Forecasting

Ignore Missing Option

March 07, 2018

the first eight selections for the second round of the Grid Resilience and Innovation Partnerships (GRIP) Program funding – specifically for the Grid Innovation Program, which is one of three GRIP funding mechanisms. This investment, totaling $2.2 billion, will support eight projects across 18 states aimed at fortifying America’s power grid against the increasing threats of extreme weather, reducing costs for communities and expanding grid capacity to accommodate growing energy demands. The program is designed to enhance grid resilience and reliability, ensuring the nation’s energy infrastructure is positioned to meet future energy needs.

GRIP Program Overview

Launched by the DOE, the GRIP Program focuses on strengthening the resilience and innovation capacity of America’s power grid. The program’s primary goal is to enhance grid resilience and reduce the impact of natural disasters, cyber threats and other disruptions. By requiring collaboration among industry stakeholders including utilities, government agencies, states, etc., the program aims to ensure a reliable energy supply for communities across the nation. To date, GRIP has allocated $5.7 billion in funding across 65 projects through its first two rounds. The newly selected projects are expected to add approximately 13 gigawatts of grid capacity, enabling more clean energy to reach consumers nationwide. In a MetrixND transformation, numbers are added by joining variables with the “+” sign. Adding three variables is as simple as writing the following expression in the transformation editor formula box.

In addition to a focus on resilience, GRIP funding promotes innovation within the energy sector. It provides financial support for developing and deploying cutting-edge technologies that optimize grid operations, improve energy efficiency and integrate renewable energy sources. These efforts are crucial for the modernization of America’s energy infrastructure. The GRIP program is divided into three funding mechanisms:

Grid Resilience Utility and Industry GrantsDataSource.Variable1 + DataSource.Variable2 + DataSource.Variable3 The complex way to add is using the “sum” function. This function requires inserting the three variables separated by commas (,) as sum function parameters as shown below.

Sum(DataSource.Variable1,DataSource.Variable2,DataSource.Variable3)
Technically speaking, the um function requires an extra five characters to do the same work as the traditional + sign.

So, why does MetrixND include this function?

In the situation where variable has missing values, the calculation using the + sign results in a missing value. In other words, a number plus a missing value equals a missing value.

100 + MISSING = MISSING
While the sum function behaves the same, the “Ignore Missing” option changes the behavior to produce a value. In other words, the sum function with the Ignore Missing option means a number plus a missing value equals a number.

100 + MISSING = 100
To activate the Ignore Missing option, check the Ignore Missing box in the transformation editor as shown below.

The Ignore Missing options works with the following functions
- Sum
- Avg
- Max
- Min
It does not matter whether you use traditional math operators or the functions when data are complete. However, when the dataset has missing values, the functions and Ignore Missing options may be the difference between forecasting a number and forecasting a MISSING.

Complexity has its purposes.

By Mark Quan

Principal Forecast Consultant

Mark Quan est consultant principal en prévisions au sein de la division des prévisions d'Itron. Depuis qu'il a rejoint Itron en 1997, M. Quan s'est spécialisé dans les solutions de prévision énergétique à court et à long terme, ainsi que dans les projets de recherche sur la charge. Quan a développé et mis en œuvre plusieurs systèmes de prévision automatisés pour prédire la demande système du lendemain, les profils de charge et la consommation au détail pour des entreprises aux États-Unis et au Canada. Les solutions de prévision à court terme comprennent des systèmes pour le « Midwest Independent System Operator » (MISO) et le « California Independent System Operator » (CAISO). Les solutions de prévision à long terme comprennent le développement et le soutien des prévisions à long terme (ventes et clients) pour des clients tels que « Dairyland Power » et « Omaha Public Power District ». Ces prévisions comprennent des informations sur l'utilisation finale et les impacts de la gestion de la demande dans un cadre économétrique. Enfin, Quan a participé à la mise en œuvre de systèmes de recherche de charge, notamment chez Snohomish PUD. Avant de rejoindre Itron, Quan a travaillé dans les secteurs du gaz, de l'électricité et de l'entreprise chez Pacific Gas and Electric Company (PG&E), où il a participé à la restructuration du secteur, à la planification de l'électricité et à la planification du gaz naturel. M. Quan est titulaire d'un master en recherche opérationnelle de l'université de Stanford et d'une licence en mathématiques appliquées de l'université de Californie à Los Angeles.

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