Exploiting Deep Learning for Wind Power Forecasting Based on Big Data Analytics

Mujeeb, Sana; Alghamdi, Turki Ali; Ullah, Sameeh; Fatima, Aisha; Javaid, Nadeem; Saba, Tanzila

doi:10.3390/app9204417

Cited by 75 publications

(26 citation statements)

References 47 publications

(108 reference statements)

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“…A best practice for short-term forecasting has been suggested by Giebel and Kariniotakis [53]. Mujeeb et al [54] proposed a demand management scheme and a wind power forecasting method using big data-driven wind power forecasting. They employed a deep-learning technique to predict the day-ahead wind power at the New England's wind farm located in Maine, USA.…”

Section: Artificial Intelligence Methodsmentioning

confidence: 99%

Using Artificial Intelligence to Predict Wind Speed for Energy Application in Saudi Arabia

Brahimi

2019

Energies

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Predicting wind speed for wind energy conversion systems (WECS) is an essential monitor, control, plan, and dispatch generated power and meets customer needs. The Kingdom of Saudi Arabia recently set ambitious targets in its national transformation program and Vision 2030 to move away from oil dependence and redirect oil and gas exploration efforts to other higher-value uses, chiefly meeting 10% of its energy demand through renewable energy sources. In this paper, we propose the use of the artificial neural networks (ANNs) method as a means of predicting daily wind speed in a number of locations in the Kingdom of Saudi Arabia based on multiple local meteorological measurement data provided by K.A.CARE. The suggested model is a feed-forward neural network model with the administered learning technique using a back-propagation algorithm. Results indicate that the best structure is obtained with thirty neurons in the hidden layers matching a minimum root mean square error (RMSE) and the highest correlation coefficient (R). A comparison between predicted and actual data from meteorological stations showed good agreement. A comparison between five machine learning algorithms, namely ANN, support vector machines (SVM), random tree, random forest, and RepTree revealed that random tree has low correlation and relatively high root mean square error. The significance of the present study relies on its ability to predict wind speeds, a necessary prerequisite to executing sustainable integration of wind power into Saudi Arabia’s electrical grid, assisting operators in efficiently managing generated power, and helping achieve the energy efficiency and production targets of Vision 2030.

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Section: Artificial Intelligence Methodsmentioning

confidence: 99%

Using Artificial Intelligence to Predict Wind Speed for Energy Application in Saudi Arabia

Brahimi

2019

Energies

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“…It is required to incorporate deep learning techniques such as Convolutional Neural Network (CNN) and LSTM. These techniques are considered as the building blocks of deep learning models [23], [31]. Various variants of these building blocks have been proposed in order to enhance flexibility in the network, i.e., BiLSTM [24].…”

Section: Related Workmentioning

confidence: 99%

“…Keeping this in mind, different electricity load forecasting methods have been developed over the past few years. Starting from conventional neural networks techniques like ANN, CNN, etc., [20]- [22], focus has been shifted towards hybrid and other meta-heuristic techniques like CNN-LSTM, EMD, etc., [23]- [27]. Conventional techniques perform well up to a certain limit.…”

mentioning

confidence: 99%

ELS-Net: A New Approach to Forecast Decomposed Intrinsic Mode Functions of Electricity Load

et al. 2020

Self Cite

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The significance of electricity cannot be overlooked as all fields of life like material production, health care, educational sector, etc., depend upon it to render consistent and high-quality services, increase productivity and business continuity. To this end, energy operators have experienced a continuous increasing trend in the electricity demand for the past few decades. This may cause many issues like load shedding, increased electricity bills, imbalance between supply and demand, etc. Therefore, forecasting of electricity demand using efficient techniques is crucial for the energy operators to decide about optimal unit commitment and to make electricity dispatch plans. It also helps to avoid wastage as well as the shortage of energy. In this study, a novel forecasting model, known as ELS-net is proposed, which is a combination of an Ensemble Empirical Mode Decomposition (EEMD) method, multi-model Ensemble Bi Long Short-Term Memory (EBiLSTM) forecasting technique and Support Vector Machine (SVM). In the proposed model, EEMD is used to distinguish between linear and non-linear intrinsic mode functions (IMFs), EBiLSTM is used to forecast the non-linear IMFs and SVM is employed to forecast the linear IMFs. Using separate forecasting techniques for linear and non-linear IMFs decreases the computational complexity of the model. Moreover, SVM requires low computational time as compared to EBiLSTM for linear IMFs. Simulations are performed to examine the effectiveness of the proposed model using two different datasets: New South Wales (NSW) and Victoria (VIC). For performance evaluation, Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) are used as performance metrics. From the simulation results, it is obvious that the proposed ELS-net model outperforms the start-of-the-art techniques, such as EMD-BILSTM-SVM, EMD-PSO-GA-SVR, BiLSTM, MLP and SVM in terms of forecasting accuracy and minimum execution time.

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“…The wavelet packet transform (WPT) is an extension of the WT, which provides a complete level-by-level decomposition of the signal [9]. WPT has become a popular signal processing method, which has been applied in various fields, such as in the medical industry [10], wind industry [11,12], electric power industry [12], condition monitoring and fault diagnosis of mechanical systems and structures [13][14][15][16][17][18], fatigue damage analysis of composites [19], and many more. The WPT is a signal analysis tool that has the feature of time-frequency resolution.…”

Section: Introductionmentioning

confidence: 99%

Guidelines for Impact Echo Test Signal Interpretation Based on Wavelet Packet Transform for the Detection of Pile Defects

Kim¹,

Mission²,

Dinoy³

et al. 2020

Applied Sciences

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Nonlinear amplification is typically done on velocity signals from low-strain pile integrity tests to enhance weak echoes and superimpose any peak reflections. This conventional method may sometimes fail to untangle the hidden information within the signal that is obscured by the presence of noise. In this study, a pile defect identification system based on the conventional nonlinear amplification method and the wavelet packet transform (WPT) was proposed to easily detect the presence of any geometric or material defects by identifying feature parameters. Diagnostic rules, which have been lacking in the literature, were presented to serve as a guide in interpreting decomposed signals and in analyzing various characteristics of peak waveforms that are associated with certain types of defects. In this study, the finite element method was used to simulate the impact echo test of nine cases of defective piles. To verify the proposed scheme, six data sets of the nine cases of defective piles were made, in which a total of 54 piles were analyzed. The results of the study showed that the identification method based on WPT could detect defects 87.04% of the time compared to the conventional method, which only detected defects 64.81% of the time.

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Exploiting Deep Learning for Wind Power Forecasting Based on Big Data Analytics

Cited by 75 publications

References 47 publications

Using Artificial Intelligence to Predict Wind Speed for Energy Application in Saudi Arabia

Using Artificial Intelligence to Predict Wind Speed for Energy Application in Saudi Arabia

ELS-Net: A New Approach to Forecast Decomposed Intrinsic Mode Functions of Electricity Load

Guidelines for Impact Echo Test Signal Interpretation Based on Wavelet Packet Transform for the Detection of Pile Defects

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