Manual Model Selection
If you want to select a model manually, then you must first of all analyse past consumption data to determine whether a distinct pattern or trend exists according to which you can manually select a model for the system.
Constant requirements pattern
If your past data represents a constant consumption flow, you can then select either the constant model or the constant model with adaption of the smoothing factors. In both cases, the forecast is carried out by firstorder exponential smoothing. When adapting the smoothing parameters, the system calculates different parameter combinations and then selects the optimum parameter combination which is the one which results in the lowest mean absolute deviation.
You have another two possibilities if your past consumption pattern is constant; either the moving average model or the weighted moving average model.
In the weighted moving average model, you can weight individual past consumption values, which means that the system will not give equal value to past data when calculating the forecast values. In so doing, you can influence the calculation so that the most recent consumption values play a more important role in the forecast than the previous periods  as is also the case in exponential smoothing.
Trend requirements pattern
If your past consumption data represents a trend, it makes sense to select either the trend model or the secondorder exponential smoothing model. In the trend model, the system calculates the forecast values by means of the firstorder exponential smoothing procedure.
In the secondorder exponential smoothing models, you can choose between a model with or without parameter optimization.
Seasonal requirements pattern
If your past consumption data represents a seasonal pattern, you can specify the seasonal model you want. The system calculates the forecast values for the seasonal model by means of firstorder exponential smoothing.
Seasonal trend requirements pattern
If your past consumption data represents a seasonal trend pattern, you can select the seasonal trend model you want. The system calculates the forecast values by means of firstorder exponential smoothing.
Irregular requirements pattern
None of the patterns or trends mentioned in the above examples can be recognized in an irregular consumption flow. If the system is to carry out a forecast for an irregular pattern, then it is usually advisable to select either the moving average model or the weighted moving average model.
Forecast Models for Different Requirements Patterns
Requirements pattern 
Forecast model 
Constant 
constant model 
constant model with smoothing factor adaption 

moving average model 

weighted moving average model 

Trend 
trend model (firstorder exponent smoothing) 
Irregular 
no forecast 
moving average model 

weighted moving average model 

Extended forecasting component used: 

Trend 
secondorder exponential smoothing model (with and without parameter optimization) 
Seasonal 
seasonal model 
Seasonal trend 
seasonal trend model 