Artificial Intelligence for Smart Renewable Energy Sector in Europe—Smart Energy Infrastructures for Next Generation Smart Cities

Abstract

One of the most challenging areas of Future Smart Cities Research is the Smart Energy domain. Critical issues related to optimization, provision of smart customizable networks and sophisticated computational techniques and methods enabled by artificial intelligence and machine learning need further investigation. The renewable energy (RE) is a powerful resource for the future global development in the context of climate change and resources depletion. Artificial intelligence (AI) implies new rules of organizing the activities in order to respond to these new requirements. It is necessary to improve the design of the energy infrastructure, the deployment and production of RE in order to face the multiple challenges that will affect the sector's growth and resilience.. In this research work we exploit the recent developments on the AI adoption for RE sector in European Union (EU). In this respect, we analysed (i) the efficiency of the transformation processes of the RE within the energy chain from Gross Inland Consumption to Final Energy Consumption, (ii) its implications on the structure of renewable energy by source (solar, wind, biomass etc.), (iii) the labour productivity in RE sector compared to the economy as a whole and its correlation with investments level, (iv) the implication of the adoption of AI for RE towards Future Smart Cities Research. The main contribution of this research is the development of a framework for understanding the contribution of AI in the RE sector in Europe. Another bold contribution of this work is the discussion of the implications for Future Smart Cities Research and future research directions.

Impact of Renewable Energy Utilization and Artificial Intelligence in Achieving Sustainable Development Goals

“…Moreover, in developed countries, the power sector has already begun to use AI and related technologies that enable smart grids, smart meters, and the Internet of Things (IoT) devices to interact. Such technology can help enhance energy management, efficiency, transparency, and the use of RE sources 79 . This step helps the RE to achieve many targets (i.e., 100% of SDG 7 targets).…”

Evaluating Recognitive Balanced Scorecard-Based Quality Improvement Strategies of Energy Investments With the Integrated Hesitant 2-Tuple Interval-Valued Pythagorean Fuzzy Decision-Making Approach to QFD

“…Offering flexible payment opportunities on energy bills will also contribute to increase customer satisfaction in this process [33,34]. Şerban and Lytras [35] focused on the European energy sector. According to the results of the analysis conducted using artificial neural networks method, it has been stated that the services to be provided should be shaped according to customer demands.…”

“…Thus, an integrated approach to the quality improvement strategies for energy investments is proposed for measuring the criteria of quality function deployment. [26], [27], [32] Prior Action (TS 2) [30], [36], [37] Partial or Excessive Action (TS 3) [29], [37], [40] Periodic Action (TS 4) [33], [34], [35]…”

“…On the other hand, the correlation between BSC-based factors A and criteria B is shown in the equation (35).…”

“…In this way, it will be possible to reduce the burden of paying very high bills during the seasons with high electricity usage. Şerban and Lytras [35] aimed to evaluate the effectiveness of the energy market in Europe. They identified that the services to be provided should be shaped according to customer demands.…”