Abstract
The results of combining a numeric extrapolation of data with the methodology of case-based reasoning and expert systems in order to improve the electric load forecasting are presented in this contribution. Registers of power consumption are stored as cases that are retrieved and adapted by an expert system to improve a numeric forecasting given by numeric algorithms. FUTURA software has been developed as a result of this work. It combines the proposed techniques in a modular way while it provides a graphic user interface and access capabilities to existing data bases.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Aamodt, A., Plaza, E. (1994) Case-Base Reasoning: foundational issues, methodologicalvariations and system approaches. AI Communications. IOS Press, Vol. 7:1 pp 39–59.
Bartkiewicz, W. (2000) Neuro-Fuzzy Approaches to Short-Term Electrical Load Forecasting. Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN’00).
Bartkiewicz, W. Gontar Z. and Zielinski, J. (2000) Uncertainty of the Short-Term Electrical Load Forecasting in Utilities. Proc. of the IEEE-INNS-ENNS-IJCNN’00.
Carpentiero, O., Silva A., Feichas C. (2000) A Hierarchical Neural Model in Short-TermLoad Forecasting. Proceedings of the IEEE-INNS-ENNS-IJCNN’00.
Charytoniuk, W. y Mo-Shing Chen (2000). Very short-term load forecasting using artificial neural networks. IEEE Trans. on Power Systems, Vol. 15 No. 1, 263–268.
Charytoniuk, W.; Chen, M.S.; Van Olinda, P. (1997) Nonparametric Regression Based Short-Term Load Forecasting. U. of Texas at Arlington. PE-891-PWRS-0-08-1997
Comisión de Tarifas de Energίa (2001) Estudio de Costos del Valor Agregado de Distribución. Gerencia Adjunta de Regulación tarifarίa(ex CTE)-OSINERG. Lima.
Dabbaaghchi I, Cristie R., et al. (1997). Al Aplication Areas in Power Systems, IEEE Expert, January February
De la Rosa, J.(1999) Sistemes experts a temps real, Publicación de Universitat de Girona, España.
Electric Power Research Institute (1979) Research into Load Forecasting and Distribution Planning, EPRI Report EL-1198, EPRI, Palo Alto,CA.
Garcia, Aet al.(1994) A Neural System for Short-Term Load Forecasting Based on Day-Type Classification, Proc. ISAP94, pp. 353–360.
GART-OSINERG. Anuario Estadίstico 1996-1997-1998-1999-2000. Gerencia Adjunta de Regulación Tarifarίa.-Organismo supervisor de la inversión en energίa GART OSINERG. Lima Perú.
Hansen, B. (2000) Analog forecasting of ceiling and visibility using fuzzy sets. Maritimes Weather Centre, Darmouth, Nova Scotia.
Hong-Tzer, Y, et al (1996) Identification of ARMAX Model for short term load forecasting: an evolutionary programming approach. IEEE Trans. on Power Systems, Vol. 11, No. 1, 403–408.
Lee, H. (1996) Spatial Electric Load Forecasting, Marcel Dekker, Inc.
Melendez J. Macaya D., Colomer J., (2001), Case Based Reasoning methodology for process suprvision” CG proceedings of the ECC’01, European Control Conference
Melendez J, Colomer J, de la Rosa JL, (2001), “Expert Supervision Based on Cases”, Proceedings of the 8th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA’0 1, pp: 431–440.
Vilcahuamán, R. et al. (1999) Sistema experto para el pronostico de la demanda. CONIMERA XXV, Lima, Perú.
Vilcahuamán, R. Medina, I. y Trelles, A. (2000) PRONOS: Sistema experto para el pronostico de la demanda. Facultad de Ingenierίa de Sistemas. Universidad Nacional del Centro del Perú.
Third international conference on case-based reasoning (1999) Engineering Applications of Case-Based Reasoning. Special Issue of The International Journal.Engineering Applications of Artificial Intelligence. Volume 12(6) 1999.
Richter, M. The Knowledge Contained in Similary Measures. University of Kaiserlautern.
Mori, H., y Yuihara, A. (2001) Deterministic Annealing clustering for ANN-based shortterm load forecasting. IEEE Trans. on Power Systems, Vol. 16, No. 3, 545–552.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2002 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Vilcahuamán, R., Meléndez, J., de la Rosa, J.L. (2002). FUTURA: Hybrid System for Electric Load Forecasting by Using Case-Based Reasoning and Expert System. In: Escrig, M.T., Toledo, F., Golobardes, E. (eds) Topics in Artificial Intelligence. CCIA 2002. Lecture Notes in Computer Science(), vol 2504. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36079-4_11
Download citation
DOI: https://doi.org/10.1007/3-540-36079-4_11
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-00011-2
Online ISBN: 978-3-540-36079-7
eBook Packages: Springer Book Archive