Forecasting Short Term Wind Energy Generation using Machine Learning

Shabbir, Noman; Ahmadiahangar, Roya; Kütt, Lauri; Iqbal, Muhammad Naveed; Rosin, Argo

doi:10.1109/rtucon48111.2019.8982365

Cited by 35 publications

(11 citation statements)

References 13 publications

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“…These algorithms make decision trees based on a random selection of data samples and get predictions from every tree, after that, they select the best viable solution through votes. A tree has several nodes and the model searches for the best solution by going through all nodes [8].…”

Section: Methodsmentioning

confidence: 99%

Predictive Analysis of Wind Power Generationusing Machine Learning

Chauhan,

Kukreti,

Sharma

et al. 2022

IJEAST

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The proposed aim of project predictive analysis of wind power generation using Machine learning is to reduce the cost, provide cleaner and eco friendly source of energy and increase the efficiency of existing systems to greater extent by calculative analysis [1]. As the world is progressing towards renewable and clean energy it is an interdisciplinary research model that is designed to find an optimal way of harnessing wind energy via machine learning models. It is a viable option for Governments and Companies around the world which will help them to set up Wind Energy Plants at appropriate locations for maximizing the output power generation by prediction from the Machine Learning model. The experience obtained while working in the simulation of predictive analysis of wind power generation using machine learning based wind conversion can be successfully implemented in real life scenario helping in increasing renewable energy production. The main objective is to maximize the performance and efficiency of Wind Mills. This project will help in automation-. The control strategy used in this project is for maximizing the performance and efficiency of a Wind Mills.

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Section: Methodsmentioning

confidence: 99%

Predictive Analysis of Wind Power Generationusing Machine Learning

Chauhan,

Kukreti,

Sharma

et al. 2022

IJEAST

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“…2018: Reference 25. Gökgöz and Filiz 2018: Reference 26. Shabbir vd. 2019: Reference 27. Tharani vd. 2020: Reference 28. Murshed and Alam 2021: Reference 29. Magazzino vd.…”

Section: Literaturementioning

confidence: 99%

Predicting renewable energy production by machine learning methods: The case of Turkey

Yağmur

Kayakuş

Terzioğlu

2023

Env Prog and Sustain Energy

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It is considered that the use of renewable energy sources will replace fossil fuels due to global climate change and accompanying decisions taken by states. In this study, unlike the renewable energy production estimation studies in the literature, a model was created by taking the socioeconomic, environmental and energy time series data of the countries. In the study, Turkey, which did not promise a numerical reduction in greenhouse gas emissions unlike other developing countries but has an increasing energy production from renewable energy sources, was chosen. In the study, the data between 1990 and 2020 were used to receive more realistic results by considering the interval before and after the Kyoto protocol. Artificial neural networks and support vector regression among machine learning methods were used to predict the model. As a result of the study, support vector regression had a 92% and artificial neural networks had a successful predictive power of 89.9% according to the coefficient of determination (R 2 ). In the study, the root mean square error value was 0.071 for artificial neural networks and 0.045 for support vector regression; the mean squared error value was 0.005 for artificial neural networks and 0.002 for support vector regression, which was close to the ideal values. Both methods were statistically successful. It is predicted that the model designed because of these successful results obtained in the study would guide the creation of energy policies and contribute to scientific studies.

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“…Moreover, in SVM, two sets of variables are defined along with their constraints by converting the primal objective function into a Lagrange function. Further details of this algorithm are given in [7,39,40]:…”

Section: Support Vector Machine Regression (Svm)mentioning

confidence: 99%

Short-Term Wind Energy Forecasting Using Deep Learning-Based Predictive Analytics

Shabbir¹,

K黷t²,

Jawad³

et al. 2022

Computers, Materials &Amp; Continua

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Wind energy is featured by instability due to a number of factors, such as weather, season, time of the day, climatic area and so on. Furthermore, instability in the generation of wind energy brings new challenges to electric power grids, such as reliability, flexibility, and power quality. This transition requires a plethora of advanced techniques for accurate forecasting of wind energy. In this context, wind energy forecasting is closely tied to machine learning (ML) and deep learning (DL) as emerging technologies to create an intelligent energy management paradigm. This article attempts to address the short-term wind energy forecasting problem in Estonia using a historical wind energy generation data set. Moreover, we taxonomically delve into the state-of-the-art ML and DL algorithms for wind energy forecasting and implement different trending ML and DL algorithms for the day-ahead forecast. For the selection of model parameters, a detailed exploratory data analysis is conducted. All models are trained on a real-time Estonian wind energy generation dataset for the first time with a frequency of 1 h. The main objective of the study is to foster an efficient forecasting technique for Estonia. The comparative analysis of the results indicates that Support Vector Machine (SVM), Non-linear Autoregressive Neural Networks (NAR), and Recurrent Neural Network-Long-Term Short-Term Memory (RNN-LSTM) are respectively 10%, 25%, and 32% more efficient compared to TSO's forecasting algorithm. Therefore, RNN-LSTM is the best-suited and computationally effective DL method for wind energy forecasting in Estonia and will serve as a futuristic solution.

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Forecasting Short Term Wind Energy Generation using Machine Learning

Cited by 35 publications

References 13 publications

Predictive Analysis of Wind Power Generationusing Machine Learning

Predictive Analysis of Wind Power Generationusing Machine Learning

Predicting renewable energy production by machine learning methods: The case of Turkey

Short-Term Wind Energy Forecasting Using Deep Learning-Based Predictive Analytics

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