A novel implementation of kNN classifier based on multi-tupled meteorological input data for wind power prediction

Yeşilbudak, Mehmet; Sağıroğlu, Şeref; Çolak, İlhami

doi:10.1016/j.enconman.2016.12.094

Cited by 80 publications

(48 citation statements)

References 37 publications

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“…In addition, we used the persistence reference model, which is also known as Naïve Predictor [25,26] and which is widely used for the benchmark tests [27][28][29], in order to compare with other models in this study. In this reference model, the forecasted value at time t + 1 is equal to the value at time t. In other words, the persistence reference model is only based on the linear correlation between the present and the future photovoltaic power values.…”

Section: Multilayer Perceptronmentioning

confidence: 99%

Daily Photovoltaic Power Prediction Enhanced by Hybrid GWO-MLP, ALO-MLP and WOA-MLP Models Using Meteorological Information

2020

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Solar energy is a safe, clean, environmentally-friendly and renewable energy source without any carbon emissions to the atmosphere. Therefore, there are many studies in the field of solar energy in order to obtain the maximum solar radiation during the day time, to estimate the amount of solar energy to be produced, and to increase the efficiency of solar energy systems. In this study, it was aimed to predict the daily photovoltaic power production using air temperature, relative humidity, total horizontal solar radiation and diffuse horizontal solar radiation parameters as multi-tupled inputs. For this purpose, grey wolf, ant lion and whale optimization algorithms were integrated to the multilayer perceptron. In addition, the effects of sigmoid, sinus and hyperbolic tangent activation functions on the prediction performance were analyzed in detail. As a result of overall accuracy indictors achieved, the grey wolf optimization algorithm-based multilayer perceptron model was found to be more successful and competitive for the daily photovoltaic power prediction. Furthermore, many meaningful patterns were revealed about the constructed models, input tuples and activation functions.

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Section: Multilayer Perceptronmentioning

confidence: 99%

Daily Photovoltaic Power Prediction Enhanced by Hybrid GWO-MLP, ALO-MLP and WOA-MLP Models Using Meteorological Information

2020

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“…Normalmente, mas nem sempre, o algoritmo consegue uma melhor precisão com k valores mais altos. O algoritmo k-NN herda formas de tratar características de aplicações, como previsão meteorológica [Yesilbudak et al 2017], detecção de quedas de idosos [Tsinganos 2017], detecção de crime [Tayal et al 2015], além de um uso amplo na maioria dos problemas de reconhecimento de padrões, como tambémé empregado em alguns estudos recentes de classificação de ECG ou de detecção de convulsões epilépticas [Shanir et al 2017].…”

Section: K-nearest Neighbor (K-nn)unclassified

Autenticação usando Sinais Biométricos: Fundamentos, Aplicações e Desafios

Cerqueira¹,

Resque²,

Medeiros³

et al. 2019

Jornada De Atualização Em Informática 2019

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Our systems and data (e.g., websites, smartphones, safes, cars, banks, airports) are protected by traditional authentication methods. However, the growing concern about information security and the deployment of smart cities are demanding efforts to find solutions that prevent theft, loss, unauthorized copy, or the forgery of keys, tokens, and passwords. The Internet of Things (IoT) enabled a large increase of personal data collected and published in the Internet. In this context, biometric authentication has earned more and more place as a security solution because they demand, in general, physical presence, vitality, and they are hard to falsify. Among the biometric signals used in academic or commercial products, we mention: iris, face, the palm of hand, fingerprints, walking, voice, electrocardiogram (ECG), electroencephalogram (EEG), and photoplethysmogram (PPG). This chapter brings the state-of-the-art about the use of several biometric signals in an authentication system, contextualizing the applications already developed and the challenges they faced when coexisting with Smart Cities and the Internet of Things. Each type of biometric signal has its own challenges for data acquisition, cost, feature selection, and method to implement the classification. In addition, this chapter presents a review of the machine learning techniques used in biometric systems. The proper choice of the identification technique and classification directly influences results, costs and also the required amount of input data, and the quality of the captured data.

