**7. The proposed method for power consumed load forecasting**

Since fuzzy methods and systems were presented for using in different applications, researchers noticed that making a fuzzy powerful system is not a simple work. The reason is that finding suitable fuzzy rules and membership functions is not a systematic work and mainly requires many trails and errors to reach to the best possible efficiency. Therefore the idea of using learning algorithms was proposed for fuzzy systems. Meanwhile learning of fuzzy network proposed them as the first goals for being unified in fuzzy methods in order to make the development and usage process of fuzzy systems automatic for different applications. Function estimation by using the learning methods is proposed in neural networks and neural-fuzzy networks.

In the suggested methods we forecast load consume and its improvement by the help of the offered method. One of the famous neural-fuzzy systems for function estimation is ANFIS model. We used this system for power consumed load forecasting in this paper too, but with this difference that we used one separate adaptive neural-fuzzy system for each season of the year. Although at the time of training these systems data overlapping is considered, because data of each season of the year is not completely independent and there is some similarities between the first days of a season with its previous season regarding the amount of load consumption. Figure 2 shows the diagram of multi adaptive neural-fuzzy system (multi ANFIS).

A Multi Adaptive Neuro Fuzzy Inference System for

with 2, 3, and 4 day ago

with 2, 7, and 14 day ago

consumption forecasting.

of 2, 3, and 4 day ago

Short Term Load Forecasting by Using Previous Day Features 349

Mean Absolute Percentage Error (MAPE) is used for studying the performance of every mentioned method with the data of related test. MAPE is determined by following relation:

MAPE= 1/N ( ∑ ����

Table 2. Power load consumption forecasting for the working days (saturday to thursday)

Table 3. Power load consumption forecasting for the working days (saturday to thursday)

As it is obvious of the above Tables, making working days separate from holidays with using previous days features (2, 7,and 14 day ago) yields a better result, in load

Fig. 3. Power load forecasting for Working days (Saturday to Thursday) of fall with features

�

**MAPE for working days load consumption forecasting** 1.5409 Spring 2.1869 Summer 2.4575 Fall 1.5116 Winter

**MAPE for working days Load consumption forecasting** 0.9602 Spring 0.8568 Summer 1.1392 Fall 1.3015 Winter

APE=|(V(forecast)-V(actual))/ V(actual)|\*100% (7)

��� ) (6)

As it is shown too, in the Figure 2, we us a switch for any subsystem of a season be thought in lieu of that season. Therefore the time of system training and testing will decrease and the entrance of extra data is prevented.

Fig. 2. Implemented Diagram of Multi ANFIS
