Optimal DRPs selection using a non‐linear model based on load profile clustering

College Students’ Mental Health Support Based on Fuzzy Clustering Algorithm

“…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 ].…”

Optimal demand response programs selection using CNN‐LSTM algorithm with big data analysis of load curves

“…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]:…”

“…Its maximum value will be obtained by Equation (10) deriving from the customer profit equation. Therefore [16]: $$$$\begin{equation} ({d\left( i \right)}) - B({d(i)}) + d(i)p(i) = A^{\prime}({\Delta d(i)}) - pen^{\prime} ({{{\Delta}}d(i)})\end{equation}$$$$ $$$$\begin{equation} \frac{{\partial B\left( {d\left( i \right)} \right)}}{{\partial d\left( i \right)}} = p\left( i \right) + A\left( i \right) + pen\left( i \right)\end{equation}$$$$…”

“…The customer revenue function is defined by the Taylor series second‐order expansion of the power demand function as follows [16]: $$$$\begin{equation} B\left( i \right) = {B_0}(i) + \frac{{{p_0}\left( i \right)d\left( i \right)}}{{1 + E{{(i,i)}^{ - 1}}}}\left\{ {{{\left( {\frac{{d\left( i \right)}}{{{d_0}\left( i \right)}}} \right)}^{E{{(i,i)}^{ - 1}}}} - 1} \right\}\end{equation}$$$$where $$E( {i,i} )$$ is the matrix of elasticity at any time interval i , by simplifying the Equations (10) and (11), the one‐period model of the power demand function with its self‐elasticity will be as follows [16]: $$$$\begin{equation} d\left( i \right) = {d_0}\left( i \right){\left( {\frac{{p\left( i \right) + A\left( i \right) + pen\left( i \right)}}{{{p_{{{\rm{\;}}_0}}}\left( i \right)}}} \right)^{E\left( {i,i} \right)}}\end{equation}$$$$…”

“…Calculate the ideal ($${V_n}^ + $$), and counter‐ideal ($${V_n}^ - $$) answers [16]: $$$$\begin{equation} \def\eqcellsep{&}\begin{array}{@{}*{2}{l}@{}} { \def\eqcellsep{&}\begin{array}{*{20}{l}} {{V_n}^ + = \left( {{\rm{max}}{V_{mn}}\left| {n \in {V^ + }} \right|,{\rm{min}}{V_{mn}}\left| {n \in {V^ - }} \right|} \right)\;}&{m\; = \;1, \ldots ,z} \end{array} }\\[9pt] { \def\eqcellsep{&}\begin{array}{*{20}{l}} {{V_n}^ - = \left( {{\rm{min}}{V_{mn}}\left| {n \in {V^ + }} \right|,{\rm{\;max}}{V_{mn}}\left| {n \in {V^ - }} \right|} \right)\;}&{m\; = \;1, \ldots ,z} \end{array} } \end{array} \end{equation}$$$$Here, the type of criteria should be specified. The criteria are either positive or negative.…”