api de python con ejemplo de código de precios en tiempo real de ercot en vivo

Hola, tenemos la respuesta a lo que buscas, continúa leyendo y la hallarás a continuación.

Ejemplo 1: api de python con precios en tiempo real de ercot en vivo

#Transform the columns using natural log
master_df['Electricity_Price_Transformed']=np.log(master_df['Electricity_Price'])
master_df['Nat_Gas_Price_MCF_Transformed']=np.log(master_df['Nat_Gas_Price_MCF'])#Difference the data by 1 month
n=1
master_df['Electricity_Price_Transformed_Differenced']= master_df['Electricity_Price_Transformed']- master_df['Electricity_Price_Transformed'].shift(n)
master_df['Nat_Gas_Price_MCF_Transformed_Differenced']= master_df['Nat_Gas_Price_MCF_Transformed']- master_df['Nat_Gas_Price_MCF_Transformed'].shift(n)

Ejemplo 2: api de python con precios en tiempo real de ercot en vivo

#Conver the dataframe to a numpy array
master_array=np.array(master_df[['Electricity_Price_Transformed_Differenced','Nat_Gas_Price_MCF_Transformed_Differenced']].dropna())#Generate a training and test set for building the model: 95/5 split
training_set = master_array[:int(0.95*(len(master_array)))]
test_set = master_array[int(0.95*(len(master_array))):]#Fit to a VAR model
model = VAR(endog=training_set)
model_fit = model.fit()#Print a summary of the model results
model_fit.summary()

Ejemplo 3: api de python con precios en tiempo real de ercot en vivo

defcalculate_model_accuracy_metrics(actual, predicted):"""
    Output model accuracy metrics, comparing predicted values
    to actual values.
    Arguments:
        actual: list. Time series of actual values.
        predicted: list. Time series of predicted values
    Outputs:
        Forecast bias metrics, mean absolute error, mean squared error,
        and root mean squared error in the console
    """#Calculate forecast bias
    forecast_errors =[actual[i]-predicted[i]for i inrange(len(actual))]
    bias =sum(forecast_errors)*1.0/len(actual)print('Bias: %f'% bias)#Calculate mean absolute error
    mae = mean_absolute_error(actual, predicted)print('MAE: %f'% mae)#Calculate mean squared error and root mean squared error
    mse = mean_squared_error(actual, predicted)print('MSE: %f'% mse)
    rmse = sqrt(mse)print('RMSE: %f'% rmse)#Execute in the main block#Un-difference the datafor i inrange(1,len(master_df.index)-1):
    master_df.at[i,'Electricity_Price_Transformed']= master_df.at[i-1,'Electricity_Price_Transformed']+master_df.at[i,'Electricity_Price_Transformed_Differenced_PostProcess']#Back-transform the data
master_df.loc[:,'Predicted_Electricity_Price']=np.exp(master_df['Electricity_Price_Transformed'])#Compare the forecasted data to the real dataprint(master_df[master_df['Predicted']==1][['Date','Electricity_Price','Predicted_Electricity_Price']])#Evaluate the accuracy of the results
calculate_model_accuracy_metrics(list(master_df[master_df['Predicted']==1]['Electricity_Price']),list(master_df[master_df['Predicted']==1['Predicted_Electricity_Price']))

Ejemplo 4: api de python con precios en tiempo real de ercot en vivo

defretrieve_time_series(api, series_ID):"""
    Return the time series dataframe, based on API and unique Series ID
    api: API that we're connected to
    series_ID: string. Name of the series that we want to pull from the EIA API
    """#Retrieve Data By Series ID 
    series_search = api.data_by_series(series=series_ID)##Create a pandas dataframe from the retrieved time series
    df = pd.DataFrame(series_search)return df
    
###Execute in the main block#Create EIA API using your specific API key
api_key ="YOR API KEY HERE"
api = eia.API(api_key)#Pull the electricity price data
series_ID='ELEC.PRICE.TX-ALL.M'
electricity_df=retrieve_time_series(api, series_ID)
electricity_df.reset_index(level=0, inplace=True)#Rename the columns for easer analysis
electricity_df.rename(columns='index':'Date',
            electricity_df.columns[1]:'Electricity_Price', 
            inplace=True)

Ejemplo 5: api de python con precios en tiempo real de ercot en vivo

defdecompose_time_series(series):"""
    Decompose a time series and plot it in the console
    Arguments: 
        series: series. Time series that we want to decompose
    Outputs: 
        Decomposition plot in the console
    """
    result = seasonal_decompose(series, model='additive')
    result.plot()
    pyplot.show()#Execute in the main block#Convert the Date column into a date object
electricity_df['Date']=pd.to_datetime(electricity_df['Date'])#Set Date as a Pandas DatetimeIndex
electricity_df.index=pd.DatetimeIndex(electricity_df['Date'])#Decompose the time series into parts
decompose_time_series(electricity_df['Electricity_Price'])

Ejemplo 6: api de python con precios en tiempo real de ercot en vivo

#Pull in natural gas time series data
series_ID='NG.N3035TX3.M'
nat_gas_df=retrieve_time_series(api, series_ID)
nat_gas_df.reset_index(level=0, inplace=True)#Rename the columns
nat_gas_df.rename(columns='index':'Date',
            nat_gas_df.columns[1]:'Nat_Gas_Price_MCF', 
            inplace=True)#Convert the Date column into a date object
nat_gas_df['Date']=pd.to_datetime(nat_gas_df['Date'])#Set Date as a Pandas DatetimeIndex
nat_gas_df.index=pd.DatetimeIndex(nat_gas_df['Date'])#Decompose the time series into parts
decompose_time_series(nat_gas_df['Nat_Gas_Price_MCF'])#Merge the two time series together based on Date Index
master_df=pd.merge(electricity_df['Electricity_Price'], nat_gas_df['Nat_Gas_Price_MCF'], 
                       left_index=True, right_index=True)
master_df.reset_index(level=0, inplace=True)#Plot the two variables in the same plot
plt.plot(master_df['Date'], 
             master_df['Electricity_Price'], label="Electricity_Price")
plt.plot(master_df['Date'], 
             master_df['Nat_Gas_Price_MCF'], label="Nat_Gas_Price")# Place a legend to the right of this smaller subplot.
plt.legend(bbox_to_anchor=(1.05,1), loc=2, borderaxespad=0.)
plt.title('Natural Gas Price vs. TX Electricity Price over Time')
plt.show()

---

---

Utiliza Nuestro Buscador