Data Science and Big Data in Energy Forecasting

Martínez‐Álvarez, Francisco; Troncoso, Alicia; Riquelme, José C.

doi:10.3390/en11113224

Cited by 4 publications

(1 citation statement)

References 13 publications

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“…In the energy sector, big data are coordinated, analyzed, and effectively applied to big data analytics. These analytics provide sophisticated energy services [36,37]. Big data analytics helps generate accurate forecasts of energy consumption, which affect power generation from renewable energy sources and pricing [10,27].…”

Section: Literature Reviewmentioning

confidence: 99%

Sustainable Smart Cities and Energy Management: The Labor Market Perspective

Pilipczuk

2020

Energies

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Nowadays, sustainability is one of the strategic goals of smart cities. They are the essential solution to creating the sustainable future. On the other hand, the smart city is intended to deal with the problems of energy management. This paper examines the influence of the smart sustainable cities concept on energy management from the labor market perspective. The paper fulfills the research gap about energy manager profession transformation considering smart sustainable city concept. The aim of the paper is to create an up-to-date holistic energy manager skill model with a focus on emerging technologies. The skill model reflects the synergy of two methodological approaches: the theoretical and practical approaches. Descriptive statistics are used to present the labor market research results. It is concluded that the core elements of the smart sustainable city concept have an impact on energy management are sustainability and big data. The labor market research also draws differences in the amount and structure of demand on energy manager skills by countries, but at the same time similar skills requirements are found. The skill model of the energy manager profession is built around several main groups associated with specific knowledge, social skills, and behavior skills. The findings of the present research can contribute to knowledge and practice by applying it in the process of developing energy manager competency models in commercial and non-commercial enterprises as well as in education programs and training courses.

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Section: Literature Reviewmentioning

confidence: 99%

Sustainable Smart Cities and Energy Management: The Labor Market Perspective

Pilipczuk

2020

Energies

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Chaotic Time Series Prediction: Run for the Horizon

Gromov

2021

Communications in Computer and Information Science

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Evaluating the latest trends of Industry 4.0 based on LDA topic model

Ozyurt,

Özköse,

Ayaz

2024

J Supercomput

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This study employs the Latent Dirichlet allocation method, a topic modeling technique, to reveal hidden patterns in Industry 4.0 research. The dataset comprises 8584 articles published in the Scopus database from 2011 to the end of 2022. The analysis categorized the articles into 12 distinct topics. The three most prominent topics identified are “Smart Cyber-Physical Systems,” “Digital Transformation and Knowledge Management” and “Data Science in Energy,” respectively. The findings from this topic modeling provide a comprehensive overview for researchers in the field of Industry 4.0, offering valuable insights into current trends and potential future research directions.

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Data Science and Big Data in Energy Forecasting

Cited by 4 publications

References 13 publications

Sustainable Smart Cities and Energy Management: The Labor Market Perspective

Sustainable Smart Cities and Energy Management: The Labor Market Perspective

Chaotic Time Series Prediction: Run for the Horizon

Evaluating the latest trends of Industry 4.0 based on LDA topic model

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