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| # Import gradio | |
| import gradio as gr | |
| # Data import from open-meteo.com | |
| import openmeteo_requests | |
| import requests_cache | |
| from retry_requests import retry | |
| # Data management and visualization | |
| import pandas as pd | |
| import numpy as np | |
| import datetime | |
| import pickle | |
| import matplotlib | |
| import matplotlib.pyplot as plt | |
| import matplotlib.dates as mdates | |
| # Machine Learning | |
| import sklearn | |
| # Print version info | |
| print("Version info:") | |
| print('pandas: %s' % pd.__version__) | |
| print('numpy: %s' % np.__version__) | |
| print('sklearn: %s' % sklearn.__version__) | |
| print(" ") | |
| def makeprediction(): | |
| #%% 1. User Inputs | |
| # These inputs may be changed by the user | |
| model_file_name = "Solar RF Model_2024-01-03T08-53-53_.pkl" # This is the name of the file generated by the script "Build_solar_RF_model.py" (must be located in same folder) | |
| horizon = 2 # Forecast horizon in days, limit is 16 days. | |
| # Note that the further we look into the future, the less accurate the weather forecast is likely to be | |
| PV_capacity = 15 # The maximum capacity of your PV installation in kW, only used for plotting. | |
| #%% 2. Read Model | |
| # Read in the model | |
| f_model = open(model_file_name, 'rb') # Opens the file | |
| model_dict = pickle.load(f_model) # Reads the dictionary from the file | |
| f_model.close() # closes the file again | |
| #%% 3. Fetch weather forecast | |
| # Setup the Open-Meteo API client with cache and retry on error | |
| cache_session = requests_cache.CachedSession('.cache', expire_after = -1) | |
| retry_session = retry(cache_session, retries = 5, backoff_factor = 0.2) | |
| openmeteo = openmeteo_requests.Client(session = retry_session) | |
| # Make sure all required weather variables are listed here | |
| url = "https://api.open-meteo.com/v1/forecast" | |
| # Calcualte start and end date | |
| start_date = datetime.datetime.now() | |
| end_date = start_date + datetime.timedelta(days=horizon) | |
| sdate_str = str(start_date)[0:10] # First date in file as string (yyyy-mm-dd) | |
| edate_str = str(end_date)[0:10] # Last date in file as string (yyyy-mm-dd) | |
| # Use the same parameters as was used for training the model. | |
| params = { | |
| "latitude": model_dict['API Request Params']['latitude'], | |
| "longitude": model_dict['API Request Params']['longitude'], | |
| "hourly": model_dict['API Request Params']['hourly'], | |
| "start_date": sdate_str, | |
| "end_date": edate_str, | |
| } | |
| responses = openmeteo.weather_api(url, params=params) | |
| # Process & print request info. | |
| response = responses[0] | |
| print("Request info:") | |
| print(f"Coordinates {response.Latitude()}°E {response.Longitude()}°N") | |
| print(f"Elevation {response.Elevation()} m asl") | |
| print(f"Timezone {response.Timezone()} {response.TimezoneAbbreviation()}") | |
| print(f"Timezone difference to GMT+0 {response.UtcOffsetSeconds()} s") | |
| #print(f"From: " + sdate_str + " To: " + edate_str) | |
| print(" ") | |
| # Process hourly data. The order of variables needs to be the same as requested. | |
| hourly = response.Hourly() # API Response | |
| hourly_data = {"date": pd.date_range( # Dictionary that we add the API respnse to | |
| start = pd.to_datetime(hourly.Time(), unit = "s"), | |
| end = pd.to_datetime(hourly.TimeEnd(), unit = "s"), | |
| freq = pd.Timedelta(seconds = hourly.Interval()), | |
| inclusive = "left" | |
