Data Driven Optimization for Smart Energy Usage

Screaming Power是一家安大略公司，提供移动的大数据云平台，用于对能源用户进行节能、节约成本和持续能源改进的有效教育。该研究旨在为Screaming Power的公用事业和企业应用开发一种数据驱动的决策方法，以便使用小时计和历史数据实时生成最佳能源使用策略。这种方法的最终目标不是完全自动化决策过程，而是提供实时建议，帮助所有能源用户，特别是管理多个建筑物的企业，以更适应和智能的方式消耗能源，以提高节能和减少碳排放。与传统的需求侧管理研究不同，本文的研究重点是实时提供建议，并具有多种显示模式。此外，还将针对不同的用户偏好和能源价格/使用/天气条件场景提供多种最佳解决方案。研究的预期成果是一份技术报告，解释该方法并证明其有效性，以及实现该方法的计算机代码。这些计算机代码将被集成到Screaming Power的移动的应用程序中，该应用程序将被安大略联邦政府大楼的能源管理人员以及安大略可能超过10万的能源用户使用。新的数据驱动优化方法将有助于系统工程领域，它不仅有利于能源消耗的决策，而且有利于能源生产和温室气体排放跟踪。

Screaming Power is an Ontario company that provides a mobile big data cloud platform for effective education of energy users on conservation, cost savings and continuous energy improvement. The proposed research is to develop a data driven decision-making method for Screaming Power's Utility and Enterprise applications, in order to generate optimal energy usage strategies in real-time using hourly meter and historical data. The ultimate goal of this method is not to fully automate the decision-making procedures, but to provide real-time recommendations that help all energy users and especially enterprises that manage multiple buildings to consume energy in a more adaptive and intelligent way to improve energy conservation and reduce carbon emissions. Different from the traditional demand side management research, the proposed research focuses on providing recommendations in real-time and with a variety of display modes. In addition, multiple optimal solutions will be provided for different user preferences and energy price/usage/weather condition scenarios. The anticipated outcomes of the research are a technical report that explains the method and demonstrates its effectiveness, as well as the computer codes realizing the method. The computer codes will be integrated into Screaming Power's mobile applications that will used by energy managers of federal government buildings in Ontario and potentially over 100,000 energy users in Ontario. The new data driven optimization method will contribute to the field of systems engineering, and it can benefit the decision-making for not only energy consumption, but also energy generation and GHG emissions tracking.