AI_QSample_019

During the occupancy of a given tenant, the electricity usage of a house is subject to unexpected fluctuation. One notable example is when the house tenets were outstation for a few days (eg on vacation). in such a case, the trend of electric usage is characterized by a very abrupt drop of electricity, which is then followed by a period of very low variations. On the other hand, a house may experience a sudden increase of electrical usage due to special events or guests, or when the house in undergoing on-site renovations or construction projects.Sometimes, a house may be left vacant as the owner moved to a different house. Moreover, change of tenants may also occur. And even among those houses where the same tenants remains, they may from time to time renovate their houses or even hire building contractors to do so. Such act will permanently change the electric consumption. In such a case, only the most recent cases will be considered.In determining the estimated kWh spent of a month, an electrician will disregard all the incidental cases and will only consider the usual trend, ie when the tenants are carrying on their normal daily lifestyles. The electrical consumption fo much daily lifestyle will hence mostly depends on the temperature. In general, the electric consumption is higher in the colder months of the year. The changing lifestyle of the tenants, though to a lesser extent, also contribute to the permanent change of the electric usage.Our program first work on determining the useful portion of each data by comparing the trends of change across the entire 365 days. First and foremost, our program considers every single number of data as represented. And we have developed out own novel way of similarity measures chich takes even the time of a day into accounts. Such definition of similarity measures enables us to quantitatively determine the differences of consumption pattern across the entire year, yet taking every half hourly data into account. Then, our program will detect all abrupt changes from the pattern it observed, the program will determine on its own, whether such abrupt changes are temporary or permanent. Our program is even able to determine whether such changes are due to even or vacant, and will assign the minimum number of days to be considered a permanent change, based on the nature of such abrupt change. Even for the slight changes of electricity consumption due to gradual change of lifestyle, our program can still detect it via quantitatively measuring the symmetry of those graphs.

在特定租户居住期间，房屋的用电量会出现意外波动。一个典型例子是租户外出（如度假）数日的情况：此时用电量趋势表现为急剧下降，随后进入一段变化极小的时期。另一方面，房屋用电量也可能因特殊活动、访客到来，或正在进行现场装修/建筑工程而突然上升。有时，房屋可能因业主搬至别处而空置；此外，租户更换也可能发生。即便同一租户持续居住的房屋，租户也可能不时进行装修或聘请建筑承包商施工，此类行为会永久性改变用电量。计算月度预估千瓦时（kWh）用量时，电工通常会忽略所有偶然情况，仅考虑租户维持正常日常生活时的常规趋势。因此，日常用电消耗在很大程度上取决于温度；一般而言，一年中较冷月份的用电量更高。租户生活方式的变化虽影响程度较小，但也会导致用电量的永久性改变。

我们的程序首先通过对比全年的变化趋势来确定每个数据的有效部分。首先，程序会考虑所有呈现的单个数据点；其次，我们开发了一种新颖的相似性度量方法，该方法甚至会将一天中的时间因素纳入考量。这种相似性度量的定义使我们能够定量确定全年消费模式的差异，同时兼顾每半小时的数据。然后，程序会从观察到的模式中检测所有突变，并自主判断这些突变是暂时性还是永久性的。程序甚至能判断这些变化是因事件还是空置导致，并根据突变的性质，为永久性变化分配所需考虑的最少天数。即使是因生活方式逐渐变化而导致的用电量轻微变化，程序也能通过定量测量相关图表的对称性来检测到。