Equipment and procedures have improved over time, but data problems have not disappeared.
Those of us who have been in the industry for many years can look back on the many hours spent fixing data problems, only to have technology eliminate them, or so we think – and create new ones.
Over those years, the reasons for collecting data changed, from PURPA1 compliance, to conservation studies, load management, rate design, cost-of-service studies, demand-side management, technology assessment, billing, profitability, competitive threats and, most recently, load profiling to enable reconciliation of sales from multiple suppliers within a service area.
The issues of data quality have 1Public Utility Regulatory Policy Act of 1978, which required all utilities to collect load research data on major rate classes comprising more than 10% of retail sales.
As we march through the 2000’s, we need to take a fresh look at validation and editing techniques and their implications on the new applications for the data.
Published Paper: Lopes-Valeditaeic2k 2000 Presentation: valedit INTRODUCTION Load Research has had a long and varied history, having been carried on the coattails of a variety of applications since the early days of the 1960’s and 1970’s.
consumption data or events and alarms) and shares this with applications such as billing systems or external data analysis.
Integrated with these systems the MDM is the core enabler of end-to-end smart metering processes.
Problem data and data loss are caused by a combination of factors, including equipment failure, data communications losses, human error, neglect, weather, computer failures and bad luck.(There’s a link to the 2000 AEIC Load Research Conference presentation as well.) Load Research has had a long and varied history, having been carried on the coattails of a variety of applications since the early days of Load Research in the 1960’s and 1970’s.With the collection of load data associated with load research came the need for quality assessment, decisions on how (and whether) to fix problem data and, once data was sufficiently clean, expanding results to the population, whether the load data represented one customer or part of a group representing tens of thousands.Data is made available for market processes or used for commercial propositions based on smart metering data.
The modern system architecture and the use of the ZONOS™ smart energy platform allow high-speed data processing and low cost scaling of the overall solution.
In the past few years, Automatic Meter Reading (AMR) and Automated Metering Infrastructure (AMI) technology and systems have become more prevalent, and many utilities have adopted such systems, or at least initiated pilot programs to test the technology and logistics, including customer response.