def run(self):
sim = Simulator(self.location, self.year_choice, Windturbine(5), terrain_factor = self.terrain_factor )
sim.latitude = self.latitude
sim.longitude = self.longitude
if self.store_wt_out:
P_wt,_ = sim.calc_wind(self.windfeatures)
else:
P_wt = 0
if self.store_sp_out:
P_sp,_ = sim.calc_solar(Az=self.solarfeatures[2::3], Inc=self.solarfeatures[1::3],sp_area=self.solarfeatures[0::3], sp_eff=self.sp_eff)
else:
P_sp = 0
if self.store_total_out:
P_tot,_ = sim.calc_total_power(self.solarfeatures, self.windfeatures, self.sp_eff)
else:
P_tot = 0
data = {'Pwt':P_wt,'Psp': P_sp,'Ptot': P_tot}
self.write_data(data, self.filename)
evt = SimDoneEvent(myEVT_SIMDONE, -1, filename=self.filename)
wx.PostEvent(self.parent, evt)
wm_price = 5350000
elif (wm_type == 1):
wm_price = 535000
elif (wm_type == 4):
wm_price = 3210000
else:
wm_price = 0
return wm_price
# code example to test if storage and deficit calculations are working
if __name__ == '__main__':
from Simulator import Simulator
from generators import Windturbine
turbine = Windturbine(4)
sim = Simulator('formatted_data.xls', '1%overschrijding-B.2', turbine)
sp_price_1 = 450
storage_price_1 = 1
print('Training: 1')
cost_calculator = CostCalculator(sp_price_1, storage_price_1, 6000, 1000000, 1000)
n_turb = 10
solar_feat = list([107806, 0, 0, 24175, 0, 0, 19751, 0, 0, 10000, 0, 0, ])
wind_feat = list([n_turb, 0.12])
output = sim.calc_total_power(solar_feat, wind_feat)
stats = cost_calculator.get_stats(output, np.sum(solar_feat[0::3]), 4, n_turb)
print('Stats: ')
print(stats)
def train(n_generations,
group_size,
surface_min,
surface_max,
angle_min,
angle_max,
orientation_min,
orientation_max,
model_name=None,
load=False,
counter=None,
directory=None,
mutationPercentage=50,
target_kw=6000,
cost_calculator=None,
simulator=None,
windturbineType=4,
N_WIND_MAX=100,
tr_rating=0.12,
sp_efficiency=16,
toScreen=False):
"""train genetic algorithm"""
genetic_algorithm = GeneticAlgorith(mutationPercentage, 150, 6, 2, 2, True)
# parameter 2 kosten voor accu per kWh
if cost_calculator is None:
cost_calculator = CostCalculator(190, 400, target_kw, 1000000, 1000,
3210000)
turbine = Windturbine(windturbineType)
if simulator is None:
location = Location("NEN")
simulator = Simulator(location, '2018', turbine)
saver = PopulationSaver(model_name, load)
if load:
group_values = saver.load()
else:
# generate random values in valid range
solar_values = np.random.rand(group_size, N_SOLAR_FEATURES)
solar_values[:, 0::3] *= (surface_max - surface_min)
solar_values[:, 0::3] += surface_min
solar_values[:, 1::3] *= (angle_max - angle_min)
solar_values[:, 1::3] += angle_min
solar_values[:, 2::3] *= (orientation_max - orientation_min)
solar_values[:, 2::3] += orientation_min
wind_values = np.random.rand(group_size, N_WIND_FEATURES)
wind_values[0] *= N_WIND_MAX
wind_values[1] *= (WIND_HEIGHT_MAX - WIND_HEIGHT_MIN)
wind_values[1] += WIND_HEIGHT_MIN
group_values = np.concatenate((solar_values, wind_values),
axis=1) # concatenate on features
# prepare min and max arrays to truncate values later
highest_allowed = np.zeros_like(group_values)
lowest_allowed = np.zeros_like(group_values)
highest_allowed[:, 0:N_SOLAR_FEATURES:3] = surface_max
