基于大数据和优化神经网络短期电力负荷预测

doi:10.11959/j.issn.1000-436x.2016245

Abstract

Abstract:

With the reduction of the cost of power data acquisition and the interconnection of large scale power systems,the types of data available in the power network are becoming more and more abundant.In the past,the centralized fore-casting method was limited to the analysis of the massive power data.Therefore,a short-term power load forecasting based on large data and particle swarm optimization BP neural network was proposed,and short-term power load fore-casting model was established.The actual load data of the national grid,using the method of prediction,compared with the actual load data and centralized load forecasting results prove that this method is accurate enough,reduce the load forecasting time with feasibility in practical application.

Key words: electric power data, particle swarm algorithm, parallel PSO to optimize the neural network, power load fore-casting, power load factor

Xin JIN,Long-wei LI,Jia-nan JI,Zhi-qi LI,Yu HU,Yong-bin ZHAO. Power short-term load forecasting based on big data and optimization neural network[J]. Journal on Communications, 2016, 37(Z1): 36-42.

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References 28

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\|R_1?x\|	线性相关程度
[0,0.3）		低度线性相关
[0.3,0.5）		中低度线性相关
[0.5,0.8）		中度相性相关
[0.8,1]	高度线性相关

时间	预测值/W	实际值/mW	误差	相对误差
1:00	0.224 25	0.212 1	?0.012 15	?5.41%
2:00	0.133 88	0.131 5	?0.002 38	?1.77%
3:00	0.141 14	0.112 1	?0.029 04	?20.57%
4:00	0.218 80	0.228 7	0.009 9	4.52%
5:00	0.578 75	0.616 2	0.037 42	6.47%
6:00	0.624 15	0.632 1	0.007 95	1.27%
7:00	0.655 24	0.655 2	0.000 24	0.06%
8:00	0.714 50	0.725 4	0.010 9	1.52%
9:00	0.715 47	0.726 1	0.010 63	1.48%
10:00	0.784 56	0.788 0	0.003 44	0.43%
11:00	0.835 42	0.843 1	0.007 68	0.91%
12:00	0.845 56	0.856 4	0.010 84	0.12%

模型	平均相对误差	训练步数	泛化能力
基本BP神经网络	1.88%	216	差
改进的神经网络算法	1.68%	7	一般
粒子群优化神经网络模型	1.20%	4	强

Power short-term load forecasting based on big data and optimization neural network

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