| Title | Urban energy efficiency assessment models from an AI and big data perspective: Tools for policy makers |
|---|---|
| ID_Doc | 44595 |
| Authors | Anthopoulos, L; Kazantzi, V |
| Title | Urban energy efficiency assessment models from an AI and big data perspective: Tools for policy makers |
| Year | 2022 |
| Published | |
| Abstract | Although energy efficiency is quite a cliche ' term, it is a topic that attracts an increasing attention the last decade, especially in the context of cities and as a means to address emerging challenges like sustainability and climate change. Several models have been introduced to conceptualize and calculate the urban energy system, and to demonstrate the variants that calibrate the local energy efficiency. Nevertheless, cutting-edge technologies like blockchain, electrical-and even autonomous-vehicles, smart building systems, Artificial Intelligence (AI) and big data etc. are growing within cities and question the identified urban energy efficiency, since they demand enormous amounts of power. In this regard, policy makers are concerned of the emerging technologies' energy efficiency and their impact on the urban energy system and they attempt to introduce corresponding standards for their development. This article focuses on the impact of AI and big data in city's energy efficiency. More specifically, a literature analysis is performed and returned a taxonomy of existing energy efficiency assessment models under the lens of AI and big data. Moreover, the definition of a unified assessment model for AI and big data energy efficiency is approached. |
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