lowest_allowed[:, 0:N_SOLAR_FEATURES:3] = surface_min
highest_allowed[:, 1:N_SOLAR_FEATURES:3] = angle_max
lowest_allowed[:, 1:N_SOLAR_FEATURES:3] = angle_min
highest_allowed[:, 2:N_SOLAR_FEATURES:3] = orientation_max
lowest_allowed[:, 2:N_SOLAR_FEATURES:3] = orientation_min
highest_allowed[:, -2] = N_WIND_MAX
lowest_allowed[:, -2] = 0
highest_allowed[:, -1] = WIND_HEIGHT_MAX
lowest_allowed[:, -1] = WIND_HEIGHT_MIN
last_generation = n_generations - 1
best_gen = 0
cost_temp = 1e20
for generation in range(saver.generation, n_generations):
if generation == n_generations - 20:
genetic_algorithm.set_mutation(mutationPercentage / 2)
elif generation == n_generations - 10:
genetic_algorithm.set_mutation(mutationPercentage / 4)
cost_array = np.zeros(group_size)
if toScreen:
print('finished simulation 0 of {}'.format(group_size), end='\r')
for i in range(group_size):
current_row = group_values[i]
# selecting windturbine type
wm_type = 4
n_Turbines = int(current_row[-2])
turbine_height = int(current_row[-1])
# run simulink
energy_production, energy_split = simulator.calc_total_power(
current_row[:N_SOLAR_FEATURES],
list([n_Turbines, turbine_height]), sp_efficiency)
# energy_production = simulator.calc_total_power(current_row[:N_SOLAR_FEATURES], list([n_Turbines, turbine_height]), sp_efficiency)
# run cost calculator
sp_sm = np.sum(current_row[0:N_SOLAR_FEATURES:3])
cost_array[i] = cost_calculator.calculate_cost(
energy_production, sp_sm, wm_type,
n_Turbines) # add turbine later
# print progress
if toScreen:
print('finished simulation {} of {}'.format(i + 1, group_size),
end='\r')
# log and print progress
best = genetic_algorithm.get_best(group_values, cost_array)
saver.log('generation:',
saver.generation,
'mean_cost:',
np.mean(cost_array),
'min_cost:',
np.min(cost_array),
to_screen=toScreen)
# store intermediate result
if np.min(cost_array) < cost_temp:
cost_temp = np.min(cost_array)
best_gen = best
saver.save_best(best)
if directory is not None:
directory.value = saver.path
if counter is not None:
counter.value = counter.value + 1
# quit when done
if generation == last_generation:
return best_gen
# run genetic algorithm
group_values = genetic_algorithm.generate_new_population(
group_values, cost_array)
# remove illegal values
group_values = np.minimum(group_values, highest_allowed)
group_values = np.maximum(group_values, lowest_allowed)
# store intermediate population
saver.save(group_values)
-45,
45,
mutationPercentage=mutationrate,
target_kw=energy_demand,
cost_calculator=cost_calc,
simulator=sim,
windturbineType=TURBINETYPE,
N_WIND_MAX=7,
tr_rating=loc_data.terrain,
sp_efficiency=16)
best_pick = best_array[0]
best_solar = best_pick[:12]
best_wind = best_pick[-2:]
best_pick_power, _ = sim.calc_total_power(best_solar, best_wind, 16)
total_solar_sm = np.sum(best_solar[0::3])
costings = cost_calc.calculate_cost(best_pick_power, total_solar_sm,
TURBINETYPE, int(best_wind[0]))
stats = cost_calc.get_stats(best_pick_power, total_solar_sm,
TURBINETYPE, int(best_wind[0]))
sol_power, _ = sim.calc_solar(Az=best_solar[2::3],
Inc=best_solar[1::3],
sp_area=best_solar[0::3])
win_power, _ = sim.calc_wind(best_wind)
win_power_total = np.sum(win_power)
sol_power_total = np.sum(sol_power)
inputs = {
'Name': loc_data.name,