Data Mining for Energy Analysis

随着资讯科技和电子技术的出现，用户使用智能电表记录能源消耗的过程变得更加方便。可以对记录的信息进行探索，找出相关的见解。然而，深入研究这些数据会带来一个严重的问题，因为我们有大量的客户，他们在不同的时间点表现出不同的行为。由于需要有效的软件来分析能源相关的大数据，SAP加拿大公司（“SAP”）正在开发SAP能源分析，这是一款用于能源相关问题的新一代实时商业智能软件。SAP能源分析提供了一套工具，可以将从智能电表和任何其他监控系统收集的数据转化为可操作的见解。具体来说，SAP能源分析提供了以交互式仪表板、个性化报告、实时业务活动监控、规范性分析等形式管理能源生产和消耗的成本效益的工具。通过使用SAP Energy Analysis有效地分析客户的消耗数据，可以监控能源生产和消耗，从而通过提高能源效率和管理实践实现更好的负载平衡、负载匹配和负载调度。为此，该公司希望增加SAP能源分析软件的功能，以充分利用收集数据的潜力。为了实现这一目标，拟议的项目旨在研究先进的数据挖掘算法，以便从收集的数据中提取有用的行为模式，这些模式可以提供洞察，跟踪客户在不同时间戳的消费情况，并开发一个自动工具，以任何维度或粒度级别即时汇总和分析客户的能源消耗概况，并预测未来的消费趋势，从而实现有效的能源使用。

The advent of information technology and electronic have facilitated the recording process of energyconsumption of costumers using smart meters. The recorded information can be explored to figure out relevantinsights. However, digging into this data pose a serious problem in the sense we have tremendous amount ofcostumers, which present various behaviour at different time point. Motivated by the need of effective softwarefor analyzing energy-related big data, SAP Canada Inc. ("SAP"), is developing SAP Energy Analysis, a newgeneration of real time business intelligence software for energy-related issues. SAP Energy Analysis offers aset of tools to run the data collected from smart meters and any other monitoring systems into actionableinsights. Specifically, SAP Energy Analysis provides tools to manage the cost effectiveness of energyproduction and consumption in the form of interactive dashboard, personalized report, real time businessactivity monitoring, prescriptive analysis, and more. By effectively analysing customers' consumption datausing SAP Energy Analysis, it becomes possible to monitor the energy production and consumption and, thus,allow better load balance, load matching and load scheduling through improving energy efficiencies andmanagement practices. To this end, the company wants to increase SAP Energy Analysis software capabilitiesto fully harness the potential of the collected data. To achieve this goal, the proposed project aims to investigateadvanced data mining algorithms in order to extract, from the collected data, useful patterns of actions thatoffer insight to track customers' consumption over different time stamps and develop an automatic tool toinstantly aggregate and analyze customers' energy consumption profiles by any dimension or level ofgranularity as well as predicting future consumption trends which in turn permits efficient energy use.