@Article{cmc.2020.09812,
AUTHOR = {Hui Li, Cailin Shi, Xin Liu, Aziguli Wulamu, Alan Yang},
TITLE = {Three-Phase Unbalance Prediction of Electric Power Based on Hierarchical Temporal Memory},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {64},
YEAR = {2020},
NUMBER = {2},
PAGES = {987--1004},
URL = {http://www.techscience.com/cmc/v64n2/39341},
ISSN = {1546-2226},
ABSTRACT = {The difference in electricity and power usage time leads to an unbalanced 
current among the three phases in the power grid. The three-phase unbalanced is closely 
related to power planning and load distribution. When the unbalance occurs, the safe 
operation of the electrical equipment will be seriously jeopardized. This paper proposes a 
Hierarchical Temporal Memory (HTM)-based three-phase unbalance prediction model
consisted by the encoder for binary coding, the spatial pooler for frequency pattern 
learning, the temporal pooler for pattern sequence learning, and the sparse distributed 
representations classifier for unbalance prediction. Following the feasibility of spatialtemporal streaming data analysis, we adopted this brain-liked neural network to a real-time 
prediction for power load. We applied the model in five cities (Tangshan, Langfang, 
Qinhuangdao, Chengde, Zhangjiakou) of north China. We experimented with the proposed 
model and Long Short-term Memory (LSTM) model and analyzed the predict results and 
real currents. The results show that the predictions conform to the reality; compared to 
LSTM, the HTM-based prediction model shows enhanced accuracy and stability. The 
prediction model could serve for the overload warning and the load planning to provide
high-quality power grid operation.},
DOI = {10.32604/cmc.2020.09812}
}