Data Mining for Energy Analysis

信息技术和电子技术的出现促进了使用智能电表的客户的能源消耗记录过程。记录的信息可以被探索，以找出相关的见解。然而，挖掘这些数据带来了一个严重的问题，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。出于对分析能源相关大数据的有效软件的需求，SAP Canada Inc.（“SAP”），正在开发SAP能源分析，新一代的真实的时间商业智能软件的能源相关问题。SAP Energy Analysis提供了一系列工具，可将从智能电表和任何其他监控系统收集的数据转化为可付诸行动的洞察。具体来说，SAP Energy Analysis提供了多种工具，可用于管理能源生产和消耗的成本效益，包括交互式仪表板、个性化报告、真实的业务活动监控、规范性分析等。通过使用SAP Energy Analysis有效分析客户的能耗数据，可以监控能源生产和消耗，从而通过提高能效和管理实践，实现更好的负载平衡、负载匹配和负载调度。为此，该公司希望增强SAP Energy Analysis软件的功能，以充分利用所收集数据的潜力。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，以提取，从收集的数据，有用的行动模式，提供洞察力，以跟踪客户在不同时间戳的消费，并开发一个自动工具，以即时聚合和分析客户的能源消耗概况的任何维度或粒度级别，以及预测未来的消费趋势，这反过来又允许有效的能源使用。

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.