Behavioral and Predictive Analytics for Efficient Energy Consumption Management in Smart Grids

智能电网系统的目标之一是通过将最终用户作为智能电网技术的核心参与者来最大限度地提高能源消耗管理计划的效率。虽然已经做出了巨大的努力来促进这一目标，但不幸的是，根据IndEco战略咨询公司最近进行的一项研究，消费者并不是一个核心考虑因素。加拿大自然资源部该研究的结论是，到2030年，智能电网技术“将受到消费者参与不足的限制”。我们假设，行为和预测分析可以提高公用事业公司如何与消费者合作的知识。鉴于智能电表产生的大量消费数据为公用事业公司了解电表两侧的动态提供了前所未有的机会，这一点特别有希望。在消费方面，行为分析技术允许公用事业公司发现可以集成到智能电网技术中的能源使用偏好。在运营方面，预测分析技术允许公用事业公司与消费者进行近乎实时的互动，以促进基于准确预测负荷需求的基础设施规划。该研究计划侧重于与家庭智能电表数据有关的行为和预测分析方面。行为分析作为一种了解家庭能源消耗的方法是相对较新的。预测分析在短期和长期负荷预测方面得到了很好的研究。然而，需要非常短期的[VST]（几秒或几分钟的延迟）预测，重点是能源的立即使用，以使消费者参与近实时的智能电网规划应用。* 该研究计划的长期目标是探索创新的行为和预测分析技术，支持公用事业和消费者采用高效的能源管理计划。具体的短期目标如下。**（1）开发行为分析机制和方法，全面分析家庭能源消耗数据，以更好地促进高效能源管理计划 *（2）开发用于VST能源预测的新预测分析技术，并开发新策略来评估这些技术的性能 *（3）开发创新平台，将数据分析技术与更少的资源限制相结合，并开发新的隐私保护机制，平衡隐私问题和数据使用之间的权衡 * 这项研究计划将提供独特的机会，在加拿大公司高度需求的主题中培训HQP。此外，开发的技术对于寻求促进有利于加拿大经济和环境的能源消耗管理计划的加拿大公用事业至关重要。

One of the objectives of smart grid systems is to maximize the efficiency of energy consumption management programs, by engaging end-users as central players in smart grid technology. Although enormous effort has been undertaken to promote this objective, unfortunately, consumers are not a central consideration according to a recent study conducted by IndEco Strategic Consulting Inc. for Natural Resource Canada. The study concluded that smart grid technology by 2030 "will be limited by weakness in consumer engagement". We hypothesize that behavioral and predictive analytics can advance utilities' knowledge of how to partner with consumers. This is particularly promising given the large volume of consumption data produced by smart meters which provide unprecedented opportunities for utilities to understand the dynamics on both sides of the meter. On the consumption side, behavioral analytics techniques allow utilities to uncover energy usage preferences that can be integrated into smart grid technologies. On the operation side, predictive analytics techniques allow utilities to interact with consumers in near real-time to facilitate infrastructure planning based on an accurate forecast of load demand.******This research program focuses on behavioral and predictive analytics aspects pertaining to household smart meter data. Behavioral analytics as an approach for understanding energy consumption in households is relatively new. Predictive analytics is well studied for short and long-term load forecasting. However, very-short-term [VST] (latency of few seconds or minutes) predictions that focus on the immediate use of energy are required to engage consumers in near real-time smart grid planning applications. ******The long-term objective of this research program is to explore innovative behavioural and predictive analytics techniques that support utilities and consumers in adopting efficient energy management programs. Specific short-term objectives are as follows. ******(1) The development of behavioural analytics mechanisms and methods to analyze comprehensively household energy consumption data to promote efficient energy management programs better ***(2) The development of new predictive analytics techniques for VST energy predictions and the development of new strategies to evaluate the performance of these techniques ***(3) The development of innovative platform to integrate data analytics techniques with fewer resource constraints and the development of new privacy-preserving mechanisms that balance the trade-off between privacy concerns and the use of data******This research program will provide unique opportunities to train HQPs in topics considered highly in-demand by Canadian companies. Also, the developed technologies are critically important for Canadian utilities seeking to promote energy consumption management programs that benefit Canada's economy and environment.**