Identifying essential data to evaluate and monitor energy performance

Abstract

The quantification of energy efficiency projects on a company's energy footprint are important. Energy management projects previously monitored energy consumption by using a ring-fenced approach. Though energy management projects are experiencing a shift towards group-level monitoring. Efficient energy monitoring is imperative for energy management. The use of technology is vital in energy management. Energy consumption meters are used to measure the usage of energy. The energy consumption data measured, is stored in a database. Energy meters do, however, introduce certain challenges. An increase in the availability of data leads to an increase in the volume of data that needs to be evaluated. These big datasets then creates a resource- and time-intensive energy analysis process. Based on these challenges, the three main focus areas of the study are (1) monitoring and reporting, (2) traceability, and (3) monitoring efficiency. This enables energy management by efficiently tracing energy initiatives over various boundaries. The objective of this study is to formulate techniques for establishing which energy consumption meters are the important meters when monitoring the energy usage of a facility. Energy metering structures consist of a Point-of-Distribution (POD), built-up of a grouping of incomer meters. Each incomer meter also consists of a grouping of feeder meters. It is essential that energy monitoring and management are based on quality data. To determine the data quality, various datasets should be compared for validation and verification. If these datasets match-up, an analysis to determine whether energy usage is accounted for throughout the entire facility, should be performed. This energy balance is utilised to understand the energy flow of a system and it also functions as a data quality test. Key performance indicators (KPIs) are created and allocated to each available feeder meter. These KPIs are known as the Absolute Impact KPI and the Volatility KPI. A combination of the two KPI's are used to rank the energy meters. The number of meters that were established to monitor at least 80% of the facility's energy consumption is then counted down in this ranked list. These highlighted feeders are then known as the essential meters. The methodology was applied to two case studies. The first case study (Case Study A) functions in the mining sector and consists of 12 PODs, 37 incomers, and 180 feeders. The identified ("essential") meters constitute 21% of Case Study A's available feeder meters. These significant meters are used to calculate and monitor the energy consumption of the entire facility on a regular basis. When comparing the total estimated energy consumption based on the essential meters, of the current month with the following month a 4% increase is noted at Case Study A. Through this study, ways to determine the essential meters were researched and developed. It was established that only the essential meters can be used to monitor the facility's energy consumption, indicating that the study objective was met. It is recommended that this study be expanded to be able to function alongside real-time energy management software.