PLS-CNN-BiLSTM: An End-to-End Algorithm-Based Savitzky–Golay Smoothing and Evolution Strategy for Load Forecasting

Refaat, Shady S.; Abu‐Rub, Haitham; Chihi, Inès; Oueslati, Fakhreddine S.

doi:10.3390/en13205464

Cited by 37 publications

(20 citation statements)

References 45 publications

(89 reference statements)

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“…CNNs analyze the hidden patterns using pooling layers for scaling, shared weights for memory reduction, and filters for capturing the semantic correlations by convolution operations in multiple-dimensional data [68]. Thus, CNN architecture acquires a strong potential in understanding spatial features [69], [70]. Despite the CNN potential, CNN model suffers from its disability in capturing special features [70].…”

Section: Cnnsmentioning

confidence: 99%

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Deep Learning in Smart Grid Technology: A Review of Recent Advancements and Future Prospects

et al. 2021

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The current electric power system witnesses a significant transition into Smart Grids (SG) as a promising landscape for high grid reliability and efficient energy management. This ongoing transition undergoes rapid changes, requiring a plethora of advanced methodologies to process the big data generated by various units. In this context, SG stands tied very closely to Deep Learning (DL) as an emerging technology for creating a more decentralized and intelligent energy paradigm while integrating high intelligence in supervisory and operational decision-making. Motivated by the outstanding success of DL-based prediction methods, this article attempts to provide a thorough review from a broad perspective on the state-of-the-art advances of DL in SG systems. Firstly, a bibliometric analysis has been conducted to categorize this review's methodology. Further, we taxonomically delve into the mechanism behind some of the trending DL algorithms. We then showcase the DL enabling technologies in SG, such as federated learning, edge intelligence, and distributed computing. Finally, challenges and research frontiers are provided to serve as guidelines for future work in the futuristic power grid domain. This study's core objective is to foster the synergy between these two fields for decision-makers and researchers to accelerate DL's practical deployment for SG systems. INDEX TERMSSmart grid, deep learning, deep neural networks, edge computing, distributed and federated learning, power systems. NOMENCLATURE Abbreviations DDL Distributed deep learning DL Deep learning DRL Deep reinforcement learning DRN Deep residual network EI Edge intelligence EPS Electric power systems FL Federated learning IoT Internet of things LSTM Long short-term memory neural network The associate editor coordinating the review of this manuscript and approving it for publication was Shadi Alawneh . NN Neural network PVPF Photovoltaic power forecasting D. RESEARCH METHODOLOGY AND SYSTEMATIC REVIEW PROTOCOLStarting from September 2019, the multiple-methods approach was conducted [24]. The collection of the mainstream research papers on SG/AI from Web of Science (WoS), Scopus, IEEE Xplore, Science Direct, and Google scholar was conducted as the largest databases of peerreviewed articles. Only peer-reviewed articles written in English, providing experimental results, and having a unique identifier from the mentioned databases were taken into consideration, including reviews, research articles, patent reports, and conference proceedings. The adopted methodology for conducting this review article employs a combination of keywords categorized into three main groups, specifically, 'Deep Learning', 'Smart Grid', and 'Prediction'. The search methodology focuses on the recent research articles from 2015-2020 to identify the comprehensive statues of the AI applications on SG. The filtering process results in 220 research papers from 600 related papers selected based on their relevance by reading the title, abstract, conclusion,

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Section: Cnnsmentioning

confidence: 99%

“…Thus, CNN architecture acquires a strong potential in understanding spatial features [69], [70]. Despite the CNN potential, CNN model suffers from its disability in capturing special features [70]. The authors in [71] implemented GRU for Long-term load forecasting.…”

Section: Cnnsmentioning

confidence: 99%

Deep Learning in Smart Grid Technology: A Review of Recent Advancements and Future Prospects

et al. 2021

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“…is the window length and K = n − L + 1 is the number of overlapping segments. Next, two types of transformations are computed for the Hankel error matrix: MinMax transformation and Hilbert transformation (HT) [41], [42]. The MinMax transformation is calculated as follows [42]:…”

