Multiple response surface methodology (MRSM) most often involves the analysis of small sample size datasets which have associated inherent statistical modeling problems. Firstly, classical model selection criteria in ...Multiple response surface methodology (MRSM) most often involves the analysis of small sample size datasets which have associated inherent statistical modeling problems. Firstly, classical model selection criteria in use are very inefficient with small sample size datasets. Secondly, classical model selection criteria have an acknowledged selection uncertainty problem. Finally, there is a credibility problem associated with modeling small sample sizes of the order of most MRSM datasets. This work focuses on determination of a solution to these identified problems. The small sample model selection uncertainty problem is analysed using sixteen model selection criteria and a typical two-input MRSM dataset. Selection of candidate models, for the responses in consideration, is done based on response surface conformity to expectation to deliberately avoid selection of models using the problematic classical model selection criteria. A set of permutations of combinations of response models with conforming response surfaces is determined. Each combination is optimised and results are obtained using overlaying of data matrices. The permutation of results is then averaged to obtain credible results. Thus, a transparent multiple model approach is used to obtain the solution which gives some credibility to the small sample size results of the typical MRSM dataset. The conclusion is that, for a two-input process MRSM problem, conformity of response surfaces can be effectively used to select candidate models and thus the use of the problematic model selection criteria is avoidable.展开更多
【目的】揭示基于动物模型最佳线性无偏预测(animal model best linear unbiased prediction,AM-BLUP)的选择指数对杜洛克猪生长及繁殖性状的选育效果。【方法】在采用AM-BLUP方法估计个体目标性状育种值基础上,以达100 kg体质量日龄(...【目的】揭示基于动物模型最佳线性无偏预测(animal model best linear unbiased prediction,AM-BLUP)的选择指数对杜洛克猪生长及繁殖性状的选育效果。【方法】在采用AM-BLUP方法估计个体目标性状育种值基础上,以达100 kg体质量日龄(相对权重0.7)和100 kg活体背膘厚(相对权重0.3)为主选性状构建选择指数,对1个闭锁的杜洛克猪群开展持续7年(2013—2019年)的选育,系统分析选育期间猪群6个生长及繁殖性状表型值、估计育种值(estimated breeding value,EBV)、选择指数及近交系数的变化。【结果】相较于2013年,2019年猪群达100 kg体质量日龄、100 kg活体背膘厚和30~100 kg料重比分别极显著缩短4.45 d、降低0.52 mm和降低0.05(P<0.01);初产和经产母猪的总产仔数分别提高0.99头(P<0.05)和1.02头(P>0.05),产活仔数分别提高0.72头和0.49头(P>0.05),21日龄窝重分别降低0.39 kg和提高6.20 kg(P>0.05);主选性状达100 kg体质量日龄和100 kg活体背膘厚的EBV分别极显著降低3.447和0.533(P<0.01),选择指数极显著提高23.62(P<0.01),除30~100 kg料重比外,其余辅选性状的EBV均获得了不同程度改进。选育结束时,群体平均近交系数为3.1973%,年均增量为0.4904%。【结论】基于AM-BLUP的指数选择可有效改良猪的生产性状,但不同性状的具体选择进展会因其遗传特性的不同而异。展开更多
Electricity load forecasting is an important part of power system dispatching.Accurately forecasting electricity load have great impact on a number of departments in power systems.Compared to electricity load simulati...Electricity load forecasting is an important part of power system dispatching.Accurately forecasting electricity load have great impact on a number of departments in power systems.Compared to electricity load simulation(white-box model),electricity load forecasting(black-box model)does not require expertise in building construction.The development cycle of the electricity load forecasting model is much shorter than the design cycle of the electricity load simulation.Recent developments in machine learning have lead to the creation of models with strong fitting and accuracy to deal with nonlinear characteristics.Based on the real load dataset,this paper evaluates and compares the two mainstream short-term load forecasting techniques.Before the experiment,this paper first enumerates the common methods of short-term load forecasting and explains the principles of Long Short-term Memory Networks(LSTMs)and Support Vector Machines(SVM)used in this paper.Secondly,based on the characteristics of the electricity load dataset,data pre-processing and feature selection takes place.This paper describes the results of a controlled experiment to study the importance of feature selection.The LSTMs model and SVM model are applied to one-hour ahead load forecasting and one-day ahead peak and valley load forecasting.The predictive accuracy of these models are calculated based on the error between the actual and predicted loads,and the runtime of the model is recorded.The results show that the LSTMs model have a higher prediction accuracy when the load data is sufficient.However,the overall performance of the SVM model is better when the load data used to train the model is insufficient and the time cost is prioritized.展开更多
文摘Multiple response surface methodology (MRSM) most often involves the analysis of small sample size datasets which have associated inherent statistical modeling problems. Firstly, classical model selection criteria in use are very inefficient with small sample size datasets. Secondly, classical model selection criteria have an acknowledged selection uncertainty problem. Finally, there is a credibility problem associated with modeling small sample sizes of the order of most MRSM datasets. This work focuses on determination of a solution to these identified problems. The small sample model selection uncertainty problem is analysed using sixteen model selection criteria and a typical two-input MRSM dataset. Selection of candidate models, for the responses in consideration, is done based on response surface conformity to expectation to deliberately avoid selection of models using the problematic classical model selection criteria. A set of permutations of combinations of response models with conforming response surfaces is determined. Each combination is optimised and results are obtained using overlaying of data matrices. The permutation of results is then averaged to obtain credible results. Thus, a transparent multiple model approach is used to obtain the solution which gives some credibility to the small sample size results of the typical MRSM dataset. The conclusion is that, for a two-input process MRSM problem, conformity of response surfaces can be effectively used to select candidate models and thus the use of the problematic model selection criteria is avoidable.
基金This work is supported by the ERA-NET Smart Energy System,Sustainable Energy Authority Ireland and Italian Ministry of Research with project N.ENSGPLUSREGSYS1800013.This publication has conducted with the financial support of the EVCHIP project under grant agreement 19/RDD/579。
文摘Electricity load forecasting is an important part of power system dispatching.Accurately forecasting electricity load have great impact on a number of departments in power systems.Compared to electricity load simulation(white-box model),electricity load forecasting(black-box model)does not require expertise in building construction.The development cycle of the electricity load forecasting model is much shorter than the design cycle of the electricity load simulation.Recent developments in machine learning have lead to the creation of models with strong fitting and accuracy to deal with nonlinear characteristics.Based on the real load dataset,this paper evaluates and compares the two mainstream short-term load forecasting techniques.Before the experiment,this paper first enumerates the common methods of short-term load forecasting and explains the principles of Long Short-term Memory Networks(LSTMs)and Support Vector Machines(SVM)used in this paper.Secondly,based on the characteristics of the electricity load dataset,data pre-processing and feature selection takes place.This paper describes the results of a controlled experiment to study the importance of feature selection.The LSTMs model and SVM model are applied to one-hour ahead load forecasting and one-day ahead peak and valley load forecasting.The predictive accuracy of these models are calculated based on the error between the actual and predicted loads,and the runtime of the model is recorded.The results show that the LSTMs model have a higher prediction accuracy when the load data is sufficient.However,the overall performance of the SVM model is better when the load data used to train the model is insufficient and the time cost is prioritized.
