文件名称:06341870
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Abstract—This paper proposes a new strategy to meet the controllable
heating, ventilation, and air conditioning (HVAC) load
with a hybrid-renewable generation and energy storage system.
Historical hourly wind speed, solar irradiance, and load data are
used to stochastically model the wind generation, photovoltaic
generation, and load. Using fuzzy C-Means (FCM) clustering,
these data are grouped into 10 clusters of days with similar data
points to account for seasonal variations. In order to minimize
cost and increase efficiency, we use a GA-based optimization
approach together with a two-point estimate method. Minimizing
the cost function guarantees minimum PV and
heating, ventilation, and air conditioning (HVAC) load
with a hybrid-renewable generation and energy storage system.
Historical hourly wind speed, solar irradiance, and load data are
used to stochastically model the wind generation, photovoltaic
generation, and load. Using fuzzy C-Means (FCM) clustering,
these data are grouped into 10 clusters of days with similar data
points to account for seasonal variations. In order to minimize
cost and increase efficiency, we use a GA-based optimization
approach together with a two-point estimate method. Minimizing
the cost function guarantees minimum PV and
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06341870.pdf