Short-term load forecasting is one of the important bases of power grid planning, so how to improve the accuracy of power load forecasting is the top priority of short-term load forecasting. In order to effectively im...
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ISBN:
(纸本)9781665464215
Short-term load forecasting is one of the important bases of power grid planning, so how to improve the accuracy of power load forecasting is the top priority of short-term load forecasting. In order to effectively improve the accuracy of power load forecasting, considering that there are many factors affecting power load, the combined model used in this paper will face the problems of too long training time and too complex a network structure of the model. This paper introduces the KPCA model, which can not guarantee the learning ability and generalization ability of a single kernel function at the same time. The MKPCA model based on the mixture of Gaussian radial kernel function and polynomial kernel function is proposed. The structure and specific steps of the RBFNN neural network are designed and the power load forecasting method based on the MKPCA-RBFNN model is proposed. Compared with the KPCA-RBFNN model through simulation experiments, it is verified that the prediction accuracy of the proposed model is superior to other traditional models, and it has practical promotion value and plays a role in scientific dataanalysis in the construction of distribution networks.
At present, the physical health of primary and secondary school students is declining while physical education plays an important role in improving students' physical quality. However, how to judge scientifically ...
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ISBN:
(纸本)9783030215620;9783030215613
At present, the physical health of primary and secondary school students is declining while physical education plays an important role in improving students' physical quality. However, how to judge scientifically and accurately students' exercise load and ensure students' exercise safety have become one of the constraints in physical education. The application of wearable devices can help teachers to understand students' exercise data in time, but the analysis procedure is complicated. Based on this, this paper puts forward a data analysis model of wearable devices in physical education. The four steps are as follows: (1) understand the whole (2) compare and observe (3) analysis and hypothesis (4) calibrate and test. And then taking Binhe Primary School in Zhejiang as an example, we apply this model to analysis the data.
In Taiwan, where residential and industrial areas are in close proximity, finding ways to effectively continuous monitor and manage water quality is an essential issue. This study established a total solution for an I...
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In Taiwan, where residential and industrial areas are in close proximity, finding ways to effectively continuous monitor and manage water quality is an essential issue. This study established a total solution for an Internet of things water quality monitoring network that integrates domestic miniaturized water quality monitoring sensors for real-time transport data of pH, temperature, conductivity, chemical oxygen demand, and copper ions. The data will be used to establish an analysismodel based on continuous monitoring of the nation's background concentration. We designed an automatic continuous monitoring and early warning analysis module for automatic analysis of environmental and instrumental anomalies for decision makers, a "pollution source analysis module" utilizing static and dynamic cross-environment data to swiftly trace upstream pollution sources, and a "pollution hotspot analysis module" to evaluate the impact area of pollutants, and immediate response measures to achieve early warning and swift evaluation for the prevention of water pollution. To do this, we installed 100 domestic miniaturized water monitoring devices in Taoyuan City for testing the solution. We found that the establishment of an Internet of things environment analysis and response model integrated with cross-environment analysis can be applied in water quality monitoring and management to assure improved environmental quality.
Moderated mediation models are used commonly in psychological research and other academic fields to model how and when effects occur. Researchers must choose which paths from the mediation model are moderated when spe...
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Moderated mediation models are used commonly in psychological research and other academic fields to model how and when effects occur. Researchers must choose which paths from the mediation model are moderated when specifying this type of model. This dissertation examines how model specification impacts statistical power and type I error rate for the index of moderated mediation. In a meta-analytic review, we found that six model specifications account for 85% of published moderated mediation analyses, so this dissertation focuses on those six models. When considering power and type I error rate, two attributes matter: the data analysis model, and the data generating process (DGP). In reference to the DGP, the data analysis model can either be correctly specified, over-specified, underspecified, or completely misspecified. A Monte Carlo simulation study was run to examine the impacts of model specification on power and type I error rate, and results were analyzed using multi-level logistic regression along with figures and tables. Over-specified models had lower statistical power to detect a significant index of moderated mediation compared to correctly specified models. Under-specified models had slightly higher power when moderation on the direct effect was omitted, but otherwise, under-specified models had much lower power than correctly specified models. Parameter bias was also unacceptably high for most under-specified models. Completely misspecified models generally still had acceptable type I error rates, with a notable exception of inflated type I error rates where moderation was omitted from the direct effect. Overall, while many published moderated mediation models may not have large enough sample sizes for adequate statistical power, over-specifying or under-specifying models can lead to lower statistical power as well, while complete model misspecification risks an inflated type I error rate.
Leakage of electricity will cause damage to power facilities and lead to large-scale power outage. This not only caused huge economic losses to the country, but also seriously damaged the order of the power supply mar...
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Leakage of electricity will cause damage to power facilities and lead to large-scale power outage. This not only caused huge economic losses to the country, but also seriously damaged the order of the power supply market, affected the normal life of the majority of users, and even threatened the normal operation of the State Grid. This paper expounds the important research significance of applying data mining to theft and leakage, and introduces the specific functions of each layer. The automatic recognition model is constructed based on LM Neural Network and cart decision tree respectively. The mining analysis is carried out on the same data set, and the accuracy of both can reach 94%. However, after using ROC curve evaluation, the offline area of LM Neural Network is larger, indicating that LM Neural Network has higher accuracy in user classification. (C) 2021 The Author(s). Published by Elsevier Ltd.
With the development of science and technology, computerized accounting and electronic books have replaced traditional books and vouchers. Traditional manual auditing has not been able to adapt to the development of i...
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ISBN:
(纸本)9780769547923
With the development of science and technology, computerized accounting and electronic books have replaced traditional books and vouchers. Traditional manual auditing has not been able to adapt to the development of information technology because of the complex electronic data in books to be audited. And computer auditing focuses on model constructing and analyzing from the macro and micro perspective, in order to analyze the audit data effectively and rationally.
This paper proposes a scientific model to explain the analysis process. We argue that dataanalysis is primarily a procedure to build understanding, and as such, it dovetails with the cognitive processes of the human ...
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This paper proposes a scientific model to explain the analysis process. We argue that dataanalysis is primarily a procedure to build understanding, and as such, it dovetails with the cognitive processes of the human mind. dataanalysis tasks closely resemble the cognitive process known as sensemaking. We demonstrate how dataanalysis is a sensemaking task adapted to use quantitative data. This identification highlights a universal structure within dataanalysis activities and provides a foundation for a theory of dataanalysis. The competing tensions of cognitive compatibility and scientific rigour create a series of problems that characterise the dataanalysis process. These problems form a useful organising model for the dataanalysis task while allowing methods to remain flexible and situation dependent. The insights of this model are especially helpful for consultants, applied statisticians and teachers of dataanalysis.
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