基于异构数据的电力短期负荷大数据预测方案

doi:10.11959/j.issn.1000-0801.2022292

Abstract

Abstract:

The power system is in a transition towards a more intelligent, flexible and interactive system with higher penetration of renewable energy generation, load forecasting, especially short-term load forecasting for individual electric customers plays an increasingly essential role in future grid planning and operation.A big data framework for short-term power load forcasting using heterogenous was proposed, which collected the data from smart meters and weather forecast, pre-processed and loaded it into a NoSQL database that was capable to store and further processing large volumes of heterogeneous data.Then, a long short-term memory (LSTM) recurrent neural network was designed and implemented to determine the load profiles and forecast the electricity consumption for the residential community for the next 24 hours.The proposed framework was tested with a publicly available smart meter dataset of a residential community, of which LSTM’s performance was compared with two benchmark algorithms in terms of root mean square error and mean absolute percentage error, and its validity has been verified.

Key words: short-term load forecasting, long short-term memory network, recurrent neural network, clustering, big data

CLC Number:

U416.216

Haibo ZHAO, Zhijun XIANG, Linsong XIAO. A big data framework for short-term power load forecasting using heterogenous data[J]. Telecommunications Science, 2022, 38(12): 103-111.

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References 12

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影响因素	SelectKBest	LassoCV
温度	2 213.146 5	0.831 2
体感温度	1 945.258 6	0.694 6
气压	314.621 4	0.152 3
相对湿度	129.316 7	0.028 7
降水概率	131.612 9	0.101 2
风速	2.644 2	-0.004 5
风向	1.571 7	0
能见度	0.482 4	0

模型	聚类0	聚类1	聚类2	聚类3	聚类4
本文模型	2.31	1.31	2.09	2.31	1.42
多元线性回归	3.12	3.53	2.84	3.08	2.51
BP神经网络	2.96	2.86	2.38	3.15	3.24

模型	聚类0	聚类1	聚类2	聚类3	聚类4
本文模型	8.14%	7.28%	7.32%	7.98%	8.06%
多元线性回归	23.83%	24.56%	23.76%	19.26%	20.44%
BP神经网络	14.32%	13.38%	14.27%	13.48%	14.34%

模型	聚类0	聚类1	聚类2	聚类3	聚类4
本文模型	2.24	1.28	2.18	2.42	1.46
多元线性回归	3.64	4.21	3.78	3.97	2.89
BP神经网络	3.49	3.25	2.98	3.65	4.03

模型	聚类0	聚类1	聚类2	聚类3	聚类4
本文模型	8.34%	7.42%	7.61%	8.32%	8.12%
多元线性回归	35.13%	43.74%	36.46%	37.13%	31.93%
BP神经网络	24.52%	35.83%	26.18%	27.12%	30.82%

A big data framework for short-term power load forecasting using heterogenous data

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