Transdisciplinary resource planning mechanism for clean energy economy

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Title: Transdisciplinary resource planning mechanism for clean energy economy While many communities and institutions in Canada, have established climate action plans and frameworks to carbon neutrality within the urban setting, there is a great lack of transdisciplinary cooperation among various stakeholders involved from design, planning, and implementation perspectives (e.g., architecture, urban planners, energy planners, engineers, economists, and policymakers). The enhancement of transparency between various stakeholders is seen as an obstacle for the clean energy infrastructure growth and hesitation of investors. To revolutionize transdisciplinary cooperation this research program, intend to design a common platform to be used by all stakeholder using latest computer-based technologies such as artificial intelligence, machine learning, advanced optimization, and business intelligence. Hence, the research program will be greatly focused on the automation of the whole process using computer technologies by bringing various disciplines under one umbrella. Apart from this, the research program will also address various issues in the existing techniques of clean energy infrastructure design. For instance, in theme 1 the research program is focused to automate the process of neighborhood energy models integrating artificial intelligence, drone data, and existing databases. These neighborhood energy models are then used by themes 2 and 3 for hybrid clean energy planning and economic analysis, respectively, implementing advanced inverse optimization algorithms (planning automation). Socio-techno-economic uncertainties are currently greatly unexplored in the literature, therefore in theme 4, the research program establishes various models to quantify these. Upon quantification, the research program will establish state-of-the-art community-scale Sabatier reactor-based renewable energy storage and other innovative renewable energy solutions to reduce uncertainties. Theme 5 will integrate business intelligence (BI) based algorithms to generate valuable output from big datasets (obtained in the first 4 themes) by processing them to generate reports, summaries, dashboards, maps, charts, etc. (as per modern practice in organizations) which can be used make strategic and investment decision by investment organizations and policymakers. Different themes will be finally shaped in the form of a computer-based interface that enables transdisciplinary clean energy planning.