Transdisciplinary resource planning mechanism for clean energy economy

虽然加拿大的许多社区和机构已经建立了城市环境中碳中和的气候行动计划和框架，但从设计、规划和实施的角度（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）参与的各种利益相关者之间的跨学科合作却非常缺乏。各利益相关者之间透明度的提高被视为清洁能源基础设施发展的障碍和投资者的犹豫。为了彻底改变跨学科合作，本研究计划打算设计一个通用平台，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，研究计划将极大地集中在使用计算机技术的整个过程的自动化上，将各种学科集中在一个保护伞下。除此之外，该研究项目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，研究项目的重点是将人工智能、无人机数据和现有数据库集成在一起，实现邻里能源模型过程的自动化。然后，主题2和主题3将这些社区能源模型分别用于混合清洁能源规划和经济分析，实现先进的逆优化算法（规划自动化）。社会-技术-经济的不确定性目前在文献中还没有得到很大的探讨，因此在主题4中，研究计划建立了各种模型来量化这些不确定性。在量化之后，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将整合基于商业智能（BI）的算法，通过处理大数据集（在前4个主题中获得）生成有价值的输出，生成报告、摘要、仪表板、地图、图表等（按照现代组织实践），可用于投资组织和决策者制定战略和投资决策。不同的主题最终将以基于计算机的界面形式形成，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.