Medium term load forecasting using ANFIS predictor

Nowadays, there are huge ranges of energy market participants. Commercial success of this area actor depends on the ability to submit competitive predictions relative to energy balance trends Thus, it seems convenient to “anticipate” this parameter evolution in time in order to act consequently and resort to protective actions. In this context, this paper proposes a tool for energy balance prediction based on ANFIS (Adaptive Neuro Fuzzy Inference System). This neuro- fuzzy predictor is modified in order to obtain an accurate forecasting for medium term. The solutions are illustrated on a real application and take into account the known “future”: the programmed actions.

[1]  Rong Chen,et al.  A semi-parametric time series approach in modeling hourly electricity loads , 2006 .

[2]  Kai Goebel,et al.  When will it break? A hybrid soft computing model to predict time-to-break margins in paper machines , 2002, Optics + Photonics.

[3]  P. McSharry,et al.  A comparison of univariate methods for forecasting electricity demand up to a day ahead , 2006 .

[4]  J. Y. Desmons,et al.  Prévision à long terme de la réponse d'un stockage de chaleur sensible dans le sol , 1997 .

[5]  V. Makis,et al.  Recursive filters for a partially observable system subject to random failure , 2003, Advances in Applied Probability.

[6]  E. Mizutani,et al.  Neuro-Fuzzy and Soft Computing-A Computational Approach to Learning and Machine Intelligence [Book Review] , 1997, IEEE Transactions on Automatic Control.

[7]  Rune Brincker,et al.  Vibration Based Inspection of Civil Engineering Structures , 1993 .

[8]  Georges A. Darbellay,et al.  Forecasting the short-term demand for electricity: Do neural networks stand a better chance? , 2000 .

[9]  Frank L. Lewis,et al.  Intelligent Fault Diagnosis and Prognosis for Engineering Systems , 2006 .

[10]  B. Hobbs,et al.  Analysis of the value for unit commitment of improved load forecasts , 1999 .

[11]  M. Medeiros,et al.  Modeling and forecasting short-term electricity load: A comparison of methods with an application to Brazilian data , 2008 .

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

[13]  L. S. Moulin,et al.  Confidence intervals for neural network based short-term load forecasting , 2000 .

[14]  Antoni Espasa,et al.  Forecasting from one day to one week ahead for the Spanish system operator , 2007 .

[15]  Carlos E. Pedreira,et al.  Neural networks for short-term load forecasting: a review and evaluation , 2001 .

[16]  Rob J Hyndman,et al.  25 years of time series forecasting , 2006 .

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

[18]  R. Buizza,et al.  Using weather ensemble predictions in electricity demand forecasting , 2003 .

[19]  K. Goebel,et al.  Prognostic information fusion for constant load systems , 2005, 2005 7th International Conference on Information Fusion.

[20]  C. Gouriéroux ARCH Models and Financial Applications , 1997 .

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

[22]  Derek W. Bunn,et al.  Large neural networks for electricity load forecasting: Are they overfitted? , 2005 .

[23]  Noureddine Zerhouni,et al.  Framework for a distributed and hybrid prognostic system , 2007 .

[24]  Kostas S. Metaxiotis,et al.  Artificial intelligence in short term electric load forecasting: a state-of-the-art survey for the researcher , 2003 .

[25]  José Ramón Cancelo,et al.  Forecasting the electricity load from one day to one week ahead for the Spanish system operator , 2008 .

[26]  Jeen-Shing Wang,et al.  An efficient recurrent neuro-fuzzy system for identification and control of dynamic systems , 2003, SMC'03 Conference Proceedings. 2003 IEEE International Conference on Systems, Man and Cybernetics. Conference Theme - System Security and Assurance (Cat. No.03CH37483).

[27]  Richard D. Braatz,et al.  Fault Detection and Diagnosis in Industrial Systems , 2001 .