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Commit 2fe2fd8b authored by Avisek Naug's avatar Avisek Naug
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reverted back the changes

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source/hybridlearning.py
View file @ 2fe2fd8b
......@@ -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
View file @ 2fe2fd8b
......@@ -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
......
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