Commit 2fe2fd8b authored by Avisek Naug's avatar Avisek Naug
Browse files

reverted back the changes

parent e812e318

source/hybridlearning.py

+99 −99
Original line number Diff line number Diff line
@@ -3,53 +3,53 @@ from source.helperfunctions import * 
from source.dataGenerator import *

from source.predictionmodel import *



# initially used to process raw data

def customdf_ahu(ahupath, ahu, savepath, metasysdata, limit=0.1):

    # read the relevant data

    data_path = [ahupath+'/BdXdata/', ahupath+'/Solcastdata/', ahupath+'/valvedata/']

    date_column_name = ['Date', 'PeriodEnd', 'Time']

    date_format = ['%m/%d/%Y %H:%M', '%Y-%m-%dT%H:%M:%SZ', None]

    outlier_names = [['AirFlow', 'CC_T', 'OAT', 'PH_T.', 'SAT'], [], []]

    time_offsets = [0, 0, 0]

    # Create the data frame

    df = createdataframe(data_path, date_column_name, date_format, outlier_names, time_offsets,

                         metasysdata, limit=limit)

    # drop solcast air temp and dew point

    df.drop(columns=['AirTemp', 'DewpointTemp'], inplace=True)

    # rename columns: maintain order of variables correctly

    namedict = {'OAT': 'OAT',

                'AirTemp': 'OAT',

                'SAT': 'SAT',

                'SAT_STP': 'SAT_STP',

                'AirFlow': 'AirFlow',

                'PHT_STP': 'PHT_STP',

                'CCT_STP': 'CCT_STP',

                'PH_T.': 'PH_T',

                'CC_T': 'CC_T',

                'RelativeHumidity': 'RH',

                'AHU_2.preheatOutput': 'P_OP',

                'AHU_2.coolOutput': 'C_OP',

                'AHU_2.heatOutput': 'R_OP',

                'Preheat Output.Preheat Output.Trend - Present Value ()': 'P_OP',

                'Chilled Water Valve Ouptut.Chilled Water Valve Ouptut.Trend - Present Value ()': 'C_OP',

                'Reheat Output.Reheat Output.Trend - Present Value ()': 'R_OP',

                'DewpointTemp': 'DP'}

    # df.columns = ['OAT', 'SAT', 'SAT_STP', 'AirFlow', 'PHT_STP', 'CCT_STP',

    #               'PH_T', 'CC_T', 'RH', 'P_OP', 'C_OP', 'R_OP']

    df.columns = [namedict[i]+ahu for i in df.columns]



    # save the data frame

    dfsave(df, savepath)

# # initially used to process raw data

# def customdf_ahu(ahupath, ahu, savepath, metasysdata, limit=0.1):

#     # read the relevant data

#     data_path = [ahupath+'/BdXdata/', ahupath+'/Solcastdata/', ahupath+'/valvedata/']

#     date_column_name = ['Date', 'PeriodEnd', 'Time']

#     date_format = ['%m/%d/%Y %H:%M', '%Y-%m-%dT%H:%M:%SZ', None]

#     outlier_names = [['AirFlow', 'CC_T', 'OAT', 'PH_T.', 'SAT'], [], []]

#     time_offsets = [0, 0, 0]

#     # Create the data frame

#     df = createdataframe(data_path, date_column_name, date_format, outlier_names, time_offsets,

#                          metasysdata, limit=limit)

#     # drop solcast air temp and dew point

#     df.drop(columns=['AirTemp', 'DewpointTemp'], inplace=True)

#     # rename columns: maintain order of variables correctly

#     namedict = {'OAT': 'OAT',

#                 'AirTemp': 'OAT',

#                 'SAT': 'SAT',

#                 'SAT_STP': 'SAT_STP',

#                 'AirFlow': 'AirFlow',

#                 'PHT_STP': 'PHT_STP',

#                 'CCT_STP': 'CCT_STP',

#                 'PH_T.': 'PH_T',

#                 'CC_T': 'CC_T',

#                 'RelativeHumidity': 'RH',

#                 'AHU_2.preheatOutput': 'P_OP',

#                 'AHU_2.coolOutput': 'C_OP',

#                 'AHU_2.heatOutput': 'R_OP',

#                 'Preheat Output.Preheat Output.Trend - Present Value ()': 'P_OP',

#                 'Chilled Water Valve Ouptut.Chilled Water Valve Ouptut.Trend - Present Value ()': 'C_OP',

#                 'Reheat Output.Reheat Output.Trend - Present Value ()': 'R_OP',

#                 'DewpointTemp': 'DP'}

#     # df.columns = ['OAT', 'SAT', 'SAT_STP', 'AirFlow', 'PHT_STP', 'CCT_STP',

#     #               'PH_T', 'CC_T', 'RH', 'P_OP', 'C_OP', 'R_OP']

#     df.columns = [namedict[i]+ahu for i in df.columns]

#

#     # save the data frame

#     dfsave(df, savepath)



# params

ahu1 = 'ahu1'

