电力科学与工程
電力科學與工程
전력과학여공정
INFORMATION ON ELECTRIC POWER
2015年
5期
1-5
,共5页
自组织特征映射%特征提取%极限学习机%短期负荷预测
自組織特徵映射%特徵提取%極限學習機%短期負荷預測
자조직특정영사%특정제취%겁한학습궤%단기부하예측
self-organizing feature map%feature extraction%extreme learning machine%short-term load forecasting
随着电力负荷内涵复杂度和非线性增加，单纯追求电力负荷预测精度将变得困难。研究根据负荷样本分析其趋势、抽取特征来解决预测精度问题，即提出一种基于自组织特征映射网络（ SOM）进行特征提取并与极限学习机（ ELM）相结合的短期电力负荷预测方法。通过SOM特征提取找出与预测日同类型的历史数据作为训练样本；然后采用ELM进行预测，该方法预测过程简捷，能得到唯一的最优解。实验以某市的电力负荷数据进行仿真和比较。结果表明，基于SOM特征提取的ELM方法不仅精简了训练样本数量，且使训练更具有针对性，提高了预测精度和泛化性能，具有一定的理论意义和较好的应用前景。
隨著電力負荷內涵複雜度和非線性增加，單純追求電力負荷預測精度將變得睏難。研究根據負荷樣本分析其趨勢、抽取特徵來解決預測精度問題，即提齣一種基于自組織特徵映射網絡（ SOM）進行特徵提取併與極限學習機（ ELM）相結閤的短期電力負荷預測方法。通過SOM特徵提取找齣與預測日同類型的歷史數據作為訓練樣本；然後採用ELM進行預測，該方法預測過程簡捷，能得到唯一的最優解。實驗以某市的電力負荷數據進行倣真和比較。結果錶明，基于SOM特徵提取的ELM方法不僅精簡瞭訓練樣本數量，且使訓練更具有針對性，提高瞭預測精度和汎化性能，具有一定的理論意義和較好的應用前景。
수착전력부하내함복잡도화비선성증가，단순추구전력부하예측정도장변득곤난。연구근거부하양본분석기추세、추취특정래해결예측정도문제，즉제출일충기우자조직특정영사망락（ SOM）진행특정제취병여겁한학습궤（ ELM）상결합적단기전력부하예측방법。통과SOM특정제취조출여예측일동류형적역사수거작위훈련양본；연후채용ELM진행예측，해방법예측과정간첩，능득도유일적최우해。실험이모시적전력부하수거진행방진화비교。결과표명，기우SOM특정제취적ELM방법불부정간료훈련양본수량，차사훈련경구유침대성，제고료예측정도화범화성능，구유일정적이론의의화교호적응용전경。
With the increase of power load connotation complexity and nonlinearity, the pure pursuit of power load forecasting accuracy becomes more difficult.This paper aimed to improve prediction accuracy in line with the anal-ysis of load sample trend and feature extraction and proposed a short-term load forecasting method based on the combination of self-organizing feature mapping network ( SOM) feature extraction and extreme learning machine ( ELM) .First, the same type data as that on the forecasting day were selected as the training sample by using the feature extraction of SOM algorithm.Then, ELM was used for prediction, since the forecasting process of ELM is simple and can generate a unique optimal solution.The power load data of one city were used for simulating and comparing.The experimental results showed that ELM method based on SOM feature extraction downsized the num-ber of training samples, made the training more targeted, and improved forecasting accuracy and generalization per-formance.This method has certain theoretical significance and good application prospects.
