Data Analysis Techniques for Achieving Energy Cost Avoidance in a Chilled Water District Cooling System

摘要

Increasing availability of metering and sensor data from today's community energy systems are providing new opportunities for understanding their behavior and efficiency. During recent data-driven investigations into the chilled water energy system operations of a university campus, the variety of data analysis techniques employed can be seen as fitting into several categories. This paper outlines four such categories, separated out by intent (i.e., finding better operational settings versus finding operational faults to address) as well as how the energy meter data is used by a practitioner (i.e., as an input to human judgment based on tacit knowledge versus one that relies on an explicit model). Selected examples are provided to elaborate on and compare the meaning of the four data analysis category types via discussion about their intent, usage, and novelty. Moreover, the preliminary attempt to classify data analysis types is a first step towards a future, more comprehensive taxonomy, envisioned as a reference for energy system operators to get the most utility out of their increasingly available metering data.