Short-term load forecasting for electrical regional of a distribution utility considering temperature

This work deals with the application of Artificial Neural Networks (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) to provide the decentralized daily load short-term forecasting which is based on the average daily temperature. It is not an easy task to forecast the load demand of an electrical regional mainly because of the system reconfiguration either temporary (operational maneuvers) or permanent (creation of new regional). In this regard, ANN and ANFIS were chosen because they have robustness in their responses. Both models carry out the load forecasting for each electrical regional of CELPE distribution system in the period of 7 and 14 days ahead. The results were compared between each other and also with the PREVER software, demonstrating a considerable improvement in performance of the new models.

[1]  Ian H. Witten,et al.  Data mining: practical machine learning tools and techniques with Java implementations , 2002, SGMD.

[2]  T.,et al.  Training Feedforward Networks with the Marquardt Algorithm , 2004 .

[3]  Milde M. S. Lira,et al.  Combined Artificial Neural Network and Adaptive Neuro-Fuzzy Inference System for Improving a Short-Term Electric Load Forecasting , 2007, ICANN.

[4]  Alireza Khotanzad,et al.  ANNSTLF-Artificial Neural Network Short-Term Load Forecaster- generation three , 1998 .

[5]  Nien Fan Zhang,et al.  Forecasting and time series analysis , 1976 .

[6]  Jyh-Shing Roger Jang,et al.  ANFIS: adaptive-network-based fuzzy inference system , 1993, IEEE Trans. Syst. Man Cybern..

[7]  Milde M. S. Lira,et al.  Combining Artificial Neural Networks and Heuristic Rules in a Hybrid Intelligent Load Forecast System , 2006, ICANN.

[8]  In-Keun Yu,et al.  Kohonen neural network and wavelet transform based approach to short-term load forecasting , 2002 .

[9]  S. J. Kiartzis,et al.  A neural network short term load forecasting model for the Greek power system , 1996 .

[10]  Mohammad Bagher Menhaj,et al.  Training feedforward networks with the Marquardt algorithm , 1994, IEEE Trans. Neural Networks.

[11]  Agnaldo J. R. Reis,et al.  Feature extraction via multiresolution analysis for short-term load forecasting , 2005, IEEE Transactions on Power Systems.

[12]  J. Theocharis,et al.  A novel approach to short-term load forecasting using fuzzy neural networks , 1998 .

[13]  Lutz Prechelt,et al.  A Set of Neural Network Benchmark Problems and Benchmarking Rules , 1994 .