Journal
ELECTRIC POWER SYSTEMS RESEARCH
Volume 211, Issue -, Pages -Publisher
ELSEVIER SCIENCE SA
DOI: 10.1016/j.epsr.2022.108226
Keywords
Big data analyses; Mini -batch stochastic gradient descent; Load forecasting; Distributed parallel computing; Map -Reduce framework
Categories
Funding
- National Natural Science Foundation of China [62063016]
- Science and Technology Plan of Gansu Province [20JR10RA177]
- Science and Technology Foundation of STATE GRID Corporation of China
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This paper proposes an improved regression model based on mini-batch stochastic gradient descent, combined with big data analysis and processing platform, to accelerate load forecasting. An adaptive sorted neighborhood method and K-means clustering method are also introduced to clean up duplicate and noise data.
Short-term Load Forecasting (STLF) is the basis of smart distribution network system operation, planning, and dispatching. The traditional linear regression prediction method has the problems of slow prediction speed and low prediction accuracy. In order to solve the problem, an improved regression model based on mini-batch stochastic gradient descent is proposed in this paper. Combined with the big data analysis and processing platform, the collected data is conformed, and the parallel computing model Map-Reduce is used to parallelize mini-batch stochastic gradient descent algorithm for improving the processing ability of mini-batch stochastic gradient descent algorithm in big data load forecasting, and shorten load forecasting time. Meanwhile, in order to clean up the duplicated data and bad data generated by the smart meter and sensor before calculation, an adaptive sorted neighborhood method is proposed to detect the repeatedly recorded data, and the K-means clustering method is used to eliminate the noise data .The experimental results show that the parallelized minibatch stochastic gradient descent algorithm is much faster than the traditional regression analysis algorithm when the data volume is large. The average absolute percentage error of the load forecasting model for Belgium and a transformer station in Baiyin city of Gansu Province in China is 1.902% and 2.058% respectively, which satisfies the requirements of load forecasting.
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