科技和产业
科技和產業
과기화산업
SCIENCE TECHNOLOGY AND INDUSTRIAL
2014年
2期
146~150
,共null页
光伏发电预测 太阳辐射曲线匹配 时段分解 BP神经网络
光伏髮電預測 太暘輻射麯線匹配 時段分解 BP神經網絡
광복발전예측 태양복사곡선필배 시단분해 BP신경망락
photovoltaic power predictions solar radiation curve matching; period decompositions BP neural network
为了对光伏发电的输出功率进行预测,本文分析光伏发电的影响因素,提出了一种基于太阳辐射功率曲线匹配的预测模型.该模型将历史数据按时段进行分解,查找与当前时段太阳辐射功率曲线最为匹配的数据,以此构建并训练BP神经网络,来预测未来3个小时内的太阳辐射功率,能够较好的实现预测目标.实验结果表明,该模型有较高的精度,可对电网调度起到重要的指导作用.
為瞭對光伏髮電的輸齣功率進行預測,本文分析光伏髮電的影響因素,提齣瞭一種基于太暘輻射功率麯線匹配的預測模型.該模型將歷史數據按時段進行分解,查找與噹前時段太暘輻射功率麯線最為匹配的數據,以此構建併訓練BP神經網絡,來預測未來3箇小時內的太暘輻射功率,能夠較好的實現預測目標.實驗結果錶明,該模型有較高的精度,可對電網調度起到重要的指導作用.
위료대광복발전적수출공솔진행예측,본문분석광복발전적영향인소,제출료일충기우태양복사공솔곡선필배적예측모형.해모형장역사수거안시단진행분해,사조여당전시단태양복사공솔곡선최위필배적수거,이차구건병훈련BP신경망락,래예측미래3개소시내적태양복사공솔,능구교호적실현예측목표.실험결과표명,해모형유교고적정도,가대전망조도기도중요적지도작용.
To predict the power of photovoltaic power generation, we analyzed the factors of photovoltaic power generation and proposed a method of predicting photovohaic power generation power based on the curve matching of solar radiation. By decomposing the historical data, searching the solar radiation curve, using the BP neural network, this method could predict photovoltaic power generation power within the next three hours and have a good performance. Experimental results show that this model can have a small forecasting error, which means that it＇s important in electric network management.
