Big data analytics for energy management

作为绿色按钮计划的一部分，加拿大和美国的公用事业公司正在尝试通过使客户能够安全地下载其能源使用数据并与第三方共享，使客户更容易获得智能电表数据。这些大数据提供了分析能源消耗模式、识别节能机会和节约能源的机会。 该研究项目的主要目标是设计一个全面的软件框架，用于监测能源消耗和分析智能电表提供的数据，沿着开发与能源相关的软件应用程序。为了实现这些目标，该项目将在能源管理的背景下探索和推进大数据分析。该框架将基于面向服务的体系结构，并将提供数据分析功能作为服务。 此外，由于传统的数据分析方法在满足大数据需求方面面临挑战，传统的机器学习和数据挖掘方法将适用于分布式和/或云环境中的大数据工作。 该项目的成果将通过为能源管理提供通用、灵活和可扩展的框架，使工业合作伙伴伦敦水电公司直接受益。该项目的成果将使能源消费者能够更好地了解他们的能源消耗，确定节省能源和成本的机会，并估计他们未来的能源消耗和成本。 预计该项目还将有助于大数据分析研究的进步，从而使其他领域受益。此外，参与该项目的HQP将获得在智能环境领域使用大数据的宝贵经验，这将使他们对任何物联网或大数据工作相关市场都有价值。

As part of the Green Button initiative, utilities in Canada and the United States are making attempts to make smart meter data more accessible to customers by enabling them to download their energy usage data securely and share it with third parties. These Big Data presents opportunities to analyze energy consumption patterns, identify savings opportunities, and conserve energy. The main objective of this research project is to devise a comprehensive software framework for monitoring energy consumption and analyzing data provided by smart meters along with the development of energy-related software applications. To achieve these goals, this project will explore and advance Big Data analytics in the context of energy management. The framework will be based on Service-Oriented Architecture and will provide data analytics capabilities as services. Moreover, because traditional data analytics methods are facing challenges in addressing Big Data requirements, traditional machine learning and data mining approaches will be adapted for work with Big Data in distributed and/or cloud environments. The results from this project will directly benefit the industrial partner London Hydro by providing a generic, flexible, and expandable framework for energy management. The outcome of this project will provide energy consumers with the ability to understand their energy consumption better, identify opportunities for energy and cost savings, and estimate their future energy consumption and cost. It is also expected that this project will contribute to advancements in Big Data analytics research which can benefit other domains. Furthermore, the HQP involved in this project will gain valuable experience in working with Big Data in the smart environment sector, which will make them valuable to any Internet of Things or Big Data job-related market.