Big Data in the Energy and Transport Sectors

Abstract

The energy and transport sectors are currently undergoing two main transformations: digitization and liberalization. Both transformations bring to the fore typical characteristics of big data scenarios: sensors, communication, computation, and control capabilities through increased digitization and automation of the infrastructure for operational efficiency leading to high-volume, high-velocity data. In liberalized markets, big data potential is realizable within consumerization scenarios and when the variety of data across organizational boundaries is utilized.In both sectors, there is a connotation that the term "big data" is not sufficient: the increasing computational resources embedded in the infrastructures can also be utilized to analyse data to deliver "smart data". The stakes are high, since the multimodal optimization opportunities are within critical infrastructures such as power systems and air travel, where human lives could be endangered, not just revenue streams.In order to identify the industrial needs and requirements for big data technologies, an analysis was performed of the available data sources in energy and transport as well as their use cases in the different categories for big data value: operational efficiency, customer experience, and new business models. The energy and transport sectors are quite similar when it comes to the prime characteristics regarding big data needs and requirements as well as future trends. A special area is

Big Data in Smart Energy Systems: A Critical Review

“…Smart energy systems provide more accessible and economical solutions for the transformation into future renewable and sustainable energy solutions integrate the electricity, heating, cooling, industry, buildings, and transportation sector (Lund et al, 2017). Indeed these systems have multiplied in recent years, with sensors, communication, computation, and control capabilities through increased digitization and automation of the infrastructure for operational efficiency leading to high-volume, highvelocity data (Rusitschka and Curry, 2016).…”

A survey of big data and machine learning

“…Reference [5] presents the definition of big data application scenarios through examples in different segments of transportation and energy sectors. Reference [6] proposes a platform which can provide a technical solution to multidisciplinary cooperation of smart grid (SG) monitoring and big data technology.…”

From Centralized Energy Generation and Distribution to Clean Energy Communities: Exploring New Modes of Governance for the Energy Sector

“…There is a strong conceptual turn from the focus on marketing energy assets to energy services (Helms 2016). Digitalization has a pronounced impact at all levels, enabling decentralized energy produc-tion, new digital processes, and innovative customer touchpoints (Rusitschka and Curry 2016;Sioshansi 2016), all of which have implications for the management of energy communities as well. In addition a changing institutional environment is putting pressure on established business models and will further the development of energy communities (Lowitzsch et al 2019).…”