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)
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)
MAPE_sum += mape
RMSPR_sum += rmspe
days_sample = 365 - 1 - 56
MAPE_sum = MAPE_sum / days_sample
RMSPR_sum = RMSPR_sum / days_sample
print('AVERAGE MAPE: %.2f, RMSPE: %.2f' % (MAPE_sum, RMSPR_sum))