AI_QSample_018

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.