An intelligent framework for short-term multi-step wind speed forecasting based on Functional Networks

Ahmed, Adil; Khalid, Muhammad

doi:10.1016/j.apenergy.2018.04.101

Cited by 63 publications

(26 citation statements)

References 47 publications

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“…The DM [3] test is introduced to demonstrate the performance difference between the proposed hybrid model and the participating comparison models. The essence of DM test is to calculate the confidence level of a variable under a certain confidence degree [45] The application of this theory in this paper is described in detail as follows.…”

Section: Variabilitymentioning

confidence: 99%

Short-Term Wind Speed Forecasting With Principle-Subordinate Predictor Based on Conv-LSTM and Improved BPNN

Chen

Zhang

et al. 2020

IEEE Access

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To improve the accuracy of short term wind speed forecasting, a novel principle-subordinate prediction model based on Conv-LSTM network and BPNN is designed in this paper. The proposed model combines deep learning algorithms and improved neural network to deal with the problem of wind speed forecasting. In this model, the prediction sequence of each subseries is obtained by Conv-LSTM to form more smoother and characteristic series; the BPNN, optimized by MWOA (Modified Whale Optimization Algorithm), is trained with the reconstruction sequence, which processed for the prediction sequence by invert-EMD (inverse empirical model decomposition). Before prediction, singular spectrum analysis (SSA) and complete ensemble empirical model decomposition adaptive noise (CEEMDAN) are adopted to de-noise and decompose the original wind speed data into several subseries. This process is beneficial to improve the ratio of signal to noise and simply the features of wind speed data. In addition, the Conv-LSTM is tested on three datasets, the results proved that data process is advantaged to obtain higher quality wind speed datasets, and convolutional layer can deep extract the characteristics of each subseries, it can facilitate LSTM to make accurate prediction. The final prediction results, compared with other five different models, demonstrate that the proposed model can achieve higher precision. Such as the performance evaluation matrix (MAPE = 2.62%, RMSE = 0.151) are smallest obtained from experiments on three wind different wind speed datasets. INDEX TERMS Short-term wind speed forecasting, deep learning, data mining, modified whale optimization algorithm, convolutional layer, feature extraction, principle-subordinate predictor.

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Section: Variabilitymentioning

confidence: 99%

Short-Term Wind Speed Forecasting With Principle-Subordinate Predictor Based on Conv-LSTM and Improved BPNN

Chen

Zhang

et al. 2020

IEEE Access

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“…These functional models are not constant, unlike common sigmodal forms; however, they keep adapting and changing during the learning process, depending on the nature of the dataset used. Thus, since FNs use these multi-argument functional models, they do not need weights to be assigned to the neurons' connections (unlike neural networks) because the weights' effects inherently exist within these neuron functions [33,34]. The outputs of the neurons are then forced to converge to an equivalent output [35].…”

Section: Functional Network (Fn)mentioning

confidence: 99%

New Computational Artificial Intelligence Models for Generating Synthetic Formation Bulk Density Logs While Drilling

et al. 2020

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Synthetic well log generation using artificial intelligence tools is a robust solution for situations in which logging data are not available or are partially lost. Formation bulk density (RHOB) logging data greatly assist in identifying downhole formations. These data are measured in the field while drilling by using a density log tool in the form of either a logging while drilling (LWD) technique or (more often) by wireline logging after the formations are drilled. This is due to operational limitations during the drilling process. Therefore, the objective of this study was to develop a predictive tool for estimating RHOB while drilling using an adaptive network-based fuzzy interference system (ANFIS), functional network (FN), and support vector machine (SVM). The proposed model uses the mechanical drilling constraints as feeding input parameters, and the conventional RHOB log data as an output parameter. These mechanical drilling parameters are usually measured while drilling, and their responses vary with different formations. A dataset of 2400 actual datapoints, obtained from a horizontal well in the Middle East, were used to build the proposed models. The obtained dataset was divided into a 70/30 ratio for model training and testing, respectively. The optimized ANFIS-based model outperformed the FN- and SVM-based models with a correlation coefficient (R) of 0.93, and average absolute percentage error (AAPE) of 0.81% between the predicted and measured RHOB values. These results demonstrate the reliability of the developed ANFIS model for predicting RHOB while drilling, based on the mechanical drilling parameters. Subsequently, the ANFIS-based model was validated using unseen data from another well within the same field. The validation process yielded an AAPE of 0.97% between the predicted and actual RHOB values, which confirmed the robustness of the developed model as an effective predictive tool for RHOB.

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“…Los pronósticos a múltiples pasos que se obtienen con la estrategia iterativa tienen la desventaja de deteriorarse al acumular errores, ya que solamente se entrena un modelo a un paso; sin embargo, su resultado es comparable al trabajar un horizonte limitado [9].…”

Section: Trabajos Relacionadosunclassified

Pronóstico de la velocidad del viento mediante técnicas de Inteligencia Artificial

Barraza¹,

Rangel²,

Gonzalez-Huitron³

et al. 2019

RCS

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Resumen. Realizar pronósticos de múltiples pasos en el tiempo no es una tarea sencilla; por lo tanto, es importante desarrollar y usar técnicas que simplifiquen esta tarea. Este trabajo prueba modelos de Inteligencia Artificial para tres diferentes estrategias de pronósticos a múltiples pasos: Iterativa, Directa, y Multiple Input-Multiple Output (MIMO). El primer paso consiste en preprocesar las series de tiempo para limpiar (eliminar datos atípicos y ruido) y transformar los datos en secuencias o imágenes; después, se usan tres tipos de Artificial Neural Networks (ANN) para construir modelos de pronóstico: Multilayer Perceptron (MLP), Long Short Term Memory (LSTM) y Convolutional Neural Networks (CNN). Se muestran experimentos con series de tiempo obtenidas en estaciones climáticas en Michoacán, México, donde se puede observar el desempeño que se obtuvo con cada modelo y estrategia.

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An intelligent framework for short-term multi-step wind speed forecasting based on Functional Networks

Cited by 63 publications

References 47 publications

Short-Term Wind Speed Forecasting With Principle-Subordinate Predictor Based on Conv-LSTM and Improved BPNN

Short-Term Wind Speed Forecasting With Principle-Subordinate Predictor Based on Conv-LSTM and Improved BPNN

New Computational Artificial Intelligence Models for Generating Synthetic Formation Bulk Density Logs While Drilling

Pronóstico de la velocidad del viento mediante técnicas de Inteligencia Artificial

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