Week 3: Revolutionizing Water Usage Insights with Unsupervised Learning

This week, I embarked on an exploration of a novel data preprocessing platform tailored for my smart water meter data. My journey began in February 2023, when I first installed my smart water meter. This device has been meticulously logging the date, time, and water meter readings in gallons, producing records akin to: (’12/02/2023 17:36:29′, ‘01018257’).

Initially, my data processing strategy was straightforward. I grouped 14 readings together based on the timing of an appliance’s operation, a method suitable for supervised learning where data is labeled explicitly. However, my ambition to apply k-means clustering—an unsupervised learning technique that operates without predefined labels—prompted a shift in my approach. Given that the duration and volume of water usage significantly hint at the type of appliance in use, I sought a more nuanced data processing method to capture these variations effectively.

To accommodate the unique characteristics of each appliance’s water usage, I refined my preprocessing strategy as follows:

– Different Time Segments for Appliances: Segmentation stops when there’s no change in the water meter reading during a minute, accurately reflecting appliance usage periods.

– Aggregated Water Usage: Calculation of total or average water usage within a specific timeframe, offering insights into consumption patterns.

– Delta Features: The variation in water usage between consecutive windows, highlighting consumption trends.

– Rolling Statistics: These are calculated over a selected number of time windows to smooth out short-term fluctuations.

– Time Features: Encoding the time of day and day of the week to investigate patterns related to time.

A challenging aspect to consider is the behavior of appliances like dishwashers or laundry machines, which intermittently use and stop using water throughout their cycles. My methodology accounts for this by learning to associate such patterns with the specific appliances, despite the temporary cessation of water flow.

In the upcoming week, I plan to test the effectiveness of these adjustments on the performance of unsupervised modeling techniques. This exploration not only promises to enhance the precision of appliance usage identification but also paves the way for innovative water consumption analytics.