**8. References**


**16** 

*Iran* 

**A Multi Adaptive Neuro Fuzzy Inference System** 

Load forecasting had an important role in power system design, planning and development and it is the base of economical studies of energy distribution and power market. The period of load forecasting can be for one year or month (long-term or medium-term) and for one

For short-term load forecasting several factors should be considered, such as time factors, weather data, and possible customers' classes. The medium- and long-term forecasts take into account the historical load and weather data, the number of customers in different categories, the appliances in the area and their characteristics including age, the economic and demographic data and their forecasts, the appliance sales data, and other factors [17].

The time factors include the time of the year, the day of the week, and the hour of the day. There are important differences in load between weekdays and weekends. The load on different weekdays also can behave differently. For example, in Iran, Fridays is weekends, may have structurally different loads than Saturdays through Thursday. This is particularly true during the summer time. Holidays are more difficult to forecast than non-holidays

Several techniques have been used for load forecasting that among its common methods we can refer to linear-regression model, ARMA, BOX-Jenkis[5] and filter model of Kalman, expert systems [6] and ANN [1-4,7]. According to load-forecasting complex nature, however its studying by linear techniques cannot meet the need of having high accuracy and being resistant. Adaptive neural-fuzzy systems can learn and build any non-linear and complex

Then neural-fuzzy systems have many applications in studying load forecasting and power systems according to the non-linear and complex nature of power nets. Among them we can

The east of Iran power plant consumed load information was used for simulation of consumed load forecasting system. The effect of weather forecasting information in

refer to load-peak forecasting and daily network load-curve forecasting.

**1. Introduction** 

day or hour (short-term) [1, 2, 3, and 4].

because of their relative infrequent occurrence.

record through educational input-output data.

**for Short Term Load Forecasting by** 

**Using Previous Day Features** 

Zohreh Souzanchi Kashani

*Islamic Azad University, Mashhad* 

*Young Researchers Club, Mashhad Branch,* 

