随着经济的发展,电力需求在全世界范围内越来越大,而其中清洁能源的发展占据了新能源开发的主导地位。在我国,风力发电是新能源发展的重中之重。可是风力发电的效率很难控制,基于风力大小的发电依赖于装机容量,或者说依赖于风电场准备...随着经济的发展,电力需求在全世界范围内越来越大,而其中清洁能源的发展占据了新能源开发的主导地位。在我国,风力发电是新能源发展的重中之重。可是风力发电的效率很难控制,基于风力大小的发电依赖于装机容量,或者说依赖于风电场准备发出多少电力。党风电场制造的电力高于实际需求时,由于电力的难于存储性,多余出的电力实际被浪费,当风电场制造的电力低于实际需求时,又会影响实际的工业发展与民用需求。考虑到风电场的装机容量之巨大,0.1个百分点的效率提升,都会给风电场带来巨大的经济利益。本文致力于应用组合模型于电力需求预测并得到精确的预测结果,从而指导实际运营中风电场的电力供给计划。在这篇文章中,ENNM(Elman Network Model)和ARSRM(Spline Rolling Auto-Regressive Model)被应用与短期电力数据预测与中长期电力数据预测。组合模型的测试在New South Wales的实际数据中测试。就在我们做出研究的期间,New South Wales的电力需求波动与6000k Wh与13000k Wh之间。我们通过对总体数据的分析,提出了一种新的基于电力卡方测试的分类方式。通过这种方式电力数据可以被分为7种。我们以字母A^G来命名分类后的数据。与此同时,数据会被分类为两个部分,其中的一个部分含有两个或两个以下的极值点,另一部分含有三个或三个以上的极值点,这种分类是为了帮助我们更好的研究数据特性并为我们能够更好的应用模型做出贡献。展开更多
Introduction： The effectiveness of treatment depends on the efficacy of the therapy and the level of compliance of the patient. NF （non-specific factors） involved in treatment are all those effects that do not depe...Introduction： The effectiveness of treatment depends on the efficacy of the therapy and the level of compliance of the patient. NF （non-specific factors） involved in treatment are all those effects that do not depend on the pharmacological properties of the drug. Materials and Methods： The job lasted a year. The sample consisted of patients with mental health disorders and was divided into two groups. Treatment compliance was measured with the Morisky-Green Test. Results were compared using the chi square test and relative risk. Results and Discussion： Group A had 23 patients ending 91.3% of them and group B of 22 patients, ending the 77.27%. At the beginning of the study, we found in the group A 0.86 NF/patients while in the group B 0.82 NF/patient. At the end of the study there was a 54.65% decrease in group A while in group B the proportion remained. At the beginning of the study both groups had approximately 40% of compliant patients. Data that remained in the control group rose to 80.95% in group A at the end of the study. Conclusions： The work demonstrates the negative influence of these factors on adherence to treatment.展开更多
文摘随着经济的发展,电力需求在全世界范围内越来越大,而其中清洁能源的发展占据了新能源开发的主导地位。在我国,风力发电是新能源发展的重中之重。可是风力发电的效率很难控制,基于风力大小的发电依赖于装机容量,或者说依赖于风电场准备发出多少电力。党风电场制造的电力高于实际需求时,由于电力的难于存储性,多余出的电力实际被浪费,当风电场制造的电力低于实际需求时,又会影响实际的工业发展与民用需求。考虑到风电场的装机容量之巨大,0.1个百分点的效率提升,都会给风电场带来巨大的经济利益。本文致力于应用组合模型于电力需求预测并得到精确的预测结果,从而指导实际运营中风电场的电力供给计划。在这篇文章中,ENNM(Elman Network Model)和ARSRM(Spline Rolling Auto-Regressive Model)被应用与短期电力数据预测与中长期电力数据预测。组合模型的测试在New South Wales的实际数据中测试。就在我们做出研究的期间,New South Wales的电力需求波动与6000k Wh与13000k Wh之间。我们通过对总体数据的分析,提出了一种新的基于电力卡方测试的分类方式。通过这种方式电力数据可以被分为7种。我们以字母A^G来命名分类后的数据。与此同时,数据会被分类为两个部分,其中的一个部分含有两个或两个以下的极值点,另一部分含有三个或三个以上的极值点,这种分类是为了帮助我们更好的研究数据特性并为我们能够更好的应用模型做出贡献。
文摘Introduction： The effectiveness of treatment depends on the efficacy of the therapy and the level of compliance of the patient. NF （non-specific factors） involved in treatment are all those effects that do not depend on the pharmacological properties of the drug. Materials and Methods： The job lasted a year. The sample consisted of patients with mental health disorders and was divided into two groups. Treatment compliance was measured with the Morisky-Green Test. Results were compared using the chi square test and relative risk. Results and Discussion： Group A had 23 patients ending 91.3% of them and group B of 22 patients, ending the 77.27%. At the beginning of the study, we found in the group A 0.86 NF/patients while in the group B 0.82 NF/patient. At the end of the study there was a 54.65% decrease in group A while in group B the proportion remained. At the beginning of the study both groups had approximately 40% of compliant patients. Data that remained in the control group rose to 80.95% in group A at the end of the study. Conclusions： The work demonstrates the negative influence of these factors on adherence to treatment.
