MODEL_PATH_ITP_AIR='/project/Pipline/models/AirInceptionTime.pt' MODEL_PATH_ITP_ENER='/project/Pipline/models/EnerInceptionTime.pt' MODEL_PATH_ITP_BOIL='/project/Pipline/models/BoilerInceptionTime.pt' DATA_PATH='/project/Pipline/data' AIR_PREPROCESSOR_PATH='/project/Pipline/data/preproc_pipe.pkl' AIR_SCALING_DATA='/project/Pipline/data/exp_pipe.pkl' ENER_PREPROCESSOR_PATH='/project/Pipline/data/Enerpreproc_pipe.pkl' ENER_SCALING_DATA='/project/Pipline/data/Ener_exp_pipe.pkl' AIR_DATETIME_COL='timestamp' #the current model is only for 1 device and of a specific appliance only AIR_COLOUMNS=['air_inlet_temp_deg_f', 'average_cfm', 'average_kw', 'compressor_room_air_generated_cfm', 'compressor_room_energy_cosumed_kwh', 'specific_power_kw_100cfm', 'percentage_loading_based_on_air_supplied_design_600cfm'] #the below columns provide no relavant information to the model AIR_DROP_COLOUMNS=['data_id','ideal_specific_power_kw_100cfm','device_id'] ENERGY_DATETIME='parameter_timestamp' ENERGY_COLOUMNS=['current_ir', 'electrical_energy', 'frequency', 'power', 'powerfactor', 'pressure', 'temperature', 'voltage_vb', 'voltage_vr', 'voltage_vy'] ENERGY_DROP_COLOUMNS=["location","current_ib","current_iy","device_1_state","device_2_state","device_id","device_name",'id',"device_type"] BOILER_PREPROCESSOR_PATH='/project/Pipline/data/boiler_preproc_pipe.pkl' BOILER_DATETIME='DateString' BOILER_COLOUMNS=['Boiler2_Feed Water Temp (T-4) (°F)','Boiler2_Gas Flow (G-2) (MMBtu)', 'Boiler2_Make Up Flow (W-17) (kGal)','Boiler2_Steam Flow (S-1) (lbs)'] #Frequency of the incoming data FREQUENCY='1H' METHOD='ffill' VALUE=0 #No. of values needed to be looked back FCST_HISTORY=200 #No. of Timestamps predicted in the future FCST_HORIZON=168 #1 Week VALID_SIZE=0.1 TEST_SIZE=0.2 #Training Parameters for a new Model ARCH_CONFIG=dict( n_layers=3, n_heads=4, d_model=16, d_ff=128, attn_dropout=0.0, dropout=0.3, patch_len=24, stride=2, padding_patch=True, )#Only needed when using PatchTst N_EPOCH=100