基于生物免疫优化支持向量机算法的居民区负荷预测
Load prediction of residential areas based on biological immuno-optimized Support Vector Machine algorithm
投稿时间:2022-11-19  修订日期:2023-03-03
DOI:
中文关键词:  SVM  PCA  免疫算法  负荷预测
英文关键词:SVM, PCA, Immune algorithm, Load forecasting
基金项目:KJ21-1-21基于多源数据融合的城市建筑体能源全局优化与互动调控技术研究项目
作者单位邮编
王坤 国网天津市电力公司电力科学研究院 300310
张利 国网天津市电力公司电力科学研究院 
赵学明 国网天津市电力公司城东供电分公司 
甘智勇 国网天津市电力公司电力科学研究院 
王森 国网天津市电力公司电力科学研究院 
田禾* 天津理工大学机械工程学院 300384
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中文摘要:
      针对居民区用电负荷随机性强、稳定性差等问题,综合考虑各因素对居民用电负荷的影响,提出一种免疫SVM(Support Vector Machine)算法负荷预测模型。以居民区历史用电量及相关气候数据为处理对象,使用PCA(Principal Component Analysis)算法对电网历史数据进行处理,并结合免疫算法对将历史电网数据进行预处理,形成数据簇并划定标签提供给预测模型进行训练。为提高模型精度,采用生物免疫优化算法对SVM模型参数进行优化,并在负荷预测环节,将预测误差作为调优依据,对预测模型进行反馈调优。将预测效果与常用于负荷预测的BP(back propagation)神经网络、SVM算法模型进行对比,免疫SVM算法负荷预测模型的短时、中时预测精准度均在98%以上,具有较好的精度与鲁棒性。
英文摘要:
      A prediction model for electric load based on an immune Support Vector Machine (SVM) algorithm is proposed to address the issues of high randomness and poor stability in the electric load of residential areas. Considering various factors that affect the electric load of residents, the historical electric consumption and relevant climate data of residential areas are used as the processing objects. The Principal Component Analysis (PCA) algorithm is utilized to preprocess the historical data of the power grid, and the immune algorithm is combined to preprocess the data by forming data clusters and defining labels for training the prediction model. To improve the accuracy of the model, the biological immune optimization algorithm is used to optimize the parameters of the SVM model. In the load prediction process, the prediction error is used as the basis for feedback tuning of the prediction model. The prediction performance of the immune SVM algorithm load prediction model is compared with that of the commonly used Back Propagation (BP) neural network and SVM algorithm model. The short-term and medium-term prediction accuracies of the immune SVM algorithm load prediction model are both above 98%, demonstrating good accuracy and robustness.
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