differences in predicted error were found, showing high mean absolute percentage error in summer and during daytime.

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 Forecasting Wind Power Generation Using Artificial Neural Network: “Pawan Danawi”—A Case Study from Sri Lanka

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Amila T. Peiris ◽  
Jeevani Jayasinghe ◽  
Upaka Rathnayake
Keyword(s):  
Neural Network ◽  
Power Generation ◽  
Wind Power ◽  
Wind Farm ◽  
Mean Squared Error ◽  
Wind Power Generation ◽  
Training Algorithms ◽  
Training Algorithm ◽  
Squared Error ◽  
Ann Models

Wind power, as a renewable energy resource, has taken much attention of the energy authorities in many countries, as it is used as one of the major energy sources to satisfy the ever-increasing energy demand. However, careful attention is needed in identifying the wind power potential in a particular area due to climate changes. In this sense, forecasting both wind power generation and wind power potential is essential. This paper develops artificial neural network (ANN) models to forecast wind power generation in “Pawan Danawi”, a functioning wind farm in Sri Lanka. Wind speed, wind direction, and ambient temperature of the area were used as the independent variable matrices of the developed ANN models, while the generated wind power was used as the dependent variable. The models were tested with three training algorithms, namely, Levenberg-Marquardt (LM), Scaled Conjugate Gradient (SCG), and Bayesian Regularization (BR) training algorithms. In addition, the model was calibrated for five validation percentages (5% to 25% in 5% intervals) under each algorithm to identify the best training algorithm with the most suitable training and validation percentages. Mean squared error (MSE), coefficient of correlation (R), root mean squared error ratio (RSR), Nash number, and BIAS were used to evaluate the performance of the developed ANN models. Results revealed that all three training algorithms produce acceptable predictions for the power generation in the Pawan Danawi wind farm with R > 0.91, MSE < 0.22, and BIAS < 1. Among them, the LM training algorithm at 70% of training and 5% of validation percentages produces the best forecasting results. The developed models can be effectively used in the prediction of wind power at the Pawan Danawi wind farm. In addition, the models can be used with the projected climatic scenarios in predicting the future wind power harvest. Furthermore, the models can acceptably be used in similar environmental and climatic conditions to identify the wind power potential of the area.

scholarly journals Generating wind power scenarios for probabilistic ramp event prediction using multivariate statistical post-processing

2018 ◽  
Author(s):  
Rochelle P. Worsnop ◽  
Michael Scheuerer ◽  
Thomas M. Hamill ◽  
Julie K. Lundquist
Keyword(s):  
Wind Speed ◽  
Wind Power ◽  
Wind Farm ◽  
Weather Prediction ◽  
Model Performance ◽  
The United States ◽  
Numerical Weather Prediction Model ◽  
Post Processing ◽  
Skill Scores ◽  
Ramp Events

Abstract. Wind power forecasting is gaining international significance as more regions promote policies to increase the use of renewable energy. Wind ramps, large variations in wind power production during a period of minutes to hours, challenge utilities and electrical balancing authorities. A sudden decrease in wind energy production must be balanced by other power generators to meet energy demands, while a sharp increase in unexpected production results in excess power that may not be used in the power grid, leading to a loss of potential profits. In this study, we compare different methods to generate probabilistic ramp forecasts from the High Resolution Rapid Refresh (HRRR) numerical weather prediction model with up to twelve hours of lead time at two tall-tower locations in the United States. We validate model performance using 21 months of 80-m wind speed observations from towers in Boulder, Colorado and near the Columbia River Gorge in eastern Oregon. We employ four statistical post-processing methods, three of which are not currently used in the literature for wind forecasting. These procedures correct biases in the model and generate short-term wind speed scenarios which are then converted to power scenarios. This probabilistic enhancement of HRRR point forecasts provides valuable uncertainty information of ramp events and improves the skill of predicting ramp events over the raw forecasts. We compute Brier skill scores for each method at predicting up- and down-ramps to determine which method provides the best prediction. We find that the Standard Schaake Shuffle method yields the highest skill at predicting ramp events for these data sets, especially for up-ramp events at the Oregon site. Increased skill for ramp prediction is limited at the Boulder, CO site using any of the multivariate methods, because of the poor initial forecasts in this area of complex terrain. These statistical methods can be implemented by wind farm operators to generate a range of possible wind speed and power scenarios to aid and optimize decisions before ramp events occur.

J0503-1-5 Development and Evaluation of a Prediction System for Wind Power Generation over a Wind Farm

2009 ◽  
Vol 2009.7 (0) ◽  
pp. 81-82
Author(s):  
Shinji KADOKURA ◽  
Atsushi HASHIMOTO ◽  
Yasuo HATTORI ◽  
Soichiro SUGIMOTO ◽  
Koji WADA ◽  
...  
Keyword(s):  
Power Generation ◽  
Wind Power ◽  
Wind Farm ◽  
Wind Power Generation ◽  
Prediction System

Structural Control Issues in New Generation Offshore Wind Energy Plants

2012 ◽  
Vol 83 ◽  
pp. 167-176 ◽  
Author(s):  
Ning Su Luo
Keyword(s):  
Power Generation ◽  
Wind Power ◽  
Wind Turbines ◽  
Structural Control ◽  
Wind Farm ◽  
Wind Power Generation ◽  
Offshore Wind ◽  
Wave Loads ◽  
Offshore Wind Power ◽  
New Generation

A new constructive solution for the offshore wind power generation is to use floating wind turbines. An offshore wind farm situated sufficiently far away from the coast can generate more wind power and will have a longer operation life since the wind is stronger and more consistent than that on or near the coast. One of the main challenges is to reduce the fatigue of a floating wind turbine so as to guarantee its proper functioning under the constraints imposed by the floating support platforms. This paper will discuss the structural control issues related to the mitigation of dynamic wind and wave loads on the floating wind turbines so as to enhance the offshore wind power generation.

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