巴黎人票APP

          <dfn id='Umlicg'><optgroup id='Umlicg'></optgroup></dfn><tfoot id='Umlicg'><bdo id='Umlicg'><div id='Umlicg'></div><i id='Umlicg'><dt id='Umlicg'></dt></i></bdo></tfoot>

          <ul id='Umlicg'></ul>

          • 暖通空调杂志社>期刊目次>2017年>第2期

            基于遗传算法优化支持向量回归机参数的供热负荷预测

            Heating load prediction based on support vector regression machine with parameters optimized by genetic algorithm

            张佼[1],田琦[2],王美萍[2]
            [1]中国能源建设集团山西省电力勘测设计院有限公司,[2]太原理工大学

            摘要:

            为了进一步提高供热负荷的预测精度,通过分析影响支持向量回归机(SVR)性能表现的参数,提出了基于遗传算法优化的SVR供热负荷预测模型。该方法利用交叉验证思想在模型性能评估和选择方面的优势,结合遗传算法的全局寻优能力,实现了参数的自动优选,并用由此得到的最佳模型进行供热负荷预测。应用某热源的实测数据进行了仿真实验,与其他算法的比较表明,该方法相对误差绝对值的平均值为4.33%,比传统SVR降低了10.77%,比小波神经网络降低了5.28%。

            关键词:遗传算法支持向量回归机供热负荷预测参数优化交叉验证

            Abstract:

            In order to further improve the prediction accuracy of the heating load, analyses the influence of parameters on support vector regression machine (SVR), puts forward a model based on the SVR optimized by genetic algorithm for the heating load forecasting. The method takes advantage of cross validation in aspect of model performance evaluation and selection, combined with the ability of global optimization of genetic algorithm, realizes automatic selection of optimal parameters, and obtains the best model to forecast the heating load. In an experimental study on a heat source data, compares with other algorithms, the results show that the average absolute value of the relative error of the method is 4.33%, 10.77% lower than that of traditional SVR machine and 5.28% lower than that of wavelet neural network.

            Keywords:geneticalgorithm,supportvectorregressionmachine,heatingloadprediction,parameteroptimization,crossvalidation

                你还没注册?或者没有登录?这篇期刊要求至少是本站的注册会员才能阅读!

                如果你还没注册,请赶紧点此注册吧!

                如果你已经注册但还没登录,请赶紧点此登录吧!