[1]WANG Fenghua,CHENG Jingzhou,WEN Fan.Fast double nonconvex regression algorithm for forecast of electric power data[J].CAAI Transactions on Intelligent Systems,2018,13(4):665-672.[doi:10.11992/tis.201708033]
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Fast double nonconvex regression algorithm for forecast of electric power data

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 13 Number of periods: 2018 4 Page number: 665-672 Column: 学术论文—智能系统 Public date: 2018-07-05
Title:
Fast double nonconvex regression algorithm for forecast of electric power data
Author(s):
WANG Fenghua1;  CHENG Jingzhou1;  WEN Fan2
1. State Grid Zhejiang Electric Power Company, Hangzhou 310000, China;
2. Economic Research Institute, State Grid Zhejiang Electric Power Company, Hangzhou 310000, China
Keywords:
alternating direction method of multiplier (ADMM); forecast of electric power data; lp norm constraint; iterative threshold method
CLC:
TP18;TM715
DOI:
10.11992/tis.201708033
Abstract:
In this paper, we propose a new forecasting algorithm called double nonconvex regression (DNR) for the fast forecast of electricity power data such as the outputs of production ability and operational benefit. First, we reinterpret the typical sparse coding classification method as a regression model for forecasting, and further divide the model into training and testing phases to fit scalar-quantity forecasts. Next, we transform the constraints of representation residuals and coefficient regularization into a nonconvex lp norm for better approximation and broader application. Lastly, we adopt the alternating direction method of multipliers algorithm to optimize the formulated forecast problem. To achieve a fast update rule for lp norm constrained subproblems, we propose a new iterative threshold method that avoids the local minimum issue. Compared with typical methods such as the SVM, BP neural network, and nonconvex regularization methods, the proposed algorithm achieves surprisingly good experimental results for electricity power data.
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