**8. Result**

In the proposed method we classified day into two categories. We divide the season days into two groups of working days (Saturday to Thursday) and holidays that their load consumption is different from other days.

Here we also calculated the output of Multi ANFIS based on the features of previous day, one time with 2, 7, and 14 day ago and another time with 2, 3, and 4 day ago. You can see the results in Table 2 and 3.

The amount of the accuracy of the performance of any of calculation methods in load forecasting is determined by measuring the obtained values of system model and comparing it with real data.

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

In the proposed method we classified day into two categories. We divide the season days into two groups of working days (Saturday to Thursday) and holidays that their load

Here we also calculated the output of Multi ANFIS based on the features of previous day, one time with 2, 7, and 14 day ago and another time with 2, 3, and 4 day ago. You can see

The amount of the accuracy of the performance of any of calculation methods in load forecasting is determined by measuring the obtained values of system model and

entrance of extra data is prevented.

Fig. 2. Implemented Diagram of Multi ANFIS

consumption is different from other days.

the results in Table 2 and 3.

comparing it with real data.

**8. Result** 

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:

$$\text{MAPE} = 1/\text{N} \left( \sum\_{l=1}^{N} APE\_l \right) \tag{6}$$

$$\text{APE} = \left| \left( \text{V(forecast)} \text{-V(actual)} \right) / \text{V(actual)} \right| \text{\*100\%} \tag{7}$$


Table 2. Power load consumption forecasting for the working days (saturday to thursday) with 2, 3, and 4 day ago


Table 3. Power load consumption forecasting for the working days (saturday to thursday) with 2, 7, and 14 day ago

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 consumption forecasting.

Fig. 3. Power load forecasting for Working days (Saturday to Thursday) of fall with features of 2, 3, and 4 day ago

A Multi Adaptive Neuro Fuzzy Inference System for

environment created by the electric industry deregulation.

Electric Power Energy syst., 28: 525-530.

effect on load forecasting.

**10. References** 

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Short Term Load Forecasting by Using Previous Day Features 351

see that using the features of 2, 7 and 14 day ago are better than 2, 3 and 4 day ago. A cyan and yellow line are refer to 3 and 4 day ago. We can see that these features cannot have good

According to this that in most proposed methods load consumption time series data is used; it seems that we can obtain better results by using time series data of one or more parameters effective in load consumption [16] also with load consumption time series. Accurate load forecasting is very important for electric utilities in a competitive

[1] Huang, S.J. and K.R. Shih, 2003. Short term load forecasting via ARMA model

[2] Kandil Nahi, Rene Wamkeue, Maarouf saad and Semaan Georges, 2006. An efficient

[3] Mandal Paras, Tomonobu Senjyu, Naomitsu Urasaki, Toshihisa Funabashi, 2006. A

[4] Topalli Ayca Kumluca, Ismet Erkmen and Ihsan Topalli, 2006. Intelligent short term load forecasting in Turkey. Int. J. Electric. Power Energy Syst., 28: 437-447. [5] Moghram, I. and S. Rahman, 1989. Analysis and evaluation of five short term load

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[9] Amjadi N, Daraeepour A, "Mixed price and load forecasting of electricity markets by a new iterative prediction method", Electric power systems research, 2009. [10] Niu D, Li J, Liu D, "Middle-long power load forecasting based on particle swarm optimization", computers and mathematics with applications, 2009. [11] Alrashidi M, Elnaggar K, "Long term electric load forecasting based on particle swarm

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neural network based several hours ahead electric load forecasting using similar

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 for load forecasting and simulation.

Fig. 4. Power load forecasting for Working days (Saturday to Thursday) of fall with features of 2, 7, and 14 day ago

#### **9. Conclusion and suggestion**

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

see that using the features of 2, 7 and 14 day ago are better than 2, 3 and 4 day ago. A cyan and yellow line are refer to 3 and 4 day ago. We can see that these features cannot have good effect on load forecasting.

According to this that in most proposed methods load consumption time series data is used; it seems that we can obtain better results by using time series data of one or more parameters effective in load consumption [16] also with load consumption time series. Accurate load forecasting is very important for electric utilities in a competitive environment created by the electric industry deregulation.