| )} | |
| # We iterate through the variables and add the API response data to our dictionary | |
| print("Adding variables to dataframe...") | |
| index_variable = 0 | |
| for variable in params['hourly']: | |
| hourly_data[variable] = hourly.Variables(index_variable).ValuesAsNumpy() # Add the variable to dataframe | |
| print("Added " + variable) | |
| index_variable += 1 # Increment counter | |
| print(" ") | |
| # Create dataframe | |
| weather_data = pd.DataFrame(data = hourly_data) | |
| weather_data = weather_data.set_index('date') # Set index to be dates | |
| #%% 4. Data manipulation & encoding | |
| # Create a new dataframe to hold all data, also encode new features. | |
| # Create a main dataframe that holds all data | |
| main_df = weather_data.copy() | |
| for index, row in main_df.iterrows(): | |
| main_df.loc[index,'month'] = index.month | |
| main_df.loc[index,'day'] = index.day | |
| main_df.loc[index,'hour'] = index.hour | |
| main_df.loc[index,'sine month'] = np.sin((index.month - 1)*np.pi/11) | |
| main_df.loc[index,'cos month'] = np.cos((index.month - 1)*np.pi/11) | |
| main_df.loc[index,'sine hour'] = np.sin((index.month - 1)*np.pi/23) | |
| main_df.loc[index,'cos hour'] = np.cos((index.month - 1)*np.pi/23) | |
| # Index to keep track of which hours the sun is up for. | |
| Sun_index = main_df[main_df['is_day'] == 1].index | |
| # Create a new dataframe with only relevant data, this will be used for statistical | |
| sun_is_up_data = main_df.copy() # Create copy | |
| sun_is_up_data = sun_is_up_data.loc[Sun_index] # Only include rows when the sun is up | |
| sun_is_up_data = sun_is_up_data[model_dict['Input features']] # Only include columns needed for model | |
| #%% 5. Make Predictions | |
| print("Making predictions...") | |
| # Create dataframe to hold predictions and initialize with 0. | |
| predictions = pd.DataFrame(0, index = weather_data.index, columns = ['Main Forecast', 'Lower Bound', 'Upper Bound']) | |
| predictions['Date'] = predictions.index.tolist() # Make dates also to a column for easier plotting | |
| # Make predictions | |
| predictions.loc[Sun_index,'Main Forecast'] = model_dict['Random Forest Model'].predict(sun_is_up_data) | |
| predictions.loc[Sun_index,['Lower Bound', 'Upper Bound']] = model_dict['Random Forest Quantile Model'].predict(sun_is_up_data, quantiles=[0.1, 0.9], interpolation='linear') | |
| print(" ") | |
| #%% 6. Make Plot | |
| # Plotting | |
| plt.style.use('_mpl-gallery') # Set plot style | |
| matplotlib.rc('font', **{'size' : 20}) # Change font size | |
| fig, ax = plt.subplots() # Create plot | |
| fig.set_size_inches(18.5, 10.5, forward=True) # Set figure size | |
| fig.set_dpi(100) # Set resolution | |
| ax.fill_between(predictions['Date'], predictions['Lower Bound'], predictions['Upper Bound'], alpha=.5, linewidth=0) # Plot prediction interval | |
| ax.plot(predictions['Date'], predictions['Main Forecast'], linewidth=4, color='black') # Plot main forecast | |
| # Calculate x-ticks | |
| step_length = int(predictions.shape[0]/32) | |
| ax.set(ylim = [0,PV_capacity*1.05], xticks = predictions['Date'].iloc[::step_length]) | |
| ax.xaxis.set_major_formatter(mdates.DateFormatter('%m-%d %H:%M')) | |
| plt.ylabel('Solar power [kW]') | |
| plt.xticks(rotation=90) | |
| plt.legend(['Prediction Interval (10 - 90%)', 'Main Forecast']) | |
| plt.title('Solar Power Forecast from ' + sdate_str + ' to ' + edate_str) | |
| # plt.show() | |
| return fig | |
| iface = gr.Interface(fn=makeprediction, inputs=None, outputs=gr.Plot()) | |
| iface.launch() |