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.step(xaxis, y_test.flatten(), 'g')
red_patch = mpatches.Patch(color='red', label='prediction')
green_patch = mpatches.Patch(color='green', label='actual')
plt.legend(handles=[red_patch, green_patch])
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
