[论文]评审意见.doc

论文需要对模型进行比较（与现有的预测模型的比较），以及各种方法的计算量，可以的话，考虑一下采用不同的数据进行计算分析。主要问题：1） 文献分析不够，现有SVM以及负荷预测的分析现状不够。2） SVM的原理介绍可以删除。3） 提及SVM预测的精度高，但是没有说明参考的那篇文献的结果，也没有进行证明。4） 图2中的各个阶段解释的不清楚。5） 论文最重要的问题是要将本文方法与其他传统方法以及优化方法在负荷预测中进行比较，这样才能证明本文的价值。6） 建议采用其他的数据对论文的方法进行测试，说明其优越性。7） 建议用一年的数据（这个可以不管）8） 论文中“forecasting accuracy of SVM is better that of other forecasting methods”必须提供证明，不行就删除吧，多个审稿人都有这个意见。9） “optimal training sample”的中文意思。10） 历史数据，输入向量SVR的参数以及输出变量的含义以及参数需要给出。11） 结论中说本文方法提高了准确度，而验证计算是采用重庆的负荷数据，需要说明的是重庆的天气是否随时间变化。12） 论文没有对比以下文献的方法与结果。Qun Zong , Wenjing Liu and Liqian Dou “ Parameter selection for SVR based on PSO”, Proceeding of the 6th World congress on intelligent control and automation, June 21-23, 2006, Dalian China. Wenbin Ma “ Power system short term load forecasting using improved support vector machines” 2008 International Symposium on Knowledge Acquisition and Modeling, DOI 10.1109/KAM 2008.68我个人的修改建议：论文的主要问题在于：1） 没有对现有预测方法的现状总结好。2） 没有与现有预测方法对比，这个是最关键的。这个做好了论文就该没问题了。3） 增加几组比较计算，就是用现有论文的数据进行计算 ，与其方法的结果比较，说明优越性。 Wie Chiang Hong “ Chaotic Particle swarm optimization algorithm in a support vector regression electric load forecasting.”. Energy Conversion and Management 50 (2009) 105-117.Huang Yue, Li Dan , Gao Liqun, Wang Hong Yuan “ A short term load forecasting approach based on support vector machine with adaptive particle swarm optimization algorithm, DOI 1-4244-27239/09, IEEE 2009. Ping Xie, Yuancheng Li “ Study of core vector regression and particle swarm optimization for rapid electric load forecasting” 2009 International conference on future biomedical information engineering, 978-1-4244-4692/09/The conclusion states that the method improves the accuracy of daily loads especially in the area where daily load is affected by weather”. It appears that the data was taken for a city in Chongqing. Does that city have varied weather conditions?historical load data, input samples/vectors. Training samples, SVR parameters and output variables are not providedReviewers Comments:Reviewer: 1Comments to the AuthorIt is not clear what the main contribution of the paper is. If the paper proposes a new AI-based prediction method, it should be first evaluated and judged in AI journals. If the paper contribution is the application of a novel technique in the field of load forecasting, objective comparisons are required to believe in its content. The paper structure and writing is very similar to a conference papers. The paper, in its current format is far from a journal paper. Literature review is poor and weakly represented. There are many interesting papers in the load forecasting literature not cited in the paper. Sections discussing the SVM background are unnecessary and can be deleted from the paper, as they add no scientific value to the paper. Citation of the key SVM resources is required in these sections. It has been claimed that the SVM forecasting accuracy is better than other techniques (e.g., NNs). This claim has not been supported and there is no citation.Section 3 describing the proposed method is not well represented. The stages shown in Fig. 2 have not been properly discussed. More details can improve the quality of the paper. To improve the quality of the paper, it is essential to compare the performance of the proposed method with other load forecasting techniques, in particular neural networks. It is not clear for the reader why one should use these methods instead of traditional load forecasting techniques. Comparative experiments can reveal the real power of the proposed method. It is very likely that other methods can produce very similar results. It is also important to report the computational requirements of the proposed method. As the proposed method combines different techniques to achieve the best results, it seems its computational requirement is higher than similar techniques. Finally, the performance of the proposed method should be tested using some other load forecasting datasets. There are many reports on successful applications of the SVM in different fields. To show these techniques work well for the load forecasting problem, further tests are required. The writing is Ok. There are some typos in the paper that should be corrected, e.g., Page 2, “Support vector regression base on PSO” should be “Support vector regression based on PSO”.Reviewer: 2Comments to the AuthorThe combination of clustering and forecasting techniques is an interesting approach.Provide more references.In Section 4 describe in more detail training data used and simulation results, also provide model performance evaluation for a greater time period, eg. 1 year.（这个可以不管）Reviewer: 3Comments to the AuthorThe paper needs major rework. Authors should address following:1) In introduction authors claims that “forecasting accuracy of SVM is better that that of other forecasting methods”, but they do not provide any reasoning or reference to substantiate their claim. This claim must be substantiated with some reference(s).2) It is not clear what does “optimal training sample” means. Does this “optimal” training set will contain enough variation to provide accurate prediction in the event when the inputs are far from the general pattern? 