Performance Evaluation of Deep Recurrent Neural Networks Architectures: Application to PV Power Forecasting

Chihi, Inès; Sidhom, Lilia; Trabelsi, Mohamed; Refaat, Shady S.; Oueslati, Fakhreddine S.

doi:10.1109/sgre46976.2019.9020965

Cited by 23 publications

(19 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The merit of RNN lies in its internal memory. 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%

“…as shown in Fig. 5(b) [45], [47]. Where x t , σ , and c t denote the input sample at time t, the activation function, and the memory unit, respectively.…”

Section: ) Lstmmentioning

confidence: 99%

“…(b f ,b i ,b o ) and (W f ,W i ,W o ) stands for the bias and weight matrix for each gate, respectively. The symbol is the corresponding multiplication of the elements [45]. A comparative study of some popular nonlinear activation functions is presented in Table 5.…”

Section: ) Lstmmentioning

confidence: 99%

See 2 more Smart Citations

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

et al. 2021

Self Cite

View full text Add to dashboard Cite

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,

show abstract

Section: B Rnnsmentioning

confidence: 99%

“…as shown in Fig. 5(b) [45], [47]. Where x t , σ , and c t denote the input sample at time t, the activation function, and the memory unit, respectively.…”

Section: ) Lstmmentioning

confidence: 99%

See 1 more Smart Citation

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

et al. 2021

Self Cite

View full text Add to dashboard Cite

show abstract

“…In simple words, these techniques analyze the past (input data) to estimate the future (system behavior). PVPF consists of direct and indirect PVPF [8]. On one side, direct PVPF analyses the historical weather data to predict PV power production [2], [8].…”

Section: Introductionmentioning

confidence: 99%

“…PVPF consists of direct and indirect PVPF [8]. On one side, direct PVPF analyses the historical weather data to predict PV power production [2], [8]. On the other side, indirect PVPF uses a dual-stage methodology.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

Self Cite

View full text Add to dashboard Cite

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).

show abstract

On the Pivotal Role of Artificial Intelligence Toward the Evolution of Smart Grids

Refaat¹,

Abu‐Rub²

2021

Smart Grid and Enabling Technologies

View full text Add to dashboard Cite

Performance Evaluation of Deep Recurrent Neural Networks Architectures: Application to PV Power Forecasting

Cited by 23 publications

References 15 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

On the Pivotal Role of Artificial Intelligence Toward the Evolution of Smart Grids

Contact Info

Product

Resources

About