Peak Load Forecasting for Vietnam National Power System to 2030
Main Article Content
Abstract
Gross domestic product (GDP) growth rate, electric power consumption, and maximum load power demand (Pmax) have a closed but complicated and unexplicit correlation. Using the feed-forward back propagation (FFBP) method, a modified model of neural network, this paper will introduce a new long-term prediction approach for the maximum load power of Vietnam. Results from simulation indicate a considerable correlation of three parameters regarding electric power consumption demand, GDP growth rate, and maximum load power demand; the mean error of suggested model is about 1.92%. This is a reasonable range of mean error for a long-term prediction where the correlation of variables is not explicit. According to the basic scenario of National Economic Forecasting Model to 2030, the Vietnam's GDP annual growth rate is about 7% per year, and the corresponding electric power demands (GWh) are forceated in the previous paper, Pmax in 2020, 2025 and 2030 are forecasted here at 40,332 MW, 60,835 MW, and 87,558 MW, respectively. Those results are 3.4 4.2% lower than forecasted values of the Revised National Power Development Plan VII (hereinafter referred to as PDP 7 rev) for the period of 2011-2020 with the vision to 2030.
Keywords
Forecast, Peak load, Neural network, Feed-forward back propagation - FFBP, Vietnam
Article Details
References
[1] Decision No. 07/QĐ-ĐTĐL on the process of forecasting power load demand in the national electricity system. Hanoi, 14/03/2013.
[2] Z. Ismail, A. Yahya, K. A. Mahpol, Forecasting Peak Load Electricity Demand Using Statistics and Rule Based Approach, American Journal of Applied Sciences. 6(8) (2009) 1618-1625.
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[7] N. H. M. Vu, N. T. P. Khanh, V. V. Cuong, P. T. T. Binh, Forecast on Vietnam Electricity Consumption to 2030, the 2017 International Conference on Electrical Engineering and Informatics (ICELTICS 2017). IEEE (2017) 81-86.
[2] Z. Ismail, A. Yahya, K. A. Mahpol, Forecasting Peak Load Electricity Demand Using Statistics and Rule Based Approach, American Journal of Applied Sciences. 6(8) (2009) 1618-1625.
[3] L. Ghods, M. Kalantar, Different Methods of Long- term Electric Load Demand Forecasting: A Comprehensive Review, Iranian Journal of Electrical and Electronic Engineering. Vol 7(4) (2011).
[4] B. S. Kermanshahi, H. Iwamiya, Up to year 2020 Load Forecasting Using Neural Nets, Electric Power System Research. Vol 24(9) (2002) 789-797.
[5] D. Howard, B. Mark, The Mathworks: User Guide Neural Network Toolbox For Use with MATLAB.
[6] Decision No. 428/QĐ-TTg on The Revised National Power Development Plan VII (PDP 7 rev). 18/03/2016.
[7] N. H. M. Vu, N. T. P. Khanh, V. V. Cuong, P. T. T. Binh, Forecast on Vietnam Electricity Consumption to 2030, the 2017 International Conference on Electrical Engineering and Informatics (ICELTICS 2017). IEEE (2017) 81-86.