基于改进人工鱼群-蛙跳算法优化LSSVM参数短期负荷预测

doi:10.16180/j.cnki.issn1007-7820.2020.12.013

Abstract

Abstract:

Short-term load forecasting plays a key role in safe dispatching and economic operation of power system.The parameters of the LSSVM directly affect the prediction effect during the load forecasting accuracy. In order to improve LSSVM load prediction accuracy, a method based on levy adaptive vision artificial fish swarm-shuffled frog leaping algorithm for parameter optimization of LSSVM is proposed. LSSVM is trained by historical data such as load and weather in a certain area. The LAFSA-SFLA-LSSVM forecasting model, the LAVAFSA-SFLA-LSSVM forecasting model and the AFSA-LSSVM forecasting model are established for power load forecasting in a certain area within 24 hours of a certain day. The results show that the accuracy of the LAVAFSA-SFLA-LSSVM forecasting model is higher than the AFSA-LSSVM forecasting model and the LAFSA-SFLA-LSSVM forecasting model, and the prediction error is smaller.

Key words: short-term load forecasting, power system scheduling, prediction accuracy, least squares support vector machine, improve artificial fish swarm-shuffled frog leaping algorithm, optimization parameter

CLC Number:

TP13

YANG Haizhu,JIANG Zhaoyang,LI Menglong,KANG Le. Parameters Selection for LSSVM Based on Artificial Fish Swarm-Shuffled Frog Jump Algorithms Optimization in Short-Term Load Forecasting[J].Electronic Science and Technology, 2020, 33(12): 67-74.

Figures/Tables 7

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函数	算法	全局最优解	最优解平均值
f₁(x)	AFSA	1.638 1	1.720 7
	LAFSA-SFLA	0.871 0	0.925 0
	LAVAFSA-SFLA	0.054 1	0.064 6
f₂(x)	AFSA	1.943 5	2.186 4
	LAFSA-SFLA	0.064 6	0.184 8
	LAVAFSA-SFLA	0.033 9	0.104 5

时段	负荷实际值 /MW	AFSA- LSSVM	LAFSA-SFLA- LSSVM	LAVAFSA-SFLA -LSSVM
1	812.97	778.55	780.58	800.99
2	760.08	712.10	715.31	732.15
3	731.84	701.89	708.88	712.98
4	686.69	652.36	663.59	671.71
5	652.28	625.56	628.27	630.19
6	619.81	590.21	595.78	599.61
7	608.22	571.01	579.36	582.98
8	580.51	548.15	550.23	558.31
9	622.92	595.93	596.58	599.78
10	670.84	630.59	633.13	650.84
11	737.49	705.89	709.21	712.89
12	825.60	775.70	780.67	800.90
13	843.53	803.98	809.87	817.37
14	837.21	802.11	810.86	825.41
15	824.04	780.39	785.26	800.03
16	813.45	765.82	769.43	795.24
17	802.63	747.69	765.57	789.79
18	778.18	733.89	741.56	753.85
19	739.08	700.82	710.12	723.99
20	722.38	675.36	688.15	700.98
21	769.04	735.81	739.53	744.04
22	822.65	780.69	786.84	800.67
23	860.68	806.81	812.41	842.64
24	822.05	788.03	792.96	810.34

Parameters Selection for LSSVM Based on Artificial Fish Swarm-Shuffled Frog Jump Algorithms Optimization in Short-Term Load Forecasting

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