DAP - Demand Analysis and Planning

Library of equipments

For the DSM forecast, equipments are defined with their nominal power. Usage profiles can be associated with the equipment for each sector-region pair. Simultaneity factors are also associated in order to represent the natural (statistical) smoothening of the resulting load curve.

Library of tariffs

Tariffs are represented by polynomials based on the energy consumed (active, capacitive and inductive) and on the peak load. This allows assessing the financial impact of DSM for a utility.

Why 4 methods for demand forecasting?

Depending on the available data and on the time you have, various levels of details can be adopted. DAP allow you to forecast from the simplest level to the most detailed level which represents all equipments: the DSM forecast level.

For each sector of each region, one of the following methods, at least, has to be applied: a further aggregation into a national forecast will be suggested.

  1. Simple Trend Forecast

The simplest forecast of the demand or the peak load is the one based on its past values. In a few clicks it will give you a reasonable or an approximate estimate.

  1. Sector Trend Forecast

A first improvement consists in assigning the future evolution of the demand or the peak load to a “determining” factor: for example the population, the Gross regional product (or the added value), the industrial production, the rate of occupied surface area (m²), etc. The determining factor should then represent the trend of the sector, in terms of power consumption.

  1. Customer Trend Forecast

In this approach, the demand is on the one hand defined by a number of customers and on the other hand by the average customer consumption: each of these variables can be forecasted separately, possibly by linking it to a determining factor which is selected to represent the customer’s trends.

  1. DSM forecast

The DSM forecast is a process in 4 steps:

    1) The forecast of the number of customers

    2) The definition of the equipment in each sector, with the selection of one’s usage profile and simultaneity factors to be associated.

    3) The forecast of the ownership of equipments in each sector.

    4) The computation of the DSM forecast itself

As a result, the user can view his DSM forecast with the share of each equipment. By comparing two DSM forecasts, i.e. a “natural demand” forecast and a “Demand Side Managed forecast”, the user can identify the impact of the DSM project on the peak load, on the consumption and on the utility revenues.

National forecasts

After working with the 4 methods, the forecaster will have various forecasts for a given sector in a given region.

He can then assign to each “sector-region” his best forecast (any type) so that a sum can be computed at the regional level or at the national level. The latter can to take into account losses, due, for example, to the network.


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