Szczegóły publikacji
Opis bibliograficzny
Digitalisation of individual energy metering with smart metering and machine learning as enabler to liberalised, decarbonising energy market / Sebastian KILUK // W: Contemporary challenges in management and economy in the area of COVID-19 [Dokument elektroniczny] / ed. by Marcin Suder, Maciej Woźniak. — Wersja do Windows. — Dane tekstowe. — Kraków : Wydawnictwa AGH, 2023. — e-ISBN: 978-83-67427-63-0. — S. 71–90. — Wymagania systemowe: Adobe Reader. — Bibliogr. s. 87–90, Summ.
Autor
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 150150 |
|---|---|
| Data dodania do BaDAP | 2023-12-22 |
| Tekst źródłowy | URL |
| Rok publikacji | 2023 |
| Typ publikacji | fragment książki |
| Otwarty dostęp |  |
| Creative Commons | |
| Wydawca | Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie |
Abstract
During the decarbonization and liberalization of the energy market the roles and possibilities for individuals change. Distributed renewable power generation and new types of tariffs are strongly dependent on and limited by the actions of regulatory bodies and network operators. The hypothesis for role of digitalization in enabling the wider participation of individuals in the energy market include the impact of smart meters, IoT and Industry 4.0 on the economically advantageous integration with the market is examined. However, the knowledge is vague about the impact of the smart meters and real-time data on the availability of the energy utilization efficiency or internal processes fit to the market conditions. Here we show that the real-time energy consumption data concentrated in order to apply machine learning (ML) based automated reasoning is expected by the individuals and organizations. We found that the real-time energy consumption data is perceived by interviewed entities as the most important and valuable asset in transforming their presence on energy market into the way allowing them to stay competitive and benefit from the liberalization and decarbonization. For the technical processes we found that the real-time, low latency consumption data processing promotes expertise and diagnostic knowledge.