ahu2 = 'ahu2'

metasysdataahu1 = [False, False, True]

metasysdataahu2 = [False, False, False]



# create custom dataframes

customdf_ahu('./data/ahu1', ahu1, 'hybrid_data_ahu1.pkl', metasysdataahu1, limit=0.1)

customdf_ahu('./data/ahu2', ahu2, 'hybrid_data_ahu2.pkl', metasysdataahu2, limit=0.5)

# metasysdataahu1 = [False, False, True]

# metasysdataahu2 = [False, False, False]

#

# # create custom dataframes

# customdf_ahu('./data/ahu1', ahu1, 'hybrid_data_ahu1.pkl', metasysdataahu1, limit=0.1)

# customdf_ahu('./data/ahu2', ahu2, 'hybrid_data_ahu2.pkl', metasysdataahu2, limit=0.5)



# read the dataframe from stored pickled data

df1 = read_pickle('../hybrid_data_ahu1.pkl')
@@ -69,62 +69,62 @@ X_train, X_test, y_train, y_test = precooldata(df1, ahu1) 
model = GBR_model(modeltype='PreCool Temp', period=1, savepath='../ResultsAHU1')

model.trainmodel(X_train, X_test, y_train, y_test, savemodel=True)



# learn function for estimating recovery heat air temperature for AHU 2

X_train, X_test, y_train, y_test = recovheatdata(df2, ahu2)

model = GBR_model(modeltype='Recovery Heat Temp', period=1, savepath='ResultsAHU2')

model.trainmodel(X_train, X_test, y_train, y_test, savemodel=True)



# learn function for estimating pre cool air temperature for AHU 2

X_train, X_test, y_train, y_test = precooldata(df2, ahu2)

model = GBR_model(modeltype='PreCool Temp', period=1, savepath='ResultsAHU2')

model.trainmodel(X_train, X_test, y_train, y_test, savemodel=True)



# learn the necessary driven models from the provided data

def customdf_condensor(savepath='condensordata.pkl', metasysdata=[False], limit=0.1):

    # read the relevant data

    data_path = ['./data/condensor/']

    date_column_name = ['Date']

    date_format = ['%m/%d/%Y %H:%M']

    outlier_names = [['Alumni_Hall_Cond_Loop_S_T.value',

                     'Alumni_Hall_Cond_Loop_R_T.value']]

    time_offsets = [0]

    # Create the data frame

    df = createdataframe(data_path, date_column_name, date_format, outlier_names, time_offsets,

                         metasysdata, limit=limit)

    # drop solcast air temp and dew point

    df.drop(columns=['Condensor_Water_Pump.pumpVfdPercent',

                     'Secondary_Chilled_Water_Pump.pumpVfdPercent'], inplace=True)

    # rename columns: maintain order of variables correctly

    namedict = {'OAT': 'OAT',

                'AirTemp': 'OAT',

                'SAT': 'SAT',

                'SAT_STP': 'SAT_STP',

                'AirFlow': 'AirFlow',

                'PHT_STP': 'PHT_STP',

                'CCT_STP': 'CCT_STP',

                'PH_T.': 'PH_T',

                'CC_T': 'CC_T',

                'RelativeHumidity': 'RH',

                'AHU_2.preheatOutput': 'P_OP',

                'AHU_2.coolOutput': 'C_OP',

                'AHU_2.heatOutput': 'R_OP',

                'Preheat Output.Preheat Output.Trend - Present Value ()': 'P_OP',

                'Chilled Water Valve Ouptut.Chilled Water Valve Ouptut.Trend - Present Value ()': 'C_OP',

