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.

This paper discusses the life-cycle environmental impact of Home Energy Management System (HEMS), in terms of their potential benefits and detrimental impacts. It is the expectation that adapting smart home automation (SHA) would lead to reduced electricity usage in the household and overall environmental advantages. The purpose of this research was to quantify the negative environmental impacts of SHA and balance them with their benefits. The evaluation of SHA has been done by conducting a generic Life cycle assessment study using SimaPro programme and the EcoInvent database. The LCA study concluded that the largest environmental impact of HEMS is the use-phase electricity consumption of home automation devices. The paper concludes that the energy payback time of home automation in term of the electricity consumption of the devices is negative by 1.6 years. The largest part of this is due to the energy consumption of smart plugs. Therefore, the paper concludes that in terms of home automation, we need to find the balance between what we actually need to control and the resulting energy consumption of the control system