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Application of chaotic particle swarm optimization in the Short-term Electricity Price Forecasting

Application of Chaotic Particle Swarm Optimization in the Sh...

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2011 Chinese Control and Decision Conference（CCDC）

作者： Jianhua Zhang:School of Control and Computer Engineering,North China Electric Power University,Changping District,Beijing 102206,China..Changhai Yu:School of Control and Computer Engineering,North China Electric Power University,Changping District,Beijing 102206,China.Guolian Hou:School of Control and Computer Engineering,North China Electric Power University,Changping District,Beijing 102206,China.

The market-oriented reform of the electric power industry is a trend around the world,electricity price issues are the key problems in the power markets and how to price the special commodity-electricity is essential for the smooth market operation.Accurate price forecasting provides crucial information for electricity market participants to make reasonable competing strategies,which is related to the position and benefit of the market participators.So using the relative historic data in predicting the future electricity price is a very meaningful work.With comprehensive considerations of the fluctuation rules and the various influencing factors on the forming of price in the power market,a short-term electricity price forecasting method based on the time series ARMAX model was chosen in this paper.Aimed to solve the problem with traditional method of parameter identification which is easy to fall into the local least values and has low identification precision,chaotic particle swarm optimization （CPSO） algorithm was proposed in this paper.Calculation example shows that this method can reflect the law of the development of the electricity price well and improve forecasting accuracy greatly.

关键词： Short-term electricity price forecasting ARMAX model chaotic particle swarm optimization algorithm

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A Novel PID Neural Network Controller based on Memristor

A Novel PID Neural Network Controller based on Memristor

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第36届中国控制会议

作者： GENG Yangyang DUAN Shukai DONG Zhekang WANG Lidan College of Electronics and Information Engineering Southwest University College of Electrical Engineering Zhejiang University

ISBN: (纸本)9781538629185

In this paper,an effective optimize method based on adaptive PID neural network controller is presented.chaotic particle swarm optimization（CPSO） is introduced to initialize the parameters of neural network for improving the convergent speed and preventing weights trapping into local optima.In order to realize the hardware platform of PID neural network controller and facilitate weight update algorithm,the nanoscale memristor is utilized to analog electronic synapse.Meanwhile,the stability of closed-loop system is further proved by Lyapunov theory.Based on several comparative experiments on automatic voltage regulation system,the final results illustrate that the proposed controller can obtain better precision in a shorter time.

关键词： Memristor adaptive PID neural network chaotic particle swarm optimization algorithm automatic voltage regulation system

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