Big data solar power forecasting based on deep learning and multiple data sources

Torres, José F.; Troncoso, Alicia; Koprinska, Irena; Wang, Zheng; Martínez‐Álvarez, Francisco

doi:10.1111/exsy.12394

Cited by 53 publications

(26 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…BD computing is classified into two folds: (1) batch processing of massive information of on-disk data with no time constraints. (2) streaming processing of inmemory data in a real-time or short period of time [171]. Several computing frameworks have been proposed to compute BD, such as Hadoop, ComMapReduce, Dryad, Piccolo, and Spark; such systems have the capabilities to scale up DL.…”

Section: E Big Data Deep Learningmentioning

confidence: 99%

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

et al. 2021

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: E Big Data Deep Learningmentioning

confidence: 99%

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

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Short and medium term electricity demand time series forecasting were extensively studied in the literature applying both statistical methods and machine learning techniques. Recently, the machine learning methods have focused on big data [25], especially deep learning [26,27], and ensemble methodologies [28,29].…”

Section: Related Workmentioning

confidence: 99%

Stability of Multiple Seasonal Holt-Winters Models Applied to Hourly Electricity Demand in Spain

2020

Self Cite

View full text Add to dashboard Cite

Electricity management and production depend heavily on demand forecasts made. Any mismatch between the energy demanded with respect to that produced supposes enormous losses for the consumer. Transmission System Operators use time series-based tools to forecast accurately the future demand and set the production program. One of the most effective and highly used methods are Holt-Winters. Recently, the incorporation of the multiple seasonal Holt-Winters methods has improved the accuracy of the predictions. These forecasts, depend greatly on the parameters with which the model is constructed. The forecasters need to deal with these parameters values when operating the model. In this article, the parameters space of the multiple seasonal Holt-Winters models applied to electricity demand in Spain is analysed and discussed. The parameters stability analysis leads to forecasters better understanding the behaviour of the predictions and managing their exploitation efficiently. The analysis addresses different time windows, depending on the period of the year as well as different training set sizes. The results show the influence of the calendar effect on these parameters and if it is necessary or not to update them in order to obtain a good accuracy over time.

show abstract

“…Torres, Troncoso, Koprinska, Wang, and Martínez‐Álvarez () propose a Deep Learning approach, based on feed‐forward neural networks for big data time series, for predicting the electricity power generated by photovoltaic solar systems for the next day (at half‐hourly intervals). As it is well known, solar energy is highly variable as it depends on meteorological conditions (solar radiation, cloud cover, rainfall, and temperature).…”

Section: Contents Of the Special Issuementioning

confidence: 99%

Soft computing applications in the field of industrial and environmental enterprises

Jiménez

Herrero

2019

Expert Systems

View full text Add to dashboard Cite

Big data solar power forecasting based on deep learning and multiple data sources

Cited by 53 publications

References 39 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

Stability of Multiple Seasonal Holt-Winters Models Applied to Hourly Electricity Demand in Spain

Soft computing applications in the field of industrial and environmental enterprises

Contact Info

Product

Resources

About