from forecast import Forecast weather = Forecast() # weather.clean_tmy('../data/golden.csv') weather.load_tmy('../data/golden_clean.csv') weather.add_predictor('Dry-bulb (C)').add_predictor('Dew-point (C)') predictions = weather.auto_regressive(12, 2) print(predictions) weather.persist()
from forecast import Forecast import matplotlib.pyplot as plot import seaborn import pandas import numpy atlanta = Forecast() #weather.clean_tmy('../data/lafayette.CSV') atlanta.load_tmy('../data/atlanta_clean.csv') atlanta.simulation_time = '07/01/1900 08:00' #weather.add_predictor('GHI (W/m^2)').add_predictor('DNI (W/m^2)') atlanta.add_predictor('Dry-bulb (C)').add_predictor('Dew-point (C)') # now let's look at colorado: golden = Forecast() golden.load_tmy('../data/golden_clean.csv') golden.simulation_time = '07/01/1900 08:00' #weather.add_predictor('GHI (W/m^2)').add_predictor('DNI (W/m^2)') golden.add_predictor('Dry-bulb (C)').add_predictor('Dew-point (C)') """ # Grab predictions for 10, 50 and 100 scenarios: predictions, test = weather.auto_regressive(24, 10) print('...predictions succeeded, now plotting...') stuff = test[weather.predictor_variables[0]].T junk = test[weather.predictor_variables[1]].T
from forecast import Forecast import matplotlib.pyplot as plot import matplotlib.dates as mdates import seaborn import pandas import numpy # Create the forecast object (default horizon = 24 hours): weather = Forecast() # set horizon with Forecast(horizon=48) # load weather data: #weather.clean_tmy('../data/houston.csv') # this saves a 'cleaned' tmy file weather.load_tmy('../data/atlanta_clean.csv') # set the simulation time if necessary: weather.simulation_time = '07/01/1900 01:00' # Set up the VAR model by adding variables: weather.add_predictor('Dry-bulb (C)').add_predictor('RHum (%)') # Make a number of predictions and capture the predictions and the test data: predictions, test = weather.auto_regressive(72, 50, trim_data=False) # Extract a variable of interest (index values correspond to the order the variables were added): db = predictions[weather.predictor_variables[0]] rh = predictions[weather.predictor_variables[1]] # ...and the test data: dbtest = test[weather.predictor_variables[0]] rhtest = test[weather.predictor_variables[1]]