Generating capacity prediction of automatic tracking power generation system on inflatable membrane greenhouse attached photovoltaic

Xiaoli Xu, Qiushuang Liu, Mamoru Minami

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

A method which can forecast generating capacity of automatic tracking power system on inflatable membrane greenhouse attached photovoltaic was proposed based on the self-adaptive variation particle swarm neural network by adding with weather information. Firstly, through combining historical data of electricity production and meteorological data, the main factors of the impact on generating capacity of power generation system on inflatable membrane greenhouse attached photovoltaic was analyzed. Then, the neural network forecasting model was established by combining the weather forecast. The self-adaptive variation particle swarm algorithm was introduced to improve the training effect by tackling the problems of slowly converging, easily falling into local optimum, and difficultly converging existed in traditional neural network forecasting model based on gradient-descent BP algorithm. The neural network was optimized with adaptable mutation particle swarm optimization (AMPSO) algorithm. The mutation was put into particle swarm optimization (PSO) algorithm to find local optimal value. Experimental results showed that the entire convergence performance was significantly improved by adopting AMPSO and the premature convergence problem can be effectively avoided in PSO.

Original languageEnglish
JournalNongye Jixie Xuebao/Transactions of the Chinese Society of Agricultural Machinery
Volume43
Issue numberSUPPL.1
DOIs
Publication statusPublished - Oct 2012

Fingerprint

power generation
solar energy
Greenhouses
swarms
Particle swarm optimization (PSO)
Power generation
Neural networks
Membranes
greenhouses
neural networks
Neural Networks (Computer)
prediction
Weather
Mutation
mutation
meteorological data
Electricity
electricity
weather

Keywords

  • Automatic tracking photovoltaic power generation system
  • Inflatable membrane greenhouse attached photovoltaic
  • Prediction of generating capacity
  • Self-adaptive variation particle swarm neural network algorithm

ASJC Scopus subject areas

  • Mechanical Engineering
  • Agricultural and Biological Sciences(all)

Cite this

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title = "Generating capacity prediction of automatic tracking power generation system on inflatable membrane greenhouse attached photovoltaic",
abstract = "A method which can forecast generating capacity of automatic tracking power system on inflatable membrane greenhouse attached photovoltaic was proposed based on the self-adaptive variation particle swarm neural network by adding with weather information. Firstly, through combining historical data of electricity production and meteorological data, the main factors of the impact on generating capacity of power generation system on inflatable membrane greenhouse attached photovoltaic was analyzed. Then, the neural network forecasting model was established by combining the weather forecast. The self-adaptive variation particle swarm algorithm was introduced to improve the training effect by tackling the problems of slowly converging, easily falling into local optimum, and difficultly converging existed in traditional neural network forecasting model based on gradient-descent BP algorithm. The neural network was optimized with adaptable mutation particle swarm optimization (AMPSO) algorithm. The mutation was put into particle swarm optimization (PSO) algorithm to find local optimal value. Experimental results showed that the entire convergence performance was significantly improved by adopting AMPSO and the premature convergence problem can be effectively avoided in PSO.",
keywords = "Automatic tracking photovoltaic power generation system, Inflatable membrane greenhouse attached photovoltaic, Prediction of generating capacity, Self-adaptive variation particle swarm neural network algorithm",
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N2 - A method which can forecast generating capacity of automatic tracking power system on inflatable membrane greenhouse attached photovoltaic was proposed based on the self-adaptive variation particle swarm neural network by adding with weather information. Firstly, through combining historical data of electricity production and meteorological data, the main factors of the impact on generating capacity of power generation system on inflatable membrane greenhouse attached photovoltaic was analyzed. Then, the neural network forecasting model was established by combining the weather forecast. The self-adaptive variation particle swarm algorithm was introduced to improve the training effect by tackling the problems of slowly converging, easily falling into local optimum, and difficultly converging existed in traditional neural network forecasting model based on gradient-descent BP algorithm. The neural network was optimized with adaptable mutation particle swarm optimization (AMPSO) algorithm. The mutation was put into particle swarm optimization (PSO) algorithm to find local optimal value. Experimental results showed that the entire convergence performance was significantly improved by adopting AMPSO and the premature convergence problem can be effectively avoided in PSO.

AB - A method which can forecast generating capacity of automatic tracking power system on inflatable membrane greenhouse attached photovoltaic was proposed based on the self-adaptive variation particle swarm neural network by adding with weather information. Firstly, through combining historical data of electricity production and meteorological data, the main factors of the impact on generating capacity of power generation system on inflatable membrane greenhouse attached photovoltaic was analyzed. Then, the neural network forecasting model was established by combining the weather forecast. The self-adaptive variation particle swarm algorithm was introduced to improve the training effect by tackling the problems of slowly converging, easily falling into local optimum, and difficultly converging existed in traditional neural network forecasting model based on gradient-descent BP algorithm. The neural network was optimized with adaptable mutation particle swarm optimization (AMPSO) algorithm. The mutation was put into particle swarm optimization (PSO) algorithm to find local optimal value. Experimental results showed that the entire convergence performance was significantly improved by adopting AMPSO and the premature convergence problem can be effectively avoided in PSO.

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