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

Massaoudi, Mohamed; Chihi, Inès; Sidhom, Lilia; Trabelsi, Mohamed; Refaat, Shady S.; Abu‐Rub, Haitham; Oueslati, Fakhreddine S.

doi:10.1109/access.2021.3062776

Cited by 75 publications

(23 citation statements)

References 52 publications

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“…Depending on the time domain, there are four distinguished forecasting horizons as illustrated in Fig. 1; specifically, Ultra-Short-Term (USTF) from seconds to one hour [34], Short-Term (STF) with the prediction period from hours to one day, Medium-Term spans up to a month ahead, and long-Term predictions for a month to a year [12]. With the aim of covering the largest number of articles regarding the review topic, possible variations were also employed for this selection.…”

Section: Research Methodology and Systematic Review Protocolmentioning

confidence: 99%

“…However, spot forecasts do not include the uncertainties around the mean value. Therefore, results can be unreliable and misleading in particular scenarios [12].…”

Section: A Point Pv Power Forecastingmentioning

confidence: 99%

“…Next, the predicted output is employed to deduce the PVPG via a predetermined mathematical model. However, it has been reported that direct PVPF leads to more accurate results than indirect PVPF [12], [13]. Nevertheless, this review article has considered both direct and indirect PVPF models.…”

Section: Introductionmentioning

confidence: 99%

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Convergence of Photovoltaic Power Forecasting and Deep Learning: State-of-Art Review

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Deep learning (DL)-based PV Power Forecasting (PVPF) emerged nowadays as a promising research direction to intelligentize energy systems. With the massive smart meter integration, DL takes advantage of the large-scale and multi-source data representations to achieve a spectacular performance and high PV forecastability potential compared to classical models. This review article taxonomically dives into the nitty-gritty of the mainstream DL-based PVPF methods while showcasing their strengths and weaknesses. Firstly, we draw connections between PVPF and DL approaches and show how this relation might cross-fertilize or extend both directions. Then, fruitful discussions are conducted based on three classes: discriminative learning, generative learning, and deep reinforcement learning. In addition, this review analyzes recent automatic architecture optimization algorithms for DL-based PVPF. Next, the notable DL technologies are thoroughly described. These technologies include federated learning, deep transfer learning, incremental learning, and big data DL. After that, DL methods are taxonomized into deterministic and probabilistic PVPF. Finally, this review concludes with some research gaps and hints about future challenges and research directions in driving the further success of DL techniques to PVPF applications. By compiling this study, we expect to help aspiring stakeholders widen their knowledge of the staggering potential of DL for PVPF.

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Section: Research Methodology and Systematic Review Protocolmentioning

confidence: 99%

“…However, spot forecasts do not include the uncertainties around the mean value. Therefore, results can be unreliable and misleading in particular scenarios [12].…”

Section: A Point Pv Power Forecastingmentioning

confidence: 99%

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Convergence of Photovoltaic Power Forecasting and Deep Learning: State-of-Art Review

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“…However, RNNs are prone to the vanishing and exploding gradient dilemma [45]. For real EPSs applications, this shortage makes RNN usually replaced with two types of memory gated structures: Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) [46].…”

Section: B Rnnsmentioning

confidence: 99%

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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“…Recurrent Neural Networks (RNNs) are deeplearning models specifically designed for time-series data [26]. Non-linear Autoregressive Recurrent Neural Network (NARX) has been successfully applied to solar power forecasting [27], [28]. However, conventional RNNs suffer from exploding and vanishing gradients [29].…”

Section: Introductionmentioning

confidence: 99%

Two-Stage Attention Over LSTM With Bayesian Optimization for Day-Ahead Solar Power Forecasting

et al. 2021

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The penetration of PVs into the power grid is increasing day by day, as they are more economical and environment-friendly. However, due to the intrinsic intermittency in solar radiation and other meteorological factors, the generated power from PVs is uncertain and unstable. Therefore, accurate forecasting of power generation is considered one of the fundamental challenges in power system. In this paper, a deep-learning model based on two-stage attention mechanism over LSTM is proposed to forecast a day-ahead PV power. In addition, the Bayesian optimization algorithm is applied to obtain the optimal combination of hyper-parameters for the proposed deep-learning model. Various input features that can affect the PV power generation such as solar radiation, temperature, humidity, snowfall, albedo etc. are considered and their impact with respect to the attention mechanisms on the forecasted PV power is analyzed. The input consists of data from 21 PVs installed at different geographical locations in Germany. The proposed model is compared with state-of-the-art models such as LSTM-Attention, CNN-LSTM, and Ensemble model for day-ahead forecasting. The model is also compared with various single attention mechanisms such as Inputattention, SNAIL, Raffel, and Hierarchical attention etc. The proposed model outperforms the traditional methods in terms of accuracy, hence proving its efficiency. Forecasting Skill (FS) score of the proposed model is 0.4813 whereas 0.4427 is for the Ensemble model, which is the best among other state-of-the-art models. Root Mean Square (RMSE) and Mean Absolute Error (MAE) of the proposed model is 0.0638 and 0.0324 respectively, whereas those of the Ensemble model are 0.0685 and 0.0369 respectively.

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

Cited by 75 publications

References 52 publications

Convergence of Photovoltaic Power Forecasting and Deep Learning: State-of-Art Review

Convergence of Photovoltaic Power Forecasting and Deep Learning: State-of-Art Review

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

Two-Stage Attention Over LSTM With Bayesian Optimization for Day-Ahead Solar Power Forecasting

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