Abstract
This paper aims to study the short-term load forecasting of electricity by using an extended self-organizing map. We first adopt a traditional Kohonen self-organizing map (SOM) to learn time-series load data with weather information as parameters. Then, in order to improve the accuracy of the prediction, an extension of SOM algorithm based on error-correction learning rule is used, and the estimation of the peak load is achieved by averaging the output of all the neurons. Finally, as an implementation example, data of electricity demand from New York Independent System Operator (ISO) are used to verify the effectiveness of the learning and prediction for the proposed methods.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Henrique, S.H., Carlos, E.P., Reinaldo, C.S.: Neural Networks for Short-Term Load Forecasting: A Review and Evaluation. IEEE Trans. Power Systems 16, 44–55 (2001)
Haida, T., Muto, S.: Regression Based Peak Load Forecasting Using a Transformation Technique. IEEE Trans. Power Systems 9, 1788–1794 (1994)
Papalexopoulos, A.D., Hesterberg, T.C.: A Regression-Based Approach to Short-Term Load Forecasting. IEEE Trans. Power Systems 5, 1535–1550 (1990)
Rahman, S., Hazim, O.: A Generalized Knowledge-Based Short Term Load-Forecasting Technique. IEEE Trans. Power Systems 8, 508–514 (1993)
Huang, S.J., Shih, K.R.: Short-Term Load Forecasting via ARMA Model Identification Including Nongaussian Process Considerations. IEEE Trans. Power Systems 18, 673–679 (2003)
Wu, H., Lu, C.: A Data Mining Approach for Spatial Modeling in Small Area Load Forecast. IEEE Trans. Power Systems. 17, 516–521 (2003)
Box, G.E.P., Jenkins, G.M.: Time Series Analysis– forecasting and Control. Holden-day, San Francisco (1976)
Infield, D.G., Hill, D.C.: Optimal Smoothing For Trend Removal In Short Term Electricity Demand Forecasting. IEEE Trans. Power Systems 13, 1115–1120 (1998)
Czernichow, T., Piras, A., Imhof, K., Caire, P., Jaccard, Y., Dorizzi, B., Germond, A.: Short Term Electrical Load Forecasting with Artificial Neural Networks. Engineering Intelligent Systems 2, 85–99 (1996)
Lalit, M.S., Mahender, K.S.: Artificial Neural Network-Based Peak Load Forecasting Using Conjugate Gradient Methods. IEEE Trans. Power Systems 12, 907–912 (2002)
Taylor, J.W., Roberto, B.: Neural Network Load Forecasting with Weather Ensemble Predictions. IEEE Trans. Power Systems 17, 626–632 (2002)
Khotanzad, A., Afkhami-Rohani, R., Maratukulam, D.: ANNSTLF–Artificial Neural Network Short-Term Load Forecaster-Generation Three. IEEE Trans. Power Systems 13, 1413–1422 (1998)
Guilherme, A., Barreto, Aluizio, F.R.: Identification and Control of Dynamical Systems Using Self-Organizing Map. IEEE Trans. Neural Networks 15, 1244–1259 (2004)
Lamedica, R., Prudenzi, A., Sforna, M., Caciotta, M., Cencelli, V.O.: A Neural Network Based Technique for Short-Term Forecasting of Anomalous Load Periods. IEEE Trans. Power Systems 11, 1749–1756 (1996)
Srinivasan, D., Tan, S.S., Chang, C.S., Chan, E.K.: Parallel Neural Network-Fuzzy Expert System Strategy for Short-Term Load Forecasting: System Implementation and Performance Evaluation. IEEE Trans. Power Systems 14, 1006–1100 (1999)
Walter, J.A., Schulten, K.J.: Implementation of Self-organizing Neural Networks for Visuo-motor Control of an Industrial Robot. IEEE Trans. Neural Networks 4, 86–95 (1993)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Fan, S., Mao, C., Chen, L. (2005). Peak Load Forecasting Using the Self-organizing Map. In: Wang, J., Liao, XF., Yi, Z. (eds) Advances in Neural Networks – ISNN 2005. ISNN 2005. Lecture Notes in Computer Science, vol 3498. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11427469_102
Download citation
DOI: https://doi.org/10.1007/11427469_102
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25914-5
Online ISBN: 978-3-540-32069-2
eBook Packages: Computer ScienceComputer Science (R0)