Читать книгу Data Mining and Machine Learning Applications - Группа авторов - Страница 55

Learning identical models for each tenant could reveal the individual level’s fundamental motivating led components. Nevertheless, it may be common for different individuals to have different inclinations and not to carry on likewise. Subsequently, expanded to a network level, a bunching investigation might gather inhabitants into a few designs of trigger behavior. The most educational list of capabilities, with its coefficients, is omitted from the yield of the measured relapse model. All the key-driven elements fall into two classifications: time-related components including month, non-weekend day/weekend, hour-day data, and condition-related variables including indoor temperature, relative humidity, CO₂ emphasis, and outside climate information. According to these two measures and with essential resizing, Figure 2.12 may address the tenants involved in the study. The flat hub speaks to the significance of indoor condition factors in determining the actions of tenants, while the vertical hub speaks to the significance of time-related factors.

Figure 2.12 Cause patterns of ventilation system operations.

K-Means calculation shows three distinct kinds of tenants:

 – Indoor condition touchy inhabitants (plotted in star): 2, 4, 6, 8

 – Time delicate tenants (plotted in the cross): 7, 9

 – Mixed sort inhabitants (plotted in specks): 1, 3, 5, 10.

The unpredictability of inhabitants’ conduct cause example could be seen from the information mining results. The Indoor condition touchy tenants are bound to cooperate with their ventilation control board when they feel somewhat unsatisfied about the indoor solace, while the time-delicate inhabitants are bound to carry on with fixed schedules (e.g., when they wake up or return from work and so forth, they modify the ventilation). There are likewise a few people in the middle of, as blended kind tenants, their practices are affected impressively by the two elements in a similar time.