I think of MetrixLT as a fancy load shape manipulator and calculator. While the software was originally designed to calibrate load shapes to monthly energy forecasts, newer features of MetrixLT allow users to calculate normal weather (including Rank and Average methods), add and subtract load shapes, and perform top-down calibrations of hourly loads. Within the myriad of features, I’ve found a hidden gem that helps me forecast peak loads. MetrixLT builds my monthly peak database.
The foundation for the monthly peak model is historic monthly peaks and the weather associated with those peaks. Monthly peaks are obtained from historic hourly temperatures. Once the peaks are identified, the dates of the peaks must be used to obtain the associated weather. Instead of culling through complex Excel formulas, MetrixLT creates this database from daily peak data in a single transformation.
An example of the results is shown below. In this figure, the January 2005 peak is 900 MW. The temperature that produced the 900 MW peak is 16.42 degrees. Likewise, the temperature on the day before peak is 33.13 degrees.
While this figure shows the temperature and prior day temperatures associated with the monthly peak, MetrixLT can provide any daily condition associated with the peak, such as dew point, wind speed or demand response estimates.
To create a monthly peak database, use the Frequency Transformation in MetrixLT. This transformation converts data of a different periodicity into monthly data. Begin by creating the Frequency Transformation and setting the frequency to “Monthly” as shown below.
Within this Transformation, insert variables you want shown in the peak database. In this example, three variables are included.
1. Monthly Peak. The monthly peak value is obtained by creating the variable and placing the daily peak series in the “Source” box. Assign the method “Maximum” and Transform will return the monthly peak value.
2.Temperature on the day of the Peak. The temperature on the peak day is obtained by creating another variable and placing the daily temperature series in the “Source” box. Assign the “Coincident Max” method and insert the daily peak series in the Coincident Max box as shown below. This transformation will return the daily temperature coincident with the monthly peak value.
3. Temperature on the day before the Peak. The temperature on the day before the peak is obtained with another variable. In this variable, assign the daily temperature series as the “Source”, select the “Coincident Max” method, and assign the daily peak series in the “Coincident Max” box. For this variable, assign “Coin Lag” the value of “1”. This transformation returns the daily temperature coincident with the monthly peak value, lagged one day.
While the three variables in this example result in the database shown at the top, the example is easily extended to obtain temperatures two days before the peak or daily wind speed on the peak day by defining more variables. Once all the desired variables are included in the monthly peak database, simply export the data, import the values into MetrixND, and build the monthly peak model.
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.