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“…The NWP approach is based on the physical kinematic equations, which use multiple meteorological variables as input for the prediction model and work by solving the complex mathematical models (Zhou et al, 2011). In ML various concepts can be used such as fuzzy logic (Monfared et al, 2009), neural networks (El-Fouly and El-Saadany, 2008, Daraeepour and Echeverri, 2014, Yesilbodak et al, 2017 and statistical models (Miranda and Dunn, 2006, Jursa and Rohrig, 2008, Zhou et al, 2011. Regression models using neural networks along with techniques like particle swarm optimization, wavelet transform (Martnez-Arellano et al, 2014), REP tree, M5P tree, bagging tree (Kusiak et al, 2009b, Kusiak andZhang, 2010), K-nearest neighbor algorithm (Jursa andRohrig, 2008, Treiber et al, 2016), principal component analysis, moving average models (De Giorgi et al, 2009, Vargas et al, 2010, Markov chain (Kusiak et al, 2009a, Treiber et al, 2016 have been used for wind analysis.…”

Section: Introductionmentioning

confidence: 99%

“…Support Vector Machines (SVM) and its variation, Least Square Support Vector Machines (LSSVM) have also been used for forecasting wind speed (De Giorgi et al, 2009, De Giorgi et al, 2014. Lot of work has been done using ML algorithms for different types of wind predictions, very short term (some seconds to less than 30 minutes ahead), short term (30 minutes to less than 6 hours ahead), medium term (6 hour to less than 1 day ahead) and long term (1 day to less than 1 week ahead) time scales (Yesilbudak et al, 2013, Yesilbodak et al, 2017. Although (Shi et al, 2010) proposed a genetic algorithm-piecewise support vector machine model for short term wind power prediction.…”

Section: Introductionmentioning

confidence: 99%

Comparative Analysis of Lstm, Rf and SVM Architectures for Predicting Wind Nature for Smart City Planning

Harbola

Coors

2019

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Meteorological data and its effect have been the attention of the researchers of the smart city planning for thorough utilization and management of resources, that help in effective government management, convenient public services and sustainable industrial development. Renewable sources of energy like wind, solar, are being integrated into city planning to improve environmental quality. Wind energy is utilized through wind turbines and requires foreknowledge of wind parameters like speed and direction. The aim of this paper is to predict dominant wind speed and direction for time-series wind dataset, that can be incorporated into city planning for selecting suitable sites for wind turbines. This paper proposes three one-dimensional (1D) algorithms using Long Short Term Memory (LSTM), Random Forest (RF) and Support Vector Machine (SVM) for dominant wind speed and direction prediction. The proposed 1D LSTM (1DLSTM), RF (1DRF) and SVM (1DSVM) take successive time values in terms of wind speed and direction as input and predict the future dominant speed and direction, separately. The proposed algorithms are trained and tested using historical wind dataset of Stuttgart and Netherlands respectively. Prediction using 1DLSTM results in total accuracies reaching up to 93.9% and 94.7%, up to 92.8% and 93.8% using 1DSVM and up to 88.7% and 89.3% using 1DRF for speed and direction, respectively. Thus, prediction of wind nature using the proposed algorithms, will give city planners advanced knowledge of wind conditions.

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A novel implementation of kNN classifier based on multi-tupled meteorological input data for wind power prediction

Cited by 80 publications

References 37 publications

Daily Photovoltaic Power Prediction Enhanced by Hybrid GWO-MLP, ALO-MLP and WOA-MLP Models Using Meteorological Information

Daily Photovoltaic Power Prediction Enhanced by Hybrid GWO-MLP, ALO-MLP and WOA-MLP Models Using Meteorological Information

Autenticação usando Sinais Biométricos: Fundamentos, Aplicações e Desafios

Comparative Analysis of Lstm, Rf and SVM Architectures for Predicting Wind Nature for Smart City Planning

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