The Role of Data Collection, Storage, and Processing in the Intelligent Energy Systems of Tomorrow

Anatoli Paul Ulmeanu (), Adrian Valentin Boicea () and Adrian Vulpe-Grigoraşi ()
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Anatoli Paul Ulmeanu: Polytechnic University of Bucharest
Adrian Valentin Boicea: Polytechnic University of Bucharest
Adrian Vulpe-Grigoraşi: Polytechnic University of Bucharest

A chapter in Handbook of Smart Energy Systems, 2023, pp 1733-1755 from Springer

Abstract: Abstract Data use has come to play nowadays a critical role as far as the power systems are concerned. The fields in which data is employed are multifarious: load forecast, dynamic pricing, energy efficiency, power quality, and so on. On the other hand, we can consider all these fields as being interrelated, in the end the goal being the optimal generation and consumption as well as the optimal operation of the transmission and of the distribution grids. But what happens when in a certain area of the system many of the households are fed through renewable sources? How does this affect the distribution or the transmission networks mentioned previously? In order to be able to provide an answer to these questions, the potential of data collection, storage, and processing has to be taken into consideration. As such, a load forecast based on a convolutional neural network, using data coming from a smart meter in a household, will be carried out in this chapter. Data privacy and communication security will be investigated as well. Another important question, related to data, is the volume. So, is it better to use the concept of Big Data and all the processing algorithms pertaining to it? Are there any important differences between the Big Data used in power engineering and Big Data used in the IT sector? Thus, it is also the aim of this chapter to provide an answer to these questions based on a detailed analysis of the data technologies currently available on the market and of their future development potential.

Keywords: Advanced measurement infrastructures; Big data; Convolutional neural networks; Load forecast; Smart grids (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/978-3-030-97940-9_83

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