2019
DOI: 10.1049/iet-gtd.2019.1085
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Optimal DRPs selection using a non‐linear model based on load profile clustering

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Cited by 8 publications
(30 citation statements)
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“…Moazzen and other scholars classify and analyze the mental health support services of college students with different attributes, realize the differentiated management of college students' mental health problems, and then realize the matching analysis of their internal relevance. The experimental results show that this method has strong stability and customized analysis effect [ 13 ]. Qiao and other scholars used different levels of iterative stacking analysis to realize the normalized analysis of different degrees of mental health problems and proposed a hybrid stacking service strategy based on high-intensity memory analysis [ 14 ].…”
Section: Related Workmentioning
confidence: 99%
“…Moazzen and other scholars classify and analyze the mental health support services of college students with different attributes, realize the differentiated management of college students' mental health problems, and then realize the matching analysis of their internal relevance. The experimental results show that this method has strong stability and customized analysis effect [ 13 ]. Qiao and other scholars used different levels of iterative stacking analysis to realize the normalized analysis of different degrees of mental health problems and proposed a hybrid stacking service strategy based on high-intensity memory analysis [ 14 ].…”
Section: Related Workmentioning
confidence: 99%
“…A(i ) is the amount of incentive received to reduce the burden on time, and pen(i ) is the penalty to be paid for the non-reduced burden that will follow the customer's closed contract. The amount of incentive paid in the i th (A ′ ) period is as follows [16]:…”
Section: Extract Drps Models Using Copmentioning
confidence: 99%
“…Its maximum value will be obtained by Equation (10) deriving from the customer profit equation. Therefore [16]: (d()i)badbreak−B(dfalse(ifalse))goodbreak+d(i)p(i)goodbreak=A(normalΔdfalse(ifalse))goodbreak−pen(normalΔdfalse(ifalse))$$\begin{equation} ({d\left( i \right)}) - B({d(i)}) + d(i)p(i) = A^{\prime}({\Delta d(i)}) - pen^{\prime} ({{{\Delta}}d(i)})\end{equation}$$ B()d()id()ibadbreak=p()igoodbreak+A()igoodbreak+pen()i$$\begin{equation} \frac{{\partial B\left( {d\left( i \right)} \right)}}{{\partial d\left( i \right)}} = p\left( i \right) + A\left( i \right) + pen\left( i \right)\end{equation}$$…”
Section: Preliminaries and Problem Formulationmentioning
confidence: 99%
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