Wind power forecasting based on daily wind speed data using machine learning algorithms

2019 ◽  
Vol 198 ◽  
pp. 111823 ◽  
Author(s):  
Halil Demolli ◽  
Ahmet Sakir Dokuz ◽  
Alper Ecemis ◽  
Murat Gokcek
Keyword(s):  
Machine Learning ◽  
Wind Speed ◽  
Wind Power ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Wind Power Forecasting ◽  
Wind Speed Data ◽  
Power Forecasting

scholarly journals Wind power plant forecasting and power prediction methods using Machine Learning Algorithms

2021 ◽  
Vol 10 (2) ◽  
pp. 687-691
Keyword(s):  
Machine Learning ◽  
Wind Speed ◽  
Wind Energy ◽  
Wind Power ◽  
Renewable Energy Sources ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Data Sets ◽  
Power Prediction ◽  
Wind Speed Data

A significant and eligible source such as wind energy has the potential for producing energy in a continuous and sustainable manner among renewable energy sources. However, wind energy has several challenges, such as initial investment costs, the stationary property of wind plants, and the difficulty in finding wind-efficient energy areas. In this study, wind power forecasting was performed based on daily wind speed data using machine learning algorithms. The proposed method is based on machine learning algorithms to forecast wind power values efficiently. Tests were conducted on data sets to reveal performances of machine learning algorithms. The results showed that machine learning algorithms could be used for forecasting long-term wind power values with respect to historical wind speed data. Furthermore, several machine learning models were built for analysis on the accuracy level of the respective models, i.e, the accuracy levels of the machine.

Comparative Investigation of Machine Learning Algorithms for Wind Power Forecasting

2021 ◽  
pp. 513-522
Author(s):  
Ayush Kumar ◽  
Neeraj Kumar ◽  
Bharat Singh ◽  
Aditya Chaudhary ◽  
Karan Dikshit ◽  
...  
Keyword(s):  
Machine Learning ◽  
Wind Power ◽  
Learning Algorithms ◽  
Comparative Investigation ◽  
Machine Learning Algorithms ◽  
Wind Power Forecasting ◽  
Power Forecasting

Wind power forecasting based on time series model using deep machine learning algorithms

2021 ◽  
Author(s):  
V. Chandran ◽  
Chandrashekhar K. Patil ◽  
Anto Merline Manoharan ◽  
Aritra Ghosh ◽  
M.G. Sumithra ◽  
...  
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Wind Power ◽  
Learning Algorithms ◽  
Time Series Model ◽  
Machine Learning Algorithms ◽  
Wind Power Forecasting ◽  
Power Forecasting

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

2021 ◽  
Vol 2141 (1) ◽  
pp. 012016
Author(s):  
Hao Chen ◽  
Yngve Birkelund
Keyword(s):  
Machine Learning ◽  
Wind Power ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
The Arctic ◽  
Support Vector ◽  
Power Prediction ◽  
Wind Power Forecasting ◽  
Arctic Regions ◽  
Power Forecasting

Abstract 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.

scholarly journals Integrated Machine Learning and Enhanced Statistical Approach-Based Wind Power Forecasting in Australian Tasmania Wind Farm

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Fang Yao ◽  
Wei Liu ◽  
Xingyong Zhao ◽  
Li Song
Keyword(s):  
Machine Learning ◽  
Wind Speed ◽  
Wind Power ◽  
Wind Farm ◽  
Statistical Approach ◽  
Short Term Memory ◽  
Prediction Interval ◽  
Wind Power Forecasting ◽  
Proposed Model ◽  
Power Forecasting

This paper develops an integrated machine learning and enhanced statistical approach for wind power interval forecasting. A time-series wind power forecasting model is formulated as the theoretical basis of our method. The proposed model takes into account two important characteristics of wind speed: the nonlinearity and the time-changing distribution. Based on the proposed model, six machine learning regression algorithms are employed to forecast the prediction interval of the wind power output. The six methods are tested using real wind speed data collected at a wind station in Australia. For wind speed forecasting, the long short-term memory (LSTM) network algorithm outperforms other five algorithms. In terms of the prediction interval, the five nonlinear algorithms show superior performances. The case studies demonstrate that combined with an appropriate nonlinear machine learning regression algorithm, the proposed methodology is effective in wind power interval forecasting.

Impact of the accuracy of NWP wind speed forecasts on wind power forecasting

2019 ◽  
Author(s):  
Yimei Wang ◽  
Yongqian Liu ◽  
Peng Song ◽  
Hui Liu ◽  
Zechun Hu
Keyword(s):  
Wind Speed ◽  
Wind Power ◽  
Wind Power Forecasting ◽  
Power Forecasting

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 Distributed Demand Side Management with Stochastic Wind Power Forecasting

2020 ◽  
Author(s):  
Paolo Scarabaggio ◽  
Sergio Grammatico ◽  
Raffaele Carli ◽  
Mariagrazia Dotoli
Keyword(s):  
Wind Speed ◽  
Wind Power ◽  
Smart Grids ◽  
Demand Side Management ◽  
Expected Value ◽  
Noncooperative Game ◽  
Theory Approach ◽  
Demand Side ◽  
Wind Power Forecasting ◽  
Power Forecasting

In this paper, we propose a distributed demand side management (DSM) approach for smart grids taking into account uncertainty in wind power forecasting. The smart grid model comprehends traditional users as well as active users (prosumers). Through a rolling-horizon approach, prosumers participate in a DSM program, aiming at minimizing their cost in the presence of uncertain wind power generation by a game theory approach.<br>We assume that each user selfishly formulates its grid optimization problem as a noncooperative game.<br>The core challenge in this paper is defining an approach to cope with the uncertainty in wind power availability. <br>We tackle this issue from two different sides: by employing the expected value to define a deterministic counterpart for the problem and by adopting a stochastic approximated framework.<br>In the latter case, we employ the sample average approximation technique, whose results are based on a probability density function (PDF) for the wind speed forecasts. We improve the PDF by using historical wind speed data, and by employing a control index that takes into account the weather condition stability.<br><div>Numerical simulations on a real dataset show that the proposed stochastic strategy generates lower individual costs compared to the standard expected value approach.</div><div><br></div><div>Preprint of paper submitted to IEEE Transactions on Control Systems Technology<br></div>

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