人工神經(jīng)網(wǎng)絡(luò)在短期負(fù)荷預(yù)測(cè)中的應(yīng)用-------外文翻譯.doc
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人工神經(jīng)網(wǎng)絡(luò)在短期負(fù)荷預(yù)測(cè)中的應(yīng)用-------外文翻譯,abstract：we discuss the use of artificial neural networks to the short term forecasting of loads. in this system, there are two types of neural networks: non-li...
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Abstract：
We discuss the use of artificial neural networks to the short term forecasting of loads. In this system, there are two types of neural networks: non-linear and linear neural networks. The nonlinear neural network is used to capture the highly non-linear relation between the load and various input parameters. A neural networkbased ARMA model is mainly used to capture the load variation over a very short time period. Our system can achieve a good accuracy in short term load forecasting.
Key words: short-term load forecasting, artificial neural network
1、Introduction
Short term (hourly) load forecasting is an essential hction in electric power operations. Accurate shoirt term load forecasts are essential for efficient generation dispatch, unit commitment, demand side management, short term maintenance scheduling and other purposes. Improvements in the accuracy of short term load forecasts can result in significant financial savings for utilities and cogenerators.
Various teclmiques for power system load forecasting have been reported in literature. Those include: multiple linear regression, time series, general exponential smoothing, Kalman filtering, expert system, and artificial neural networks. Due to the
摘要：
在本文，我們將討論如何利用人工神經(jīng)網(wǎng)絡(luò)對(duì)短期負(fù)荷進(jìn)行預(yù)測(cè)。在這類系統(tǒng)中，有兩種類型的神經(jīng)網(wǎng)絡(luò)：非線性和線性神經(jīng)網(wǎng)絡(luò)。非線性神經(jīng)網(wǎng)絡(luò)是用來(lái)捕獲負(fù)荷和各種輸入?yún)?shù)之間的高度非線性關(guān)系?；贏RMA模型的神經(jīng)網(wǎng)絡(luò)，主要用來(lái)捕捉很短的時(shí)間期限內(nèi)負(fù)載的變化。我們的系統(tǒng)可以實(shí)現(xiàn)準(zhǔn)確性高的短期負(fù)荷預(yù)測(cè)。
關(guān)鍵詞：短期負(fù)荷預(yù)測(cè)，人工神經(jīng)網(wǎng)絡(luò)
1、緒論
短期（每小時(shí)）負(fù)荷預(yù)測(cè)對(duì)于電力系統(tǒng)的穩(wěn)定運(yùn)行是必要的。準(zhǔn)確的負(fù)荷預(yù)測(cè)對(duì)于高效的發(fā)電調(diào)度，開(kāi)停機(jī)計(jì)劃，需求方的管理，短時(shí)維護(hù)安排或其他目的等是很必要的。改進(jìn)短期負(fù)荷預(yù)測(cè)的準(zhǔn)確性能為公共事業(yè)和聯(lián)合發(fā)電節(jié)省很多開(kāi)支。
很多種電力系統(tǒng)負(fù)荷預(yù)測(cè)方法在學(xué)術(shù)界已經(jīng)報(bào)導(dǎo)了。這些方法包括：多元線性回歸法，時(shí)間序列法，一般指數(shù)平滑法，卡爾曼濾波法，專家系統(tǒng)法和人工神經(jīng)網(wǎng)絡(luò)預(yù)測(cè)法。由于電力負(fù)荷和各種參數(shù)（天氣的溫度，濕度，風(fēng)速等）之間的
We discuss the use of artificial neural networks to the short term forecasting of loads. In this system, there are two types of neural networks: non-linear and linear neural networks. The nonlinear neural network is used to capture the highly non-linear relation between the load and various input parameters. A neural networkbased ARMA model is mainly used to capture the load variation over a very short time period. Our system can achieve a good accuracy in short term load forecasting.
Key words: short-term load forecasting, artificial neural network
1、Introduction
Short term (hourly) load forecasting is an essential hction in electric power operations. Accurate shoirt term load forecasts are essential for efficient generation dispatch, unit commitment, demand side management, short term maintenance scheduling and other purposes. Improvements in the accuracy of short term load forecasts can result in significant financial savings for utilities and cogenerators.
Various teclmiques for power system load forecasting have been reported in literature. Those include: multiple linear regression, time series, general exponential smoothing, Kalman filtering, expert system, and artificial neural networks. Due to the
摘要：
在本文，我們將討論如何利用人工神經(jīng)網(wǎng)絡(luò)對(duì)短期負(fù)荷進(jìn)行預(yù)測(cè)。在這類系統(tǒng)中，有兩種類型的神經(jīng)網(wǎng)絡(luò)：非線性和線性神經(jīng)網(wǎng)絡(luò)。非線性神經(jīng)網(wǎng)絡(luò)是用來(lái)捕獲負(fù)荷和各種輸入?yún)?shù)之間的高度非線性關(guān)系?；贏RMA模型的神經(jīng)網(wǎng)絡(luò)，主要用來(lái)捕捉很短的時(shí)間期限內(nèi)負(fù)載的變化。我們的系統(tǒng)可以實(shí)現(xiàn)準(zhǔn)確性高的短期負(fù)荷預(yù)測(cè)。
關(guān)鍵詞：短期負(fù)荷預(yù)測(cè)，人工神經(jīng)網(wǎng)絡(luò)
1、緒論
短期（每小時(shí)）負(fù)荷預(yù)測(cè)對(duì)于電力系統(tǒng)的穩(wěn)定運(yùn)行是必要的。準(zhǔn)確的負(fù)荷預(yù)測(cè)對(duì)于高效的發(fā)電調(diào)度，開(kāi)停機(jī)計(jì)劃，需求方的管理，短時(shí)維護(hù)安排或其他目的等是很必要的。改進(jìn)短期負(fù)荷預(yù)測(cè)的準(zhǔn)確性能為公共事業(yè)和聯(lián)合發(fā)電節(jié)省很多開(kāi)支。
很多種電力系統(tǒng)負(fù)荷預(yù)測(cè)方法在學(xué)術(shù)界已經(jīng)報(bào)導(dǎo)了。這些方法包括：多元線性回歸法，時(shí)間序列法，一般指數(shù)平滑法，卡爾曼濾波法，專家系統(tǒng)法和人工神經(jīng)網(wǎng)絡(luò)預(yù)測(cè)法。由于電力負(fù)荷和各種參數(shù)（天氣的溫度，濕度，風(fēng)速等）之間的
