ENERGY FORECAST METHODOLOGIES

Understanding and knowledge of the future are essential to good planning and organization. It is therefore necessary to have tools capable of making reliable forecasts. To this are basic energy forecast solutions.

Energy forecast solutions allows, based on past behavior and environmental conditions, predict the behavior of the same element in the future.

Depending on the environment, the future behavior of an element can be governed by stationary casuistry, or dispose of non-periodic behavior.

Depending on the case mix and customer needs, they have different forecasting methodologies to adapt the behavior of information to provide.

 

METODOLOGÍAS DE PREVISIÓN DE ENERGÍA 1

SYMBOLIC PREDICTOR

This forecasting method is an improved combination of techniques:

  • Decomposition in seasonality
  • Exponential Smoothing

The decomposition naturally reflects seasonality allows periodic components of the series. This decomposition is complemented with exceptionalities treatment that takes into account situations that fall outside preset cycles.

The symbolic predictor is well suited to model difficult to explain behaviors, such as the treatment of special events. Its features allow you to make forecasts with little or no history and properly modeling complex behaviors.

Finally it should be noted that the symbolic predictor is extremely fast and you need little information to forecast. Many algorithms to calculate a new forecast, need to be recalculated from all the historical, while symbolic predictor only requires the last update of a few parameters that is specially designed for applications with a large volume of forecasts perform or welfare systems in real time.

 

concept illustration of sustainable energy, solar panels and windmills

ESTIMATED AGGREGATE DEMAND

Aggregate demand forecasting solutions are characterized by:

  • Expert user in business and technology foresight.
  • The data quality is excellent.
  • Quality requirements of the forecast are very high.
  • Reduced volume of data. Office environment.
  • Small number of series (1 to 10).

Product highlights forecast Aggregate Demand Power, which has specialized for forecasting electricity demand time (active or reactive) for aggregate consumption of a consumer group (region or zone).

The forecast product of Aggregate Demand Electric offers the following features for teams planning and operations:

  • All times demand active and reactive energy.
  • Reports for analysis of daily, weekly and monthly demand.
  • Interconnection in real time (optional).
  • Tools that allow the user to enter and modify the values: hourly values, definition of special days and temperature values.
  • Analysis of historical series in which the temperature, season and day of the week for the forecast included and to generate the profile characteristics curves.

 

hydroelectric power station

DISTRIBUTED DEMAND FORECAST

Forecast demand distributed solutions are characterized by:

  • Expert user in business, but need not know the technology foresight.
  • Quality variable data. Historical reconstruction is required in the solution.
  • Quality forecasts are required, but at a reasonable cost.
  • Huge volume of data. Client-Server Architecture or 3-tier (web)
  • Large number of series (between 500 and 5000).

 

BENEFITS

Forecasting methodologies allow:

  • Define short and long term strategies
  • Estimate the impact of varying business variables and trends.
  • By modeling the whole problem, algorithms developed by AIA Group allow:
    • What-if simulations
    • Trend Analysis
    • Sensitivity of the information to business variables.

 

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