3) Information regarding the number and nature of historical load data, input samples/vectors. Training samples, SVR parameters and output variables are not provided. These details are required for anybody who wants to apply the same method and test it for comparison.4) Was the simulation carried out for just one day? The results would appear more promising if results are included under varied weather conditions (for instance, on a summer day, one rainy day and one normal day) and also for different dates. The authors must add more results for different days and varied weather condition.5) The conclusion states that the method improves the accuracy of daily loads especially in the area where daily load is affected by weather”. It appears that the data was taken for a city in Chongqing. Does that city have varied weather conditions?6) The results have been compared only with PSO-SVR model whereas there are several other well established methods available for load forecasting such as neural network /fuzzy logic combined with optimization methods. The results of the proposed method must be validated by comparing with some other standard method too.Reviewer: 4Comments to the AuthorThis work is not made interesting and useful to the power system community for several reasons as pointed in the following :1. Major work on the use of SVM to the forecasting, which includes a winning entry of EUNITE competition, has not been cited and discussed. B.-J. Chen, M.-W. Chang, and C.-J. Lin, “Load forecasting using supportvector machines: A study on EUNITE competition 2001,” IEEE Trans.Power Syst., vol. 19, no. 4, pp. 18211830, Nov. 2004.I.Nicholas Sapankeyvsh and Ravi Shankar “ Time series prediction using support vector regression : A survey” IEEE Computational intelligence magazine , pp. 24-38, May 2009.2. The initialization of PSO parameters and the training parameters such as swarm size, particle dimension, velocity range, no. of iterations, etc. is not given.3. Previous works on the use of SVM along with PSO for the purpose of forecasting of load must have been cited though such PSO based SVR models are already reported in the literature. Just mention of other applications of SVM with PSO on price / rain/wind/product demand/silicon content in metal / traffic accidents etc would have made manuscript more informative. The proposed approach should have been compared with at least a few of the recent approaches using PSO-SVR. e.g.Qun Zong , Wenjing Liu and Liqian Dou “ Parameter selection for SVR based on PSO”, Proceeding of the 6th World congress on intelligent control and automation, June 21-23, 2006, Dalian China. Wenbin Ma “ Power system short term load forecasting using improved support vector machines” 2008 International Symposium on Knowledge Acquisition and Modeling, DOI 10.1109/KAM 2008.68 Wie Chiang Hong “ Chaotic Particle swarm optimization algorithm in a support vector regression electric load forecasting.”. Energy Conversion and Management 50 (2009) 105-117.Huang Yue, Li Dan , Gao Liqun, Wang Hong Yuan “ A short term load forecasting approach based on support vector machine with adaptive particle swarm optimization algorithm, DOI 1-4244-27239/09, IEEE 2009. Ping Xie, Yuancheng Li “ Study of core vector regression and particle swarm optimization for rapid electric load forecasting” 2009 International conference on future biomedical information engineering, 978-1-4244-4692/09/4. Moreover, the following important hybrid model using SVM which gives reasonably good results, has not been discussed in the manuscript, e.g.Shu Fan and Luonan Chen, “ Short- term load forecasting based on an adaptive hybrid method” IEEE Trans. on Power System, vol . 21, no.1, Feb. 2006.5. The motivation for using particularly FCM with PSO is also not given.6. A few papers with better results have been reported in the literature recently using Wavelets along with SVM. It is surprising that the authors fail to notice their important contribution to the field of load forecasting.7. In fact results are presented only for the proposed combinations; PSO SVR and FCM PSO SVR. These results are not compared with those due to the benchmark methods such as EUNITE winning entry and other conventional or popular techniques to show superiority of the proposed combinations. Comparison with popular bench mark methods for instance EUNITE winning entry should not pose a problem as the software and data for applying EUNITE are easily accessible.8. The justification for using FCM based clustering is not provided. If clustering is not so important simple k-means clustering could have been used since the membership functions computed from FCM go unused.9. What way the FCM SVR PSO combi