**10. References**

350 Fuzzy Inference System – Theory and Applications

Also in order to compare, the diagram of daily load forecasting curves for fall through both groups is shown in Figures 3 and 4. It should be mentioned that MATLAB software is used

Fig. 4. Power load forecasting for Working days (Saturday to Thursday) of fall with features

Comparing mentioned methods above shows that separation of working days from holidays has a better result in load consumption forecasting. As shown in Figure 5 we can

Fig. 5. Compare of the feature of 2, 7 and 14 day ago with 2, 3 and 4 day ago

for load forecasting and simulation.

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**9. Conclusion and suggestion** 


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**17** 

*Australia* 

**Fuzzy Inference System in** 

**Energy Demand Prediction** 

Fuzzy Inference Systems (FIS) have been widely used in many applications including image processing, optimization, control and system identification. Among these applications, we would like to investigate energy demand modelling. Generally, developing an energy demand model is the challenge of interpreting the historical use of energy in an electric power network into equations which approximate the future use of energy. The developed model's equations are coded and embedded into a processor based system, which predicts the output when a certain type of input occurs. However, the range and quality of prediction is still limited within the knowledge supplied to the model. The major concern about the energy demand modelling is to categorize the type of prediction in short or longterm prediction. In addition, it is crucial to categorize the type of the power network to be modelled. Since identifying the useful historical operation data for setting the model parameters is crucial in modelling, the operation history of the modelled systems must to be analysed. In simple terms, modelling energy demand is the art of identifying the right modelling technique and system's operation parameters. The operation parameters differ based on the type and size of the modelled system. So, taking into consideration why the system is modelled will justify the selection of modelling techniques. Among the reasons for modelling energy demand is managing the use of energy through an Energy Management

For EMS, most of the Artificial Intelligence (AI) methods will lack robustness in terms of their programming and their required computation resources, especially when the EMS is designed to perform on-line quick response tasks. Artificial Neural Network (ANN) might be good candidate among modelling techniques, as there has to be a compromise between robustness of the method and its required computation resources for a specific type of modelling. However, there are a few reasons why ANNs are not suitable for our proposed discussion: their limited adaptability within limited computation resources, their training time and their models' complexity, especially when we deal with highly non-linear systems. Looking at our case study and the reasons this scenario is modelled, we have found that Fuzzy Inference Systems (FIS) are the most appropriate for modelling the energy demand in this specific system, since model development, model parameters, model adaptation capability and computation resources requirements are met. The reason behind choosing FIS

**1. Introduction** 

System (EMS).

Thair Mahmoud, Daryoush Habibi, Octavian Bass and Stefan Lachowics *School of Engineering, Edith Cowan University,* 

