# Probability for different occupancy cluster types, Mostly Home, Early Regular Worker, Mostly Away, Regular Worker
# these probabilities are derived from ATUS using the k-modes algorithm
occupancy_types_probability: [0.381, 0.297, 0.165, 0.157]

###
# This is the baseline schedule for plugload, lighting and ceiling fan. It will be modified based on occupancy
plugload:
  weekday_sch: [0.035, 0.033, 0.032, 0.031, 0.032, 0.033, 0.037, 0.042, 0.043, 0.043, 0.043, 0.044, 0.045, 0.045, 0.044, 0.046, 0.048, 0.052, 0.053, 0.05, 0.047, 0.045, 0.04, 0.036]
  weekend_sch: [0.035, 0.033, 0.032, 0.031, 0.032, 0.033, 0.037, 0.042, 0.043, 0.043, 0.043, 0.044, 0.045, 0.045, 0.044, 0.046, 0.048, 0.052, 0.053, 0.05, 0.047, 0.045, 0.04, 0.036]
  monthly_multiplier: [1.248, 1.257, 0.993, 0.989, 0.993, 0.827, 0.821, 0.821, 0.827, 0.99, 0.987, 1.248]

lighting:
  # the exterior and garage lighting makes use of the weekday/weekend schedule. Indoor lighting schedule is generated
  # on the fly. Holiday lighting makes use of the holiday_sch
  weekday_sch: [0.04, 0.037, 0.037, 0.036, 0.033, 0.036, 0.043, 0.047, 0.034, 0.023, 0.024, 0.025, 0.024, 0.028, 0.031, 0.032, 0.039, 0.053, 0.063, 0.067, 0.071, 0.069, 0.059, 0.05]
  weekend_sch: [0.04, 0.037, 0.037, 0.036, 0.033, 0.036, 0.043, 0.047, 0.034, 0.023, 0.024, 0.025, 0.024, 0.028, 0.031, 0.032, 0.039, 0.053, 0.063, 0.067, 0.071, 0.069, 0.059, 0.05]
  monthly_multiplier: [1.248, 1.257, 0.993, 0.989, 0.993, 0.827, 0.821, 0.821, 0.827, 0.99, 0.987, 1.248]
  holiday_sch: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.008168, 0.098016, 0.168028, 0.193699, 0.283547, 0.192532, 0.03734, 0.01867]

ceiling_fan:
  weekday_sch: [0.04, 0.037, 0.037, 0.036, 0.033, 0.036, 0.043, 0.047, 0.034, 0.023, 0.024, 0.025, 0.024, 0.028, 0.031, 0.032, 0.039, 0.053, 0.063, 0.067, 0.071, 0.069, 0.059, 0.05]
  weekend_sch: [0.04, 0.037, 0.037, 0.036, 0.033, 0.036, 0.043, 0.047, 0.034, 0.023, 0.024, 0.025, 0.024, 0.028, 0.031, 0.032, 0.039, 0.053, 0.063, 0.067, 0.071, 0.069, 0.059, 0.05]
  monthly_multiplier: [1.248, 1.257, 0.993, 0.989, 0.993, 0.827, 0.821, 0.821, 0.827, 0.99, 0.987, 1.248]
###

# probabilities below for all water draw events are extracted from DHW event generators
# the onset, duration, events_per_cluster_probs, flow rate mean and std could all refer to the DHW event generator excel sheet ('event characteristics' and 'Start Times' sheet) 
# Water Draw Probability Distribution
sink:
  duration_probability: [0.901242, 0.076572, 0.01722, 0.003798, 0.000944, 0.000154, 4.6e-05, 2.2e-05, 2.0e-06]
  events_per_cluster_probs: [0.62458, 0.18693, 0.08011, 0.04330, 0.02178, 0.01504, 0.00830, 0.00467, 0.00570, 0.00285, 0.00181, 0.00233, 0.00130, 0.00104, 0.00026]
  hourly_onset_prob: [0.007, 0.018, 0.042, 0.062, 0.066, 0.062, 0.054, 0.050, 0.049, 0.045, 0.041, 0.043, 0.048, 0.065, 0.075, 0.069, 0.057, 0.048, 0.040, 0.027, 0.014, 0.007, 0.005, 0.005]
  total_annual_cluster: 6000
  between_event_gap: 2  # in minutes
  flow_rate_mean: 1.14
  flow_rate_std: 0.61

shower:
  between_event_gap: 30  # integer minutes (0.51 * 60)
  flow_rate_mean: 2.25
  flow_rate_std: 0.68

bath:
  bath_to_shower_ratio: 0.078843  # 2884.0 / 36579
  duration_mean: 5.65
  duration_std: 2.09
  flow_rate_mean: 4.4
  flow_rate_std: 1.17

dishwasher:
  flow_rate_mean: 1.39
  flow_rate_std: 0.2
  between_event_gap: 10  # in integer minutes (0.16 * 60)

clothes_washer:
  flow_rate_mean: 2.2
  flow_rate_std: 0.62
  between_event_gap: 4  # integer minutes (0.08 * 60).to_i
  load_size_probability: [0.682926829, 0.227642276, 0.056910569, 0.032520325]

# power draw distribution for dishwasher, clothes_washer and clothes_dryer and cooking is based on csv files