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