Transdisciplinary resource planning mechanism for clean energy economy

标题：清洁能源经济的跨学科资源规划机制虽然加拿大的许多社区和机构已经建立了气候行动计划和框架，以在城市环境中实现碳中和，但从设计、规划和实施的角度涉及的各个利益攸关方(例如，建筑、城市规划者、能源规划者、工程师、经济学家和政策制定者)之间缺乏跨学科的合作。各利益攸关方之间透明度的提高被视为清洁能源基础设施增长和投资者犹豫不决的障碍。为了彻底改变这一研究计划的跨学科合作，计划设计一个通用平台，供所有利益相关者使用，使用最新的基于计算机的技术，如人工智能、机器学习、高级优化和商业智能。因此，研究计划将主要集中在利用计算机技术实现整个过程的自动化，将不同的学科纳入一个保护伞下。此外，该研究计划还将解决现有清洁能源基础设施设计技术中的各种问题。例如，在主题1中，研究计划的重点是使邻里能源模型的过程自动化，该过程集成了人工智能、无人机数据和现有数据库。然后，主题2和主题3分别使用这些邻域能源模型进行混合清洁能源规划和经济分析，从而实现先进的逆优化算法(规划自动化)。社会-技术-经济不确定性目前在文献中很大程度上没有被探索，因此，在主题4中，研究方案建立了各种模型来量化这些不确定性。在量化后，该研究计划将建立最先进的社区规模的基于Sabatier反应堆的可再生能源存储和其他创新的可再生能源解决方案，以减少不确定性。主题5将集成基于商业智能(BI)的算法，以从大数据集(在前4个主题中获得)生成有价值的输出，方法是对它们进行处理，以生成报告、摘要、仪表板、地图、图表等(根据组织中的现代实践)，可用于投资组织和政策制定者的战略和投资决策。不同的主题将最终以基于计算机的界面的形式形成，从而实现跨学科的清洁能源规划。

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.