                'Reheat Output.Reheat Output.Trend - Present Value ()': 'R_OP',

                'DewpointTemp': 'DP',

                'Alumni_Hall_Cond_Loop_R_T.value': 'CondRT',

                'Alumni_Hall_Cond_Loop_S_T.value': 'CondST',

                'Alumni_Hall_SCHW1_DP.value': 'Ahu1DP',

                'Alumni_Hall_SCHW2_DP.value': 'Ahu2DP',

                'Alumni_Hall_CU_DP.value': 'CuDP',

                'Alumni_Hall_PCHW_Flow.value': 'PchwFlow'}

    df.columns = [namedict[i] for i in df.columns]



    # save the data frame

    dfsave(df, savepath)

# create custom dataframes

customdf_condensor()

df3 = read_pickle('condensordata.pkl')

# # learn function for estimating recovery heat air temperature for AHU 2

# X_train, X_test, y_train, y_test = recovheatdata(df2, ahu2)

# model = GBR_model(modeltype='Recovery Heat Temp', period=1, savepath='ResultsAHU2')

# model.trainmodel(X_train, X_test, y_train, y_test, savemodel=True)

#

# # learn function for estimating pre cool air temperature for AHU 2

# X_train, X_test, y_train, y_test = precooldata(df2, ahu2)

# model = GBR_model(modeltype='PreCool Temp', period=1, savepath='ResultsAHU2')

# model.trainmodel(X_train, X_test, y_train, y_test, savemodel=True)

#

# # learn the necessary driven models from the provided data

# def customdf_condensor(savepath='condensordata.pkl', metasysdata=[False], limit=0.1):

#     # read the relevant data

#     data_path = ['./data/condensor/']

#     date_column_name = ['Date']

#     date_format = ['%m/%d/%Y %H:%M']

#     outlier_names = [['Alumni_Hall_Cond_Loop_S_T.value',

#                      'Alumni_Hall_Cond_Loop_R_T.value']]

#     time_offsets = [0]

#     # Create the data frame

#     df = createdataframe(data_path, date_column_name, date_format, outlier_names, time_offsets,

#                          metasysdata, limit=limit)

#     # drop solcast air temp and dew point

#     df.drop(columns=['Condensor_Water_Pump.pumpVfdPercent',

#                      'Secondary_Chilled_Water_Pump.pumpVfdPercent'], inplace=True)

#     # rename columns: maintain order of variables correctly

#     namedict = {'OAT': 'OAT',

#                 'AirTemp': 'OAT',

#                 'SAT': 'SAT',

#                 'SAT_STP': 'SAT_STP',

#                 'AirFlow': 'AirFlow',

#                 'PHT_STP': 'PHT_STP',

#                 'CCT_STP': 'CCT_STP',

#                 'PH_T.': 'PH_T',

#                 'CC_T': 'CC_T',

#                 'RelativeHumidity': 'RH',

#                 'AHU_2.preheatOutput': 'P_OP',

#                 'AHU_2.coolOutput': 'C_OP',

#                 'AHU_2.heatOutput': 'R_OP',

#                 'Preheat Output.Preheat Output.Trend - Present Value ()': 'P_OP',

#                 'Chilled Water Valve Ouptut.Chilled Water Valve Ouptut.Trend - Present Value ()': 'C_OP',

#                 'Reheat Output.Reheat Output.Trend - Present Value ()': 'R_OP',

#                 'DewpointTemp': 'DP',

#                 'Alumni_Hall_Cond_Loop_R_T.value': 'CondRT',

#                 'Alumni_Hall_Cond_Loop_S_T.value': 'CondST',

#                 'Alumni_Hall_SCHW1_DP.value': 'Ahu1DP',

#                 'Alumni_Hall_SCHW2_DP.value': 'Ahu2DP',

#                 'Alumni_Hall_CU_DP.value': 'CuDP',

#                 'Alumni_Hall_PCHW_Flow.value': 'PchwFlow'}

#     df.columns = [namedict[i] for i in df.columns]

#

#     # save the data frame

#     dfsave(df, savepath)

# # create custom dataframes

# customdf_condensor()

# df3 = read_pickle('condensordata.pkl')





# setup environment to learn the appropriate control method

source/predictionmodel.py

+2 −2
Original line number Diff line number Diff line
@@ -78,8 +78,8 @@ class GBR_model(): 
        plt.rc('axes', labelsize=8)



        # width as measured in inkscape

        width = 25

        height = 5  # width / 1.618

        width = 10.487

        height = width / 1.618

        plt.rcParams["figure.figsize"] = (width, height)



        # Plotting the prediction versus target curve:train