**1. Introduction**

336 Fuzzy Inference System – Theory and Applications

Fauziah, A. (January 2005). PM Mahu Laporan Segera – TNB Diarah Siasat Terperinci Punca

Jang, J.-S.R. (1993). ANFIS: Adaptive Network Based Fuzzy Inference System. *IEEE Transaction on* 

Marusic, A. & Gruhonjic-Ferhatbegovic, S. (2006). A Computerized Fault Location Process

Michael Negnevitsky. (2005). *Artificil Intelligent: A Guide to Intelligent System*, Addison-

Mitra, P., Maulik, S., Chowdhury, S.P. & Chowdhury, S. (2008). ANFIS Based Automatic

Oliveira, K.R.C. (2007). *Advanced Intelligent Computing Theories and Applications with Aspects of* 

Richard, E.B.( 2009). *Electric Power Distribution Reliability*. CRS Press, ISBN 978-0-8493-7567-5,

Sadeh, J. & Afradi, H. (2009). A New and Accurate Fault Location Algorithm for Combined

Silva, K.M., Souza, B.A. & Brito, N.S.D. (2006). Fault Detection and Classification in

Souza, J.C.S., Meza, E.M., Sebilling, M.T. & Do Corato, M.B. (2004). Alarm Processing in

Thomas Tamo Tatiesa & Joseph Voufo. (2009). Fault Diagnosis on Medium Voltage (MV)

Zhisheng Zhang & Yarning Sun, 2007. Assessment on Fault-Tolerance Performance Using

Zhiwei Liao, Fushuan Wen, Wenxin Guo, Xiangzhen He, Wei Jiang, Taifu Dong, Junhui &

*Electric Power System Research*, Vol.79, No.11, pp. 1538-1545, ISSN 0378-7796 Sekine, Y., Akimonto, Y., Kunugi, M. & Fukui, C. (1992). Fault Diagnosis of Power System.

*Proceedings of the IEEE*. Vol.80, No.5, pp. 673-683, ISSN 0018-9219

*Power Delivery*, Vol.21, No.4, pp. 2058-2063, ISSN 0885-8977

*Power Delivery*, Vol.19, No.2, pp. 537-544, ISSN 0885-8977

ISBN 978-7-900714-13-8, Nanjuing, China, April 6-9, 2008

*Artificial Intelligent*, Springer Berlin, ISBN 978-3-540-74201-2

*Systems, Man and Cybernetic,* Vol.23, No.3, pp. 665-685, ISSN 0018-9472

Wesley, ISBN 0-321-20466-2, Harlow, England

\_Utama

USA

ISSN 1996-1073

August 1, 2007

Sol, May 16-19, 2006

pp. 129-134, ISSN 1078-3466

Gangguan Bekalan Elektrik, In: *Utusan Malaysia,* 14.01.2005, Available from http://www.utusan.com.my/utusan/info.asp?pub=Utusan\_Malaysia&sec=Berita

for Overhead Radial Distribution Feeders, *IEEE Mediterranean Conference on Electrotechnical,* pp. 1114-1117, ISBN 1-4244-0087-2, Benalmadena, Malaga, Costa del

Voltage Regulator with Hybrid Learning Algorithm. *International Journal of Innovations in Energy Systems and Power,* Vol. 3, No.2, pp. 1-5, ISSN 1913-135X Mohamed, A. & Mazumder. (1999). A Neural Network Approach to Fault Diagnosis in a

Distribution System. *International Journal of Power and Energy systems*. Vol.19, No.2,

Transmission Lines Using Adaptive Network-Based Fuzzy Inference System. *Journal of* 

Transmission Lines Based On Wavelet Transform and ANN. *IEEE Transaction on* 

Electrical Power Systems through a Neuro-Fuzzy Approach. *IEEE Transaction on* 

Electric Power Distribution Networks: The Case of The Downstream Network of The AES-SONEL Ngousso Sub-Station. *Journal of Energies,* Vol.2, No.2, pp. 243-257,

