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Publications

Authors
Vangelis Marinakis, Themistoklis Koutsellis, Alexandros Nikas and Haris Doukas
Date
Description

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.


Authors
Elissaios Sarmas, Evangelos Spiliotis, Vangelis Marinakis, Georgios Tzanes, John Kaldellis and Haris Doukas
Date
Description

This study introduces an energy management method that smooths electricity consumption and shaves peaks by scheduling the operating hours of water pumping stations in a smart fashion. Machine learning models are first used to accurately forecast the electricity consumed and produced by renewable energy sources on an hourly level. Then, the forecasts are exploited by an algorithm that optimally allocates the operating hours of the pumps with the objective to minimize predicted peaks. Constraints related with the operation of the pumps are also considered. The performance of the proposed method is evaluated considering the case of a Greek remote island, Tilos. The island involves an energy management system that facilitates the monitoring and control of local water pumping stations that support residential water supply and irrigation. Results indicate that smart scheduling of water pumps in a small-scale island environment can reduce the daily and weekly deviation of electricity consumption by more than 15% at no monetary cost. It is also concluded that the potential gains of the proposed approach are strongly connected with the amount of load that can be shifted each day, the accuracy of the forecasts used, and the amount of electricity produced by renewable energy sources.


Authors
Elissaios Sarmas, Evangelos Spiliotis, Vangelis Marinakis, Themistoklis Koutselis and Haris Doukas
Date
Description

Energy efficiency is critical for meeting global energy and climate targets, requiring however significant investments. Due to the lack of mature decision-support systems and the utilization of traditional investment mechanisms that focus on the economical aspects of the energy efficiency projects and neglect their environmental impact, such projects can experience difficulties in being funded. In the interim, the impact of the digitization era is more apparent than ever, as algorithms and data availability and quality have significantly improved. This study aspires to bridge the gap in energy efficiency financing with the development of a data-driven methodology that labels energy efficiency investments based on their expected utility in terms of renovation cost and energy savings. Various machine learning classification methods are deployed and combined through a meta-learning model with the objective to improve overall classification performance and determine the funding that each investment should receive according to its particular characteristics. The proposed methodology is evaluated using a set of 312 projects that have been completed in Latvia. Our results indicate that the meta-learner outperforms all baseline classifiers, effectively identifying projects of high and medium potential and successfully distinguishing low from high potential ones.