Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 33104
Application of Neural Networks for 24-Hour-Ahead Load Forecasting
Authors: Fatemeh Mosalman Yazdi
Abstract:
One of the most important requirements for the operation and planning activities of an electrical utility is the prediction of load for the next hour to several days out, known as short term load forecasting. This paper presents the development of an artificial neural network based short-term load forecasting model. The model can forecast daily load profiles with a load time of one day for next 24 hours. In this method can divide days of year with using average temperature. Groups make according linearity rate of curve. Ultimate forecast for each group obtain with considering weekday and weekend. This paper investigates effects of temperature and humidity on consuming curve. For forecasting load curve of holidays at first forecast pick and valley and then the neural network forecast is re-shaped with the new data. The ANN-based load models are trained using hourly historical. Load data and daily historical max/min temperature and humidity data. The results of testing the system on data from Yazd utility are reported.Keywords: Artificial neural network, Holiday forecasting, pickand valley load forecasting, Short-term load-forecasting.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1070919
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2193References:
[1] D. C. Park, M. A. El-Sharkawi, R. J. MarksII, L. E. Atlas, M. J. Damborg, "Electrical Load Forecasting Using an Artificial Neural Network," IEEE Trans. on Power Systems., vol. 6, No. 2, Nov. 1991, pp. 442-448.
[2] T. M. Peng, N. F. Hubele, G. G. Karady, "Conceptual Approach to the Application of Neural Network for Short-Term Load Forecasting," IEEE International Symposium on Circuits and Systems., vol. 4, 1990, pp. 2942-2945.
[3] S. T. Chen, D. C. Yu, A. R. Moghaddamjo, "Weather Sensitive Short- Term Load Forecasting Using Nonfully Connected Artificial Neural Network," IEEE Trans. on Power Systems., vol. 7, No. 3, Aug. 1992, pp. 1098-1105.
[4] T. S. Dillon, S. Sestito, S. Leung, "An Adaptive Neural Network Approach in A Power System," IEEE Proc. of 1991 ANNPS., July. 1991, pp. 17-21.
[5] K. Y. Lee, Y. T. Cha, C. C. Ku, "A Study on Neural Networks for Short- Term Load Forecasting," Proc. of Applications of Neural Networks to Power Systems 1991, Seattle, WA, July. 1991, pp. 26-30.
[6] A. Khotanzad, M. H. Davis, A. Abaye, D. J. Martukulam, "An Artificial Neural Network Hourly Temperature Forecaster with Applications in Load Forecasting," IEEE Trans. PWRS, vol. 11, No. 2, May. 1996, pp. 870-876.
[7] T. Matsumoto, S. Kitamara, Y. Ueki, T. Matsui, "Short Term Load Forecasting by Artificial Neural Networks Using Individual and Collective Data of Preceding Years," Neural Networks to Power Systems, 1993, pp. 245-250.