Deep neural networks for ultra-short-term wind forecasting

Đalto, Mladen; Matuško, Jadranko; Vašak, Mario

doi:10.1109/icit.2015.7125335

Cited by 81 publications

(43 citation statements)

References 21 publications

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“…Some good examples of time series forecasting using DNNs include Dalto, who used them for ultra-short-term wind forecasting [15], and Kuremoto et al [16], who used DNNs on the Competition on Artificial Time Series benchmark. In both applications, DNNs outperformed neural networks trained by backpropagation.…”

Section: Prior Workmentioning

confidence: 99%

Short-Term Load Forecasting of Natural Gas with Deep Neural Network Regression †

2018

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Deep neural networks are proposed for short-term natural gas load forecasting. Deep learning has proven to be a powerful tool for many classification problems seeing significant use in machine learning fields such as image recognition and speech processing. We provide an overview of natural gas forecasting. Next, the deep learning method, contrastive divergence is explained. We compare our proposed deep neural network method to a linear regression model and a traditional artificial neural network on 62 operating areas, each of which has at least 10 years of data. The proposed deep network outperforms traditional artificial neural networks by 9.83% weighted mean absolute percent error (WMAPE).

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Section: Prior Workmentioning

confidence: 99%

Short-Term Load Forecasting of Natural Gas with Deep Neural Network Regression †

2018

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“…The obtained results in the above mentioned study prove that Deep Neural Network (DNN) is capable enough to provide better feature space for highly varying and non-stationary data like weather data series of temperature, pressure and wind speed. Related study has been provided in [13] for short term wind prediction and in [14] for load forecasts.…”

Section: Literature Reviewmentioning

confidence: 99%

Meteonowcasting using Deep Learning Architecture

Narejo¹,

Pasero²

2017

ijacsa

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Abstract-The area of deep learning has enjoyed a resurgence on its peak, in almost every field of interest. Weather forecasting is a complicated and one of the most challenging tasks that includes observing and processing huge amount of data. The present paper proposes an effort to apply deep learning approach for the prediction of weather parameters such as temperature, pressure and humidity of a particular site. The implemented predictive models are based on Deep Belief Network (DBN) and Restricted Boltzmann Machine (RBM). Initially, each model is trained layer by layer in an unsupervised manner to learn the non-linear hierarchical features from the input distribution of dataset. Subsequently, each model is retrained globally in supervised manner with an output layer to predict the appropriate output. The obtained results are encouraging. It is found that the feature based forecasting model can make predictions with high degree of accuracy. This implies that the model can be suitably adapted for making longer forecasts over larger geographical areas.

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“…The single layer perceptron is able to solve linearly separable problems. When multiple layers are added into single layer perceptron to solve the complex problem which is not linearly separable this model is called as multilayer perceptron [14].…”

Section: Multilayer Perceptronmentioning

confidence: 99%

“…There are supervised learning algorithms namely recurrent network [12], convolutional neural network [13] and multilayer perceptron [14]. In [14] [17], authors presented multilayer perceptron (MLP) for prediction problems such as breast cancer [14], wind forecasting [14] and heart disease [16] [17].…”

Section: Related Workmentioning

confidence: 99%

Construction Cost Prediction Using Neural Networks

Magdum

Adamuthe

2017

IJSC

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Construction cost prediction is important for construction firms to compete and grow in the industry. Accurate construction cost prediction in the early stage of project is important for project feasibility studies and successful completion. There are many factors that affect the cost prediction. This paper presents construction cost prediction as multiple regression model with cost of six materials as independent variables. The objective of this paper is to develop neural networks and multilayer perceptron based model for construction cost prediction. Different models of NN and MLP are developed with varying hidden layer size and hidden nodes. Four artificial neural network models and twelve multilayer perceptron models are compared. MLP and NN give better results than statistical regression method. As compared to NN, MLP works better on training dataset but fails on testing dataset. Five activation functions are tested to identify suitable function for the problem. 'elu' transfer function gives better results than other transfer function.

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Deep neural networks for ultra-short-term wind forecasting

Cited by 81 publications

References 21 publications

Short-Term Load Forecasting of Natural Gas with Deep Neural Network Regression †

Short-Term Load Forecasting of Natural Gas with Deep Neural Network Regression †

Meteonowcasting using Deep Learning Architecture

Construction Cost Prediction Using Neural Networks

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