Random vector functional link neural network based ensemble deep learning for short-term load forecasting

Gao, Ruobin; Du, Liang; Suganthan, Ponnuthurai Nagaratnam; Zhou, Qin; Yuen, Kum Fai

doi:10.1016/j.eswa.2022.117784

Cited by 48 publications

(12 citation statements)

References 41 publications

(52 reference statements)

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“…Finally, for more works related to the load forecasting, refs. [53][54][55][56][57][58][59][60][61][62][63][64][65][66][67][68][69][70] A dataset can be created to train the deep learning-based application (e.g., the ANN) to forecast the values of the loads. Then, based on Figure 4, the current values and the historical values of the desired inputs can be used to predict the future values of power consumption in a cyber-physical microgrid using the trained ANN.…”

Section: Deep Learning-based Load Forecastingmentioning

confidence: 99%

Deep Learning for Forecasting-Based Applications in Cyber–Physical Microgrids: Recent Advances and Future Directions

et al. 2023

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Renewable energy resources can be deployed locally and efficiently using the concept of microgrids. Due to the natural uncertainty of the output power of renewable energy resources, the planning for a proper operation of microgrids can be a challenging task. In addition, the information about the loads and the power consumption of them can create benefits to increase the efficiency of the microgrids. However, electrical loads can have uncertainty due to reasons such as unpredictable behavior of the consumers. To exploit a microgrid, energy management is required at the upper level of operation and control in order to reduce the costs. One of the most important tasks of the energy management system is to satisfy the loads and, in other words, develop a plan to maintain equilibrium between the power generation and power consumption. To obtain information about the output power of renewable energy resources and power consumption, deep learning can be implemented as a powerful tool, which is able to predict the desired values. In addition, weather conditions can affect the output power of renewable energy-based resources and the behavior of the consumers and, as a result, the power consumption. So, deep learning can be deployed for the anticipation of the weather conditions. This paper will study the recent works related to deep learning, which has been implemented for the prediction of the output power of renewable energy resources (i.e., PVs and wind turbines), electrical loads, and weather conditions (i.e., solar irradiance and wind speed). In addition, for possible future directions some strategies are suggested, the most important of which is the implementation of quantum computing in cyber–physical microgrids.

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Section: Deep Learning-based Load Forecastingmentioning

confidence: 99%

Deep Learning for Forecasting-Based Applications in Cyber–Physical Microgrids: Recent Advances and Future Directions

et al. 2023

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“…The typical hybrid model is TEI@I complex system research methodology (Wang et al, 2005) which combines decomposition technology and prediction model. In the decomposition part, the main features of the time series are identified and extracted through decomposition technology, and a series of modal components of the complex price series are obtained, which reduces the complexity of the data series (da Silva et al, 2021;Gao et al, 2022). In the prediction part, the prediction model is used to predict the different components.…”

Section: Introductionmentioning

confidence: 99%

Multi-step carbon price forecasting based on a new quadratic decomposition ensemble learning approach

Zhang

Tang

2023

Front. Energy Res.

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Numerous studies show that it is reasonable and effective to apply decomposition technology to deal with the complex carbon price series. However, the existing research ignores the residual term containing complex information after applying single decomposition technique. Considering the demand for higher accuracy of the carbon price series prediction and following the existing research path, this paper proposes a new hybrid prediction model VMD-CEEMDAN-LSSVM-LSTM, which combines a new quadratic decomposition technique with the optimized long short term memory (LSTM). In the decomposition part of the hybrid model, the original carbon price series is processed by variational mode decomposition (VMD), and then the residual term obtained by decomposition is further decomposed by complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN). In the prediction part of the hybrid model, least squares support vector machine (LSSVM) is introduced, and LSSVM-LSTM model is constructed to predict the components obtained by decomposition. The empirical research of this paper selects two different case data from the European Union emissions trading system (EU ETS) as samples. Taking the results of Case Ⅰ in the 1-step ahead forecasting scenario as an example, the prediction evaluation indexes eMAPE, eRMSE and R2 of the VMD-CEEMDAN-LSSVM-LSTM hybrid model constructed in this paper are 0.3087, 0.0921 and 0.9987 respectively, which are significantly better than other benchmark models. The empirical results confirm the superiority and robustness of the hybrid model proposed in this paper for carbon price forecasting.

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“…Unlike the gradient-based neural networks which need iterative parameter updating, randomized neural networks [28,29], represented by random vector functional link networks (RVFL) [30], provide a simple but efficient learning scheme known as pseudoinverse learning [31]. In an RVFL network, the hidden layers serve as an important part, but their parameters can be generated randomly and kept fixed during training.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Graph Convolutional RVFL Network for Hyperspectral Image Classification

Zhang,

Cai,

Liu

et al. 2023

Remote Sensing

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Graph convolutional networks (GCN) have emerged as a powerful alternative tool for analyzing hyperspectral images (HSIs). Despite their impressive performance, current works strive to make GCN more sophisticated through either elaborate architecture or fancy training tricks, making them prohibitive for HSI data in practice. In this paper, we present a Graph Convolutional RVFL Network (GCRVFL), a simple but efficient GCN for hyperspectral image classification. Specifically, we generalize the classic RVFL network into the graph domain by using graph convolution operations. This not only enables RVFL to handle graph-structured data, but also avoids iterative parameter adjustment by employing an efficient closed-form solution. Unlike previous works that perform HSI classification under a transductive framework, we regard HSI classification as a graph-level classification task, which makes GCRVFL scalable to large-scale HSI data. Extensive experiments on three benchmark data sets demonstrate that the proposed GCRVFL is able to achieve competitive results with fewer trainable parameters and adjustable hyperparameters and higher computational efficiency. In particular, we show that our approach is comparable to many existing approaches, including deep CNN models (e.g., ResNet and DenseNet) and popular GCN models (e.g., SGC and APPNP).

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Random vector functional link neural network based ensemble deep learning for short-term load forecasting

Cited by 48 publications

References 41 publications

Deep Learning for Forecasting-Based Applications in Cyber–Physical Microgrids: Recent Advances and Future Directions

Deep Learning for Forecasting-Based Applications in Cyber–Physical Microgrids: Recent Advances and Future Directions

Multi-step carbon price forecasting based on a new quadratic decomposition ensemble learning approach

An Efficient Graph Convolutional RVFL Network for Hyperspectral Image Classification

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