Section: Proposed Narx-lstm Architecturementioning

confidence: 99%

“…Then, data cleaning from missing and invalid samples and feature importance evaluation are conducted to avoid its adverse impact on the forecasting system accuracy. Finally, the data is normalized with a magnitude range of [0, 1] using Min-Max method [42]. This normalization prevents the features from getting affected by the bad influence of the outliers and data scale while increasing the convergence speed and the performance of the model.…”

Section: Figurementioning

confidence: 99%

An Effective Hybrid NARX-LSTM Model for Point and Interval PV Power Forecasting

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This paper proposes an effective Photovoltaic (PV) Power Forecasting (PVPF) technique based on hierarchical learning combining Nonlinear Auto-Regressive Neural Networks with exogenous input (NARXNN) with Long Short-Term Memory (LSTM) model. First, the NARXNN model acquires the data to generate a residual error vector. Then, the stacked LSTM model, optimized by Tabu search algorithm, uses the residual error correction associated with the original data to produce a point and interval PVPF. The performance of the proposed PVPF technique was investigated using two real datasets with different scales and locations. The comparative analysis of the NARX-LSTM with twelve existing benchmarks confirms its superiority in terms of accuracy measures. In summary, the proposed NARX-LSTM technique has the following major achievements: 1) Improves the prediction performance of the original LSTM and NARXNN models; 2) Evaluates the uncertainties associated with point forecasts with high accuracy; 3) Provides a high generalization capability for PV systems with different scales. Numerical results of the comparison of the proposed NARX-LSTM method with two real-world PV systems in Australia and USA demonstrate its improved prediction accuracy, outperforming the benchmark approaches with an overall normalized Rooted Mean Squared Error (nRMSE) of 1.98% and 1.33% respectively. INDEX TERMS Long Short-Term Memory (LSTM), photovoltaic power forecasting, Nonlinear Auto-Regressive Neural Networks with exogenous input (NARXNN), Tabu Search Algorithm (TSA).

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“…At percent, DL has become one of the most attractive technologies in short-term electrified power load prediction as a result of its excellent end-learning capacity and offers the most advanced forecasting performance [19]. Li et al [20] combined the convolution neural network (CNN), long short-term memory neural network (LSTM), and gated recurrent unit (GRU) algorithm and proposed a prediction model based on deep learning for power load forecasting in Beijing. Massaoudi et al [21] combined the savitzky Golay filter with the bi-directional long-term memory neural network (BiLSTM) to predict the short-term power load.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid System Based on LSTM for Short-Term Power Load Forecasting

et al. 2020

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As the basic guarantee for the reliability and economic operations of state grid corporations, power load prediction plays a vital role in power system management. To achieve the highest possible prediction accuracy, many scholars have been committed to building reliable load forecasting models. However, most studies ignore the necessity and importance of data preprocessing strategies, which may lead to poor prediction performance. Thus, to overcome the limitations in previous studies and further strengthen prediction performance, a novel short-term power load prediction system, VMD-BEGA-LSTM (VLG), integrating a data pretreatment strategy, advanced optimization technique, and deep learning structure, is developed in this paper. The prediction capability of the new system is evaluated through simulation experiments that employ the real power data of Queensland, New South Wales, and South Australia. The experimental results indicate that the developed system is significantly better than other comparative systems and shows excellent application potential.

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PLS-CNN-BiLSTM: An End-to-End Algorithm-Based Savitzky–Golay Smoothing and Evolution Strategy for Load Forecasting

Cited by 37 publications

References 45 publications

Deep Learning in Smart Grid Technology: A Review of Recent Advancements and Future Prospects

Deep Learning in Smart Grid Technology: A Review of Recent Advancements and Future Prospects

An Effective Hybrid NARX-LSTM Model for Point and Interval PV Power Forecasting

A Hybrid System Based on LSTM for Short-Term Power Load Forecasting

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