考慮概率區(qū)間的微電網(wǎng)短期負荷多目標預測方法
發(fā)布時間：2018-12-20 15:15
【摘要】：微電網(wǎng)負荷隨機性強、波動大,負荷單點預測已經(jīng)難以滿足微電網(wǎng)穩(wěn)定運行需要.提出一種考慮概率區(qū)間的微電網(wǎng)短期負荷多目標預測方法,以循環(huán)神經(jīng)網(wǎng)絡為預測模型,以逼近理想解排序策略、網(wǎng)格篩選策略對基本多目標人工蜂群算法進行改進,優(yōu)化循環(huán)神經(jīng)網(wǎng)絡的權值和閾值,避免單目標區(qū)間預測中懲罰系數(shù)難以選擇的問題,對歷史負荷數(shù)據(jù)進行記憶并修正預測結果,有效提高微電網(wǎng)短期負荷區(qū)間預測準確性與可靠性.仿真結果表明,本文所構建的考慮概率區(qū)間的微電網(wǎng)短期負荷多目標預測方法,預測性能優(yōu)越、結果準確,可為微電網(wǎng)安全經(jīng)濟調度提供決策依據(jù).
[Abstract]:The load of microgrid has strong randomness and large fluctuation, so it is difficult to meet the need of stable operation of micro-grid by single-point load forecasting. This paper presents a multi-objective forecasting method for short-term load of microgrid considering probabilistic interval. Cyclic neural network is used as the prediction model, and the basic multi-objective artificial bee colony algorithm is improved by approximate ideal solution ranking strategy and grid screening strategy. The weights and thresholds of the cyclic neural network are optimized to avoid the problem that the penalty coefficient is difficult to select in the single-objective interval prediction. The historical load data are memorized and the prediction results are corrected. The accuracy and reliability of short-term load interval forecasting for microgrid are improved effectively. The simulation results show that the multi-objective forecasting method for microgrid short-term load based on probabilistic interval is superior in performance and accurate and can provide decision basis for safe and economic dispatch of microgrid.
【作者單位】： 江南大學物聯(lián)網(wǎng)技術應用教育部工程研究中心;
【基金】：國家自然科學基金(No.61579167,No.61572237) 高等學校博士學科點專項科研基金(No.20130093110011)
【分類號】：TP18;TM715
[Abstract]:The load of microgrid has strong randomness and large fluctuation, so it is difficult to meet the need of stable operation of micro-grid by single-point load forecasting. This paper presents a multi-objective forecasting method for short-term load of microgrid considering probabilistic interval. Cyclic neural network is used as the prediction model, and the basic multi-objective artificial bee colony algorithm is improved by approximate ideal solution ranking strategy and grid screening strategy. The weights and thresholds of the cyclic neural network are optimized to avoid the problem that the penalty coefficient is difficult to select in the single-objective interval prediction. The historical load data are memorized and the prediction results are corrected. The accuracy and reliability of short-term load interval forecasting for microgrid are improved effectively. The simulation results show that the multi-objective forecasting method for microgrid short-term load based on probabilistic interval is superior in performance and accurate and can provide decision basis for safe and economic dispatch of microgrid.
【作者單位】： 江南大學物聯(lián)網(wǎng)技術應用教育部工程研究中心;
【基金】：國家自然科學基金(No.61579167,No.61572237) 高等學校博士學科點專項科研基金(No.20130093110011)
【分類號】：TP18;TM715
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