Data mining has been successfully applied in many fields to find useful information stored in vast databases. Market segmentation, which segments data into homogenous clusters by using cluster analysis, is among the m...Data mining has been successfully applied in many fields to find useful information stored in vast databases. Market segmentation, which segments data into homogenous clusters by using cluster analysis, is among the most important of the applications in data mining. In this study, we propose a clustering system which integrates particle swarm optimization and honey bee mating optimization methods （PSHBMO）. Simulations for a benchmark test function show that our proposed method is better equipped to find the global optimum than other well-known clustering algorithms. Finally, the proposed clustering system is applied to a real-world consumer electronic company to perform market segmentation via the RFM model.展开更多
Renewable energy sources(RES)such as wind turbines(WT)and solar cells have attracted the attention of power system operators and users alike,thanks to their lack of environmental pollution,independence of fossil fuels...Renewable energy sources(RES)such as wind turbines(WT)and solar cells have attracted the attention of power system operators and users alike,thanks to their lack of environmental pollution,independence of fossil fuels,and meager marginal costs.With the introduction of RES,challenges have faced the unit commitment(UC)problem as a traditional power system optimization problem aiming to minimize total costs by optimally determining units’inputs and outputs,and specifying the optimal generation of each unit.The output power of RES such as WT and solar cells depends on natural factors such as wind speed and solar irradiation that are riddled with uncertainty.As a result,the UC problem in the presence of RES faces uncertainties.The grid consumed load is not always equal to and is randomly different from the predicted values,which also contributes to uncertainty in solving the aforementioned problem.The current study proposes a novel two-stage optimization model with load and wind farm power generation uncertainties for the security-constrained UC to overcome this problem.The new model is adopted to solve the wind-generated power uncertainty,and energy storage systems(ESSs)are included in the problem for further management.The problem is written as an uncertain optimization model which are the stochastic nature with security-constrains which included undispatchable power resources and storage units.To solve the UC programming model,a hybrid honey bee mating and bacterial foraging algorithm is employed to reduce problem complexity and achieve optimal results.展开更多
文摘Data mining has been successfully applied in many fields to find useful information stored in vast databases. Market segmentation, which segments data into homogenous clusters by using cluster analysis, is among the most important of the applications in data mining. In this study, we propose a clustering system which integrates particle swarm optimization and honey bee mating optimization methods （PSHBMO）. Simulations for a benchmark test function show that our proposed method is better equipped to find the global optimum than other well-known clustering algorithms. Finally, the proposed clustering system is applied to a real-world consumer electronic company to perform market segmentation via the RFM model.
文摘Renewable energy sources(RES)such as wind turbines(WT)and solar cells have attracted the attention of power system operators and users alike,thanks to their lack of environmental pollution,independence of fossil fuels,and meager marginal costs.With the introduction of RES,challenges have faced the unit commitment(UC)problem as a traditional power system optimization problem aiming to minimize total costs by optimally determining units’inputs and outputs,and specifying the optimal generation of each unit.The output power of RES such as WT and solar cells depends on natural factors such as wind speed and solar irradiation that are riddled with uncertainty.As a result,the UC problem in the presence of RES faces uncertainties.The grid consumed load is not always equal to and is randomly different from the predicted values,which also contributes to uncertainty in solving the aforementioned problem.The current study proposes a novel two-stage optimization model with load and wind farm power generation uncertainties for the security-constrained UC to overcome this problem.The new model is adopted to solve the wind-generated power uncertainty,and energy storage systems(ESSs)are included in the problem for further management.The problem is written as an uncertain optimization model which are the stochastic nature with security-constrains which included undispatchable power resources and storage units.To solve the UC programming model,a hybrid honey bee mating and bacterial foraging algorithm is employed to reduce problem complexity and achieve optimal results.
