[論文] 評審意見

1、.論文需要對模型進行比較（與現(xiàn)有的預(yù)測模型的比較），以及各種方法的計算量，可以的話，考慮一下采用不同的數(shù)據(jù)進行計算分析。主要問題：1） 文獻分析不夠，現(xiàn)有SVM以及負(fù)荷預(yù)測的分析現(xiàn)狀不夠。2） SVM的原理介紹可以刪除。3） 提及SVM預(yù)測的精度高，但是沒有說明參考的那篇文獻的結(jié)果，也沒有進行證明。4） 圖2中的各個階段解釋的不清楚。5） 論文最重要的問題是要將本文方法與其他傳統(tǒng)方法以及優(yōu)化方法在負(fù)荷預(yù)測中進行比較，這樣才能證明本文的價值。6） 建議采用其他的數(shù)據(jù)對論文的方法進行測試，說明其優(yōu)越性。7） 建議用一年的數(shù)據(jù)（這個可以不管）8） 論文中“forecasting accuracy o

2、f SVM is better that of other forecasting methods”必須提供證明，不行就刪除吧，多個審稿人都有這個意見。9） “optimal training sample”的中文意思。10） 歷史數(shù)據(jù)，輸入向量SVR的參數(shù)以及輸出變量的含義以及參數(shù)需要給出。11） 結(jié)論中說本文方法提高了準(zhǔn)確度，而驗證計算是采用重慶的負(fù)荷數(shù)據(jù)，需要說明的是重慶的天氣是否隨時間變化。12） 論文沒有對比以下文獻的方法與結(jié)果。Qun Zong , Wenjing Liu and Liqian Dou “ Parameter selection for SVR based on P

3、SO”, 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我個人

4、的修改建議：論文的主要問題在于：1） 沒有對現(xiàn)有預(yù)測方法的現(xiàn)狀總結(jié)好。2） 沒有與現(xiàn)有預(yù)測方法對比，這個是最關(guān)鍵的。這個做好了論文就該沒問題了。3） 增加幾組比較計算，就是用現(xiàn)有論文的數(shù)據(jù)進行計算 ，與其方法的結(jié)果比較，說明優(yōu)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 Yu

5、e, 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 elect

6、ric 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 ci

7、ty 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 th

8、e 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

9、 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

10、 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 cita

11、tion.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 loa

12、d 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

13、 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

14、 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

15、 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 mo

16、re detail training data used and simulation results, also provide model performance evaluation for a greater time period, eg. 1 year.（這個可以不管）Reviewer: 3Comments to the AuthorThe paper needs major rework. Authors should address following:1) In introduction authors claims that “forecasting accuracy of

17、 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

18、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 requ

19、ired 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 als

20、o 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 Cho

21、ngqing. 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

22、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

23、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

24、“ 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. Prev

25、ious 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 a

26、ccidents 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 intelli

27、gent 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

28、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 1-4

29、244-27239/09, 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 usin

30、g 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 no

31、t 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;

32、 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 sh

33、ould 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 combin