def main():
gamma = 0.89
eta = 0.9
day_chunk = 2
total_years = 1
episode_number = 0
E_cap = 6.0
P_cap = 3.0
E_init = 0.3*E_cap
epsilon = 0.5
actions = np.arange(-P_cap, P_cap + 0.01, 0.5).tolist()
actions.sort()
total_number_hours = 24
look_ahead = 1
savings = list()
#Creation of objects
with open('model_store_k1_summer') as fp:
models = pickle.load(fp)
#starting main episode loop
total_iterations = 1#total_years * day_chunk #day_chunk*total_years
for i in range(50):
grid_list = []
reward_list = list()
action_list = list()
energy_list = list()
price_list = list()
load_action_list = list()
episode_number = 0
environment = Environment(gamma, eta, day_chunk, total_years)
environment.setCurrentState(episode_number, E_init)
learningAgent = LearningAgent(environment.currentState, actions, E_cap, P_cap, epsilon)
funtionApproximator = FunctionApproximation('sgd', actions)
funtionApproximator.models = models
agent_bill, base_bill = 0.0, 0.0
while(episode_number < total_iterations) :
grid_list = list()
cost_list = list()
load_list = list()
netload_list = list()
solar_list = list()
add = False
for time in range(total_number_hours) :
energy_list.append(learningAgent.currentState[1])
action_sequence, rewardCumulative = learningAgent.getAction(episode_number, environment.currentState, funtionApproximator, environment, look_ahead, gamma, time)
if action_sequence[0]== None:
add = True
break
print "action action_sequence:",action_sequence
copy_current = deepcopy(environment.currentState)
nextState, qvalue, isValid = environment.nextStep(episode_number, time, [learningAgent.actions[action_index] for action_index in action_sequence], look_ahead, funtionApproximator, learningAgent)
action_taken = learningAgent.actions[action_sequence[0]]
print action_taken
#print('Episode', episode_number, 'current', learningAgent.currentState, 'action', action_taken,'next', nextState)
grid_list.append(environment.getP_grid(copy_current, action_taken))
action_list.append(action_taken)
netload_list.append(learningAgent.currentState[0])
load_list.append(environment.getLoad(episode_number, time))
solar_list.append(environment.getSolar(episode_number, time))
price_list.append(learningAgent.currentState[2]*100-4)
cost_list.append(learningAgent.currentState[2])
learningAgent.currentState = nextState
episode_number += 1
reward_list.append(rewardCumulative)
agent_bill = sum([a*b for a,b in zip(cost_list, grid_list)])
base_bill = sum([max(0, a*b) for a,b in zip(cost_list, netload_list)])
if not add :
savings.append(base_bill - agent_bill)
print savings, len(savings)
print 'Mean Savings:', np.mean(savings), '+/-', np.std(savings)
#sns.distplot(savings)
plt.plot(savings)
plt.show()
plt.plot(grid_list, label='Grid', c='r')
plt.plot(action_list, label='Charge/Discharge', c='c')
plt.plot(load_list, label='Load')
plt.plot(price_list, label='Price (Scaled up)')
plt.plot(solar_list, label='Solar Power', c='y')
plt.legend(loc=1, mode="expand", borderaxespad=0.)
plt.xticks(np.arange(1, len(grid_list)+1, 1.0))
plt.yticks(np.arange(-3,11, 0.5))
plt.xlabel('Hours')
plt.ylabel('Grid')
plt.show()
