AI and Data Democratisation for Intelligent Energy Management

Abstract

Despite the large number of technology-intensive organisations, their corporate know-how and underlying workforce skill are not mature enough for a successful rollout of Artificial Intelligence (AI) services in the near-term. However, things have started to change, owing to the increased adoption of data democratisation processes, and the capability offered by emerging technologies for data sharing while respecting privacy, protection, and security, as well as appropriate learning-based modelling capabilities for non-expert end-users. This is particularly evident in the energy sector. In this context, the aim of this paper is to analyse AI and data democratisation, in order to explore the strengths and challenges in terms of data access problems and data sharing, algorithmic bias, AI transparency, privacy and other regulatory constraints for AI-based decisions, as well as novel applications in different domains, giving particular emphasis on the energy sector. A data democratisation framework for intelligent energy management is presented. In doing so, it highlights the need for the democratisation of data and analytics in the energy sector, toward making data available for the right people at the right time, allowing them to make the right decisions, and eventually facilitating the adoption of decentralised, decarbonised, and democratised energy business models.

Innovación y gobernanza para el desarrollo sustentable en México

“…Con la utilización de la ia en el sector energético destacan las aplicaciones en la predicción y/u optimización del consumo o demanda de energía eléctrica, en la minimización de costos y la reducción de picos derivados de la operación de componentes o máquinas para lograr un equilibrio entre la demanda de energía y la producción de energía durante los períodos de más alta demanda; y la flexibilidad para garantizar el equilibrio entre la demanda y la oferta. (Marinakis, 2021;Mohamad et al, 2020;Salem et al, 2016;Lughofer y Sayed-Mouchaweh, 2019).…”

Using Blockchain and Artificial Intelligence in Energy Management as a Tool to Achieve Energy Efficiency

“…Makala et al (2020) defined possible constraints: a feasible lack of the knowledge needed to understand the specifics of power systems; dependence on cellular technologies limits the potential of Artificial Intelligence in rural and other underserved areas of many emerging markets, particularly in low-income countries; the digital transformation of the electricity network has turned it into a target for hackers; integrating different data sources or experiencing a low volume of data for machine learning models to learn from; absence of understanding of Artificial Intelligence-based models inner workings nor how they were developed, which can constitute security risk (Wang et al, 2022). Marinakis et al (2021) allocated access to Artificial Intelligence enablers, access to human capital, and an AI-skilled talented workforce as major challenges that may reduce the speed with which AI is adopted and thus limit the economic potential of energy stakeholders. Therefore, there is a need to democratize the data and analytics in the energy sector to allow people to be aware of the data to be able to make the right decisions.…”

An end-to-end approach for scalable real time Anomaly detection in smart buildings

“…It is worth mentioning that there are several existing specifications for data-driven initiatives and underlying B2B reference architectures at the interplay among smart buildings, AI, IoT, Big Data, smart energy grids, industry / manufacturing, including IFC [47], OGC CityGML [48], BDVA SRIA4.0 [49], COSMAG [50], FIWARE Smart Energy Reference Architecture [51], IDSA data sovereignty conceptual architecture [52] and IoT/edge AIOTI [53] High Level Architecture, towards a living Reference Architecture specifically tailored to the buildings value chain. Moreover, indicative architectures and frameworks for AI and data democratization for intelligent energy management are presented at [54][55][56][57]. However, all the aforementioned initiatives are too generic including many components that are not useful for the specific use case.…”