Energy Forecasting: A Review and Outlook

Hong, Tao; Pinson, Pierre; Wang, Yi; Weron, Rafał; Yang, Di; Zareipour, Hamidreza

doi:10.1109/oajpe.2020.3029979

Cited by 362 publications

(164 citation statements)

References 116 publications

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“…Finally, let us note that forecasts, no matter how accurate, are of limited use if they cannot be utilized to yield profits [16]. However, measuring the economic value of reducing electricity price forecasting errors-although desirable-is a difficult task, as argued in [45,46]. A model that yields lower errors may not always lead to better trading decisions.…”

Section: Discussionmentioning

confidence: 99%

Importance of the Long-Term Seasonal Component in Day-Ahead Electricity Price Forecasting Revisited: Parameter-Rich Models Estimated via the LASSO

Jędrzejewski¹,

Marcjasz²,

Weron³

2021

Energies

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Recent studies suggest that decomposing a series of electricity spot prices into a trend-seasonal and a stochastic component, modeling them independently, and then combining their forecasts can yield more accurate predictions than an approach in which the same parsimonious regression or neural network-based model is calibrated to the prices themselves. Here, we show that significant accuracy gains can also be achieved in the case of parameter-rich models estimated via the least absolute shrinkage and selection operator (LASSO). Moreover, we provide insights as to the order of applying seasonal decomposition and variance stabilizing transformations before model calibration, and propose two well-performing forecast averaging schemes that are based on different approaches for modeling the long-term seasonal component.

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Section: Discussionmentioning

confidence: 99%

Importance of the Long-Term Seasonal Component in Day-Ahead Electricity Price Forecasting Revisited: Parameter-Rich Models Estimated via the LASSO

Jędrzejewski¹,

Marcjasz²,

Weron³

2021

Energies

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“…In [15], the authors claimed that the generalization ability is a severe dilemma for the employment of DL techniques in wind and solar energy prediction. Reference [165] reports that one of the common issues of the prediction techniques resumes in the poor model universality, which can be referred to the limited data sources. For instance, authors in [166] reports that the lack of data has been known as one of the common failure reasons in load forecasting.…”

Section: Deep Transfer Learningmentioning

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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“…Step8: Error evaluation [40]. The root mean square error (RMSE), the mean absolute error (MAE), and the adjusted Rsquare ( 2 adj R ) are used to evaluate the prediction performance of the model.…”

Section: B the Construction Process Of Hybrid Modelmentioning

confidence: 99%

Short-Term Photovoltaic Power Forecasting Based on VMD and ISSA-GRU

et al. 2021

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Photovoltaic (PV) power generation is affected by many meteorological factors and environmental factors, which has obvious intermittent, random, and volatile characteristics. To improve the accuracy of short-term PV power prediction, a hybrid model (VMD-ISSA-GRU) based on variational mode decomposition (VMD), improved sparrow search algorithm (ISSA) and gated recurrent unit (GRU) is proposed. First of all, the PV time series is decomposed into a series of different subsequences by VMD to reduce the non-stationarity of the original data. Then, the main factors affecting PV power generation are obtained by using the correlation coefficients of Spearman and Pearson, which reduces the computational complexity of the model. Finally, the GRU network optimized by ISSA is used to predict all the subsequences and residual error of VMD, and the prediction results are reconstructed. The results show that the hybrid VMD-ISSA-GRU model has stronger adaptability and higher accuracy than other traditional models. The mean absolute error (MAE) in the whole test set is 1.0128 KW, the root mean square error (RMSE) is 1.5511 KW.INDEX TERMS short-term PV power forecast, variational mode decomposition, improved sparrow search algorithm, GRU neural network.

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Energy Forecasting: A Review and Outlook

Cited by 362 publications

References 116 publications

Importance of the Long-Term Seasonal Component in Day-Ahead Electricity Price Forecasting Revisited: Parameter-Rich Models Estimated via the LASSO

Importance of the Long-Term Seasonal Component in Day-Ahead Electricity Price Forecasting Revisited: Parameter-Rich Models Estimated via the LASSO

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

Short-Term Photovoltaic Power Forecasting Based on VMD and ISSA-GRU

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