Telecommunications Science ›› 2022, Vol. 38 ›› Issue (12): 103-111.doi: 10.11959/j.issn.1000-0801.2022292

• Research and Development • Previous Articles     Next Articles

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

Haibo ZHAO1, Zhijun XIANG2, Linsong XIAO1   

  1. 1 Willfar Information Technology Co., Ltd., Changsha 410205, China
    2 China Electronic Power Equipment and Technology Co., Ltd., Beijing 100052, China
  • Revised:2022-12-06 Online:2022-12-20 Published:2022-12-01
  • Supported by:
    The National Key Research and Development Program of China(2020YFB0906000);The National Key Research and Development Program of China(2020YFB0906002)


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: 

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