Review of Digital Transformation in the Energy Sector
Abstract
Digitalization has begun as a transformative force within the energy sector, reforming traditional practices and paving the way for enhanced operational efficiency and sustainability. Enabled by key technologies such as smart meters, digitalization embodies a paradigm shift in energy management. Nonetheless, it is crucial to recognize that these enabling technologies are only the catalysts and not the end goal. This paper presents a comprehensive overview of digital services and products in the energy sector, with a specific focus on emerging technologies like AI and Connected Data Spaces. The objective of this review paper is to assess the maturity and adoption levels of these digital solutions, seeking to draw insights into the factors influencing their varying levels of success. This maturity and adoption assessment was carried out by applying a Fuzzy logic approach which allowed us to compensate for the lack of detailed information in current literature. By analyzing the reasons behind high maturity-low adoption and vice-versa, this study seeks to cast light on the dynamics shaping the digital transformation of the energy sector.
Introduction
Since the latter part of 2022, our world has borne witness to an unprecedented surge in the realm of digitalization. Artificial intelligence tools, leveraged by large language models (LLM), have allowed unprecedent interpretative interactive environment creation with the end user and access, processing and data sharing have never been so wide.
The Generative AI field alone, has permitted the creation and growth of digital services in most fields of knowledge, impacting our society from several perspectives, job efficiency, knowledge transfer and acquisition, content creation, code development just to mention a few, with wide uptake and social acceptance. The energy domain is no exception.
Review of Digital Transformation in the Energy Sector: Salvador Carvalhosa, Alexandre Lucas, Camilla Neumann, Andreas Türk
While machine learning, computer vision or deep learning tools have supported many energy-applied models.
from Digital twins, generation/demand forecasting, consumer clustering, network planning tools just to mention a few, recent developments on the Data Spaces area, smart metering roll out, IOT and data AI driven tools, have promoted a new generation of services and tools to several target groups in the energy sector.
