def getWeatherFeature():
data = readData.loadResidentialData()
sumLoad = np.zeros((35040,))
#userLoad = readData.getUserData(data, 0)
for i in range(144):
sumLoad += readData.getUserData(data, i)
load_1hr = changeInterval.From15minTo1hour(sumLoad)
# load weather data
#weather_data = pd.read_csv('F:/OneDrive/Load Forecast/residential/data/weather2013_Austin.csv')
weather_data = pd.read_csv('F:/SkyDrive/Load Forecast/residential/data/weather2013_Austin.csv')
# test correlations
corr_temp = sp.pearsonr(load_1hr, np.array(weather_data['temperature']))[0]
corr_humidity = sp.pearsonr(load_1hr, np.array(weather_data['humidity']))[0]
corr_pressure = sp.pearsonr(load_1hr, np.array(weather_data['pressure']))[0]
corr_precip = sp.pearsonr(load_1hr, np.array(weather_data['precip_intensity']))[0]
print(corr_temp, corr_humidity, corr_pressure, corr_precip)
# selected features: temperature, humidity, pressure
temperature = np.array(weather_data['temperature'])
temperature = normalize(temperature)
humidity = np.array(weather_data['humidity'])
humidity = normalize(humidity)
pressure = np.array(weather_data['pressure'])
pressure = normalize(pressure)
return(temperature, humidity, pressure)
plt.show()
print('MAPE: %.2f, RMSPE: %.2f' % (mape, rmspe))
tf.reset_default_graph()
sess.close()
return (mape, rmspe)
if __name__ == "__main__":
# import load data
data = readData.loadResidentialData()
sumLoad = np.zeros((35040, ))
#userLoad = readData.getUserData(data, 0)
for i in range(144):
sumLoad += readData.getUserData(data, i)
# import weather data
(temperature, humidity, pressure) = load_weather_corr.getWeatherFeature()
# import cycles data
(dailycycle, weeklycycle) = cycles.getCycles()
sumLoad = changeInterval.From15minTo1hour(sumLoad)
MAPE_sum = 0.0
RMSPR_sum = 0.0
for curr_day in range(56, 364):
print(curr_day)
(mape, rmspe) = RNN_LSTM(sumLoad, curr_day)
if __name__ == "__main__":
# parameters
T = 96
n_train = 365
n_lag = 2
# import load data
data = readData.loadResidentialData()
n_customer = data.shape[1]
# load sum, 2 years of data
sumLoad = np.zeros((365 * 2 * T,))
# sum up the load data
for i in range(n_customer):
customer_load = readData.getUserData(data, i)
sumLoad += np.nan_to_num(customer_load)
minLoad = np.min(sumLoad)
maxLoad = np.max(sumLoad)
sumLoad = (sumLoad - minLoad) / (maxLoad - minLoad)
# call clustering function
N_cluster = 3
(X_train0, y_train0, X_train1, y_train1, X_train2, y_train2, X_test0, X_test1, X_test2, y_test0, y_test1, y_test2) = clustering.Clustering(T, N_cluster, n_train, n_lag, sumLoad)
# neural network forecast
print("start NN forecast on group 0")
(MAPE0, RMSPE0, days0) = CNN_forecast(n_lag, T, X_train0, y_train0, X_test0, y_test0, maxLoad, minLoad)
print('forecast result group 0 : MAPE: %.2f, RMSPE: %.2f' % (MAPE0, RMSPE0))