Neural Network Model Based on Ant Colony Optimization Algorithm for Fault Diagnosis in Distribution Systems of Electric Power Systems, *The 8th International Conference on Software Engineering, Artificial Intelligent, Networking and Parallel Distributed Computing*, pp. 712-716, ISBN 0-7695-2909-7, Qingdao, China, July 30 –

Binghua Xu. (2008). An Analytic Model and Optimization Technique Based Methods for Fault Diagnosis in Power System. *The 3rd International Conference on Electric Utility Deregulation and Restructuring and Power Technologies*, pp. 1388-1393, 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 day or hour (short-term) [1, 2, 3, and 4].

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 because of their relative infrequent occurrence.

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 record through educational input-output data.

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

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

A Multi Adaptive Neuro Fuzzy Inference System for

other applications of regression models to loads forecasting.

(FARMAX) for one day ahead hourly load forecasts.

and outputs, and internally.

propagation algorithm was used for training.

Short Term Load Forecasting by Using Previous Day Features 339

load consumption and other factors such as weather, day type, and customer class. Engle et al. [18] presented several regression models for the next day peak forecasting. Their models incorporate deterministic influences such as holidays, stochastic influences such as average loads, and exogenous influences such as weather. References [19], [20], [21], [22] describe

*Time series*. Time series methods are based on the assumption that the data have an internal structure, such as autocorrelation, trend, or seasonal variation. Time series forecasting methods detect and explore such a structure. Time series have been used for decades in such fields as economics, digital signal processing, as well as electric load forecasting. In particular, ARMA (autoregressive moving average), ARIMA (autoregressive integrated moving average), ARMAX (autoregressive moving average with exogenous variables), and ARIMAX (autoregressive integrated moving average with exogenous variables) are the most often used classical time series methods. ARMA models are usually used for stationary processes while ARIMA is an extension of ARMA to non-stationary processes. ARMA and ARIMA use the time and load as the only input parameters. Since load generally depends on the weather and time of the day, ARIMAX is the most natural tool for load forecasting among the classical time series models. Fan and McDonald [23] and Cho et al. [24] describe implementations of ARIMAX models for load forecasting. Yang et al. [25] used evolutionary programming (EP) approach to identify the ARMAX model parameters for one day to one week ahead hourly load demand forecast. Evolutionary programming [26] is a method for simulating evolution and constitutes a stochastic optimization algorithm. Yang and Huang [27] proposed a fuzzy autoregressive moving average with exogenous input variables

*Neural networks*. The use of artificial neural networks (ANN or simply NN) has been a widely studied electric load forecasting technique since 1990 [28]. Neural networks are essentially non-linear circuits that have the demonstrated capability to do non-linear curve fitting. The outputs of an artificial neural network are some linear or nonlinear mathematical function of its inputs. The inputs may be the outputs of other network elements as well as actual network inputs. In practice network elements are arranged in a relatively small number of connected layers of elements between network inputs and outputs. Feedback paths are sometimes used. In applying a neural network to electric load forecasting, one must select one of a number of architectures (e.g. Hopfield, back propagation, Boltzmann machine), the number and connectivity of layers and elements, use of bi-directional or unidirectional links, and the number format (e.g. binary or continuous) to be used by inputs

The most popular artificial neural network architecture for electric load forecasting is back propagation. Back propagation neural networks use continuously valued functions and supervised learning. That is, under supervised learning, the actual numerical weights assigned to element inputs are determined by matching historical data (such as time and weather) to desired outputs (such as historical electric loads) in a pre-operational "training session". Artificial neural networks with unsupervised learning do not require preoperational training. Bakirtzis et al. [29] developed an ANN based short-term load forecasting model for the Energy Control Center of the Greek Public Power Corporation. In the development they used a fully connected three-layer feed forward ANN and back

consumed load was considered by entering Mashhad climate information gathered from weather forecasting department of the province.
