Artificial intelligence (AI) techniques and algorithms are increasingly being utilized in energy and renewable research to tackle various engineering problems. However, a majority of the AI studies in the energy domain have been focusing on solving specific technical issues. There is limited discussion on how AI can be utilized to enhance the energy system operations, particularly the electricity market, with a holistic view. The purpose of the study is to introduce the platform architectural logic that encompasses both technical and economic perspectives to the development of AI-enabled energy platforms for the future electricity market with massive and distributed renewables. A constructive and inductive approach for theory building is employed for the concept proposition of the AI energy platform by using the aggregated data from a European Union (EU) Horizon 2020 project and a Finnish national innovation project. Our results are presented as a systemic framework and high-level representation of the AI-enabled energy platform design with four integrative layers that could enable not only value provisioning but also value utilization for a distributed energy system and electricity market as the new knowledge and contribution to the extant research. Finally, the study discusses the potential use cases of the AI-enabled energy platform.

Artificial intelligence (AI) is emerging to become a highly potential enabling technology for smart buildings. However, the development of AI applications quite often follows a traditional, closed, and product-oriented approach. This study aims to introduce the platform model and ecosystem thinking to the development of AI-enabled smart buildings. The study identifies the needs for a user-oriented digital service ecosystem and business model in the smart building sector in Finland, which aimed to facilitate the launch of scalable businesses and an experiential and dynamic business ecosystem. A multi-method, interpretive case study was applied in the focal ecosystem, with the leading real estate and facility management operators in Northern Europe as part of a Finnish national innovation project. Our results propose an extended comprehensive framework of the 5C ecosystemic model (Connection, Content, Computation, Context, and Commerce) and the possible paths of ecosystem players in the domain of smart building and smart built environment, both theoretically and empirically. The platform-oriented business models are missing, yet desired, by the ecosystem actors. The value chain and ecosystem platforms imply the quest for new (platform) models. Finally, our research discusses the need for new value-chain- and ecosystem-oriented AI development and big data platforms in the future.

Enabling and empowering the diverse energy resources to have active yet efficient participation in the smart grid and energy market is an unrivaled challenge for the energy industry. This research expands the four dominant archetypes of business models in the energy and electricity market, creating a fifth archetype, the “blockchain marketplace”. The contributions of the study are to identify the extant electricity market designs and architectures as centralized and pseudo-decentralized while proposing a fully decentralized architecture enabled by the blockchain. The research contributes to the literature of smart grids and demand-side management and introduces the value configuration/architecture approach for the energy market and business model domains.

Digital twin (DT) emerges as a key concept of the Industry 4.0 paradigm and beyond. However, the current literature lacks focus on humans and human activities as a part of complex system DTs. Acknowledging human aspects in DTs can enhance work performance, well-being, motivation, and personal development of professionals. This study examines emerging requirements for human digital twins (HDTs) in three use cases of industry–academia collaboration on complex systems. The results draw together the overall design problem and four design objectives for HDTs. We propose to combine the machine and human-related aspects of DTs and highlight the need for virtual-to-virtual interoperability between HDTs and machines alike. Furthermore, we outline differences between humans and machines regarding digital twinning by addressing human activities and knowledge-based behavior on systems. Design of HDTs requires understanding of individual professional characteristics, such as skills and information preferences, together with twinning between the physical and digital machine entities and interactions between the human and machine DTs. As the field moves toward including humans as a part of the DT concept, incorporating HDTs in complex systems emerges as an increasingly significant issue.

Observing blending of realities, daily life and gameplay in location-based mobile games is challenging. This study aims at observing this blending by targeting a vast number of images (N = 2432), which have been taken during gameplay of a well-known game, Pokémon GO. Images were collected from social media communities of Pokémon GO players in Twitter, Facebook and Instagram, and analysed using visual and online ethnography. To keep the sample size manageable for analysis, the images were collected only from Nordic Pokémon GO player communities in eight cities during 2016–2018. The findings show that the blending of daily life and gameplay is observable from the shared photos especially from the augmented reality screenshots which is why in this article the context of gameplay, both outdoors and indoors, in Pokémon GO is described in more detail than in previous studies.