List of implemented models#

Thermal networks#

class pysip.statespace.thermal_network.R2C2Qgh(parameters=None, hold_order=0, method='mfd', name='', latent_forces='')[source]#

Bases: RCModel

None

Model Variables#

  • Inputs

    • To: outdoor air temperature (°C)

    • Qgh: global horizontal solar radiation (W)

    • Qh: HVAC system heat (W)

  • Outputs

    • xi: indoor air temperature (°C)

  • States

    • xw: wall temperature (°C)

    • xi: indoor space temperature (°C)

Model Parameters#

  • Thermal resistance

    • Ro: between the outdoor and the wall node (°C/W)

    • Ri: between the wall node and the indoor (°C/W)

  • Thermal capacity

    • Cw: Wall (J/°C)

    • Ci: indoor air, indoor walls, furnitures, etc. (J/°C)

  • State deviation

    • sigw_w: (any)

    • sigw_i: (any)

  • Measure deviation

    • sigv: (any)

  • Initial mean

    • x0_w: (any)

    • x0_i: (any)

  • Initial deviation

    • sigx0_w: (any)

    • sigx0_i: (any)

class pysip.statespace.thermal_network.TiTh_RwRhAi(parameters=None, hold_order=0, method='mfd', name='', latent_forces='')[source]#

Bases: RCModel

Second order RC model

Model Variables#

  • Inputs

    • To: outdoor temperature (°C)

    • Qgh: solar irradiance (W)

    • Qh: HVAC system heat (W)

  • Outputs

    • xi: indoor temperature (°C)

  • States

    • xi: indoor temperature (°C)

    • xh: heater temperature (°C)

Model Parameters#

  • Thermal resistance

    • Rw: between the outdoor and the indoor (°C/W)

    • Rh: between the indoor and the heater (°C/W)

  • Thermal capacity

    • Ci: of the indoor (J/°C)

    • Ch: of the heater (J/°C)

  • Solar aperture

    • Ai: effective solar aperture (m²)

  • State deviation

    • sigw_i: of the indoor dynamic (any)

    • sigw_h: of the heater dynamic (any)

  • Measure deviation

    • sigv: of the indoor temperature measurements (any)

  • Initial mean

    • x0_i: of the infoor temperature (any)

    • x0_h: of the heater temperature (any)

  • Initial deviation

    • sigx0_i: of the infoor temperature (any)

    • sigx0_h: of the heater temperature (any)

class pysip.statespace.thermal_network.TiTm_RwRmAi(parameters=None, hold_order=0, method='mfd', name='', latent_forces='')[source]#

Bases: RCModel

Second order RC model

Model Variables#

  • Inputs

    • To: outdoor temperature (°C)

    • Qgh: solar irradiance (W)

    • Qh: HVAC system heat (W)

  • Outputs

    • xi: indoor temperature (°C)

  • States

    • xi: indoor temperature (°C)

    • xm: internal mass temperature (°C)

Model Parameters#

  • Thermal resistance

    • Rw: between the outdoor and the indoor (°C/W)

    • Rm: between the indoor and the internal mass (°C/W)

  • Thermal capacity

    • Ci: of the indoor (J/°C)

    • Cm: of the internal mass (J/°C)

  • Solar aperture

    • Ai: effective solar aperture (m²)

  • State deviation

    • sigw_i: of the indoor dynamic (any)

    • sigw_m: of the internal mass dynamic (any)

  • Measure deviation

    • sigv: of the indoor temperature measurements (any)

  • Initial mean

    • x0_i: of the infoor temperature (any)

    • x0_m: of the internal mass temperature (any)

  • Initial deviation

    • sigx0_i: of the infoor temperature (any)

    • sigx0_m: of the internal mass temperature (any)

class pysip.statespace.thermal_network.Ti_RA(parameters=None, hold_order=0, method='mfd', name='', latent_forces='')[source]#

Bases: RCModel

None

Model Variables#

  • Inputs

    • To: outdoor temperature (°C)

    • Qgh: solar irradiance (W)

    • Qh: HVAC system heat (W)

  • Outputs

    • xi: indoor temperature (°C)

  • States

    • xi: indoor temperature (°C)

Model Parameters#

  • Thermal resistance

    • R: between the outdoor and the indoor (°C/W)

  • Thermal capacity

    • C: effective overall capacity (J/°C)

  • Solar aperture

    • A: effective solar aperture (m²)

  • State deviation

    • sigw: of the indoor dynamic (any)

  • Measure deviation

    • sigv: of the indoor temperature measurements (any)

  • Initial mean

    • x0: of the infoor temperature (any)

  • Initial deviation

    • sigx0: of the infoor temperature (any)

class pysip.statespace.thermal_network.Ti_RAcv(parameters=None, hold_order=0, method='mfd', name='', latent_forces='')[source]#

Bases: RCModel

None

Model Variables#

  • Inputs

    • To: outdoor temperature (°C)

    • Qgh: solar irradiance (W)

    • Qh: HVAC system heat (W)

    • Qv: heat from the ventilation system (W)

  • Outputs

    • xi: indoor temperature (°C)

  • States

    • xi: indoor temperature (°C)

Model Parameters#

  • Thermal resistance

    • R: between the outdoor and the indoor (°C/W)

  • Thermal capacity

    • C: effective overall capacity (J/°C)

  • Solar aperture

    • A: effective solar aperture (m²)

  • Coefficient

    • cv: scaling of the heat from the ventilation (any)

  • State deviation

    • sigw: of the indoor dynamic (any)

  • Measure deviation

    • sigv: of the indoor temperature measurements (any)

  • Initial mean

    • x0: of the infoor temperature (any)

  • Initial deviation

    • sigx0: of the infoor temperature (any)

class pysip.statespace.thermal_network.TwTiTb_RoRiRbAwAicv(parameters=None, hold_order=0, method='mfd', name='', latent_forces='')[source]#

Bases: RCModel

Third order RC model

Model Variables#

  • Inputs

    • To: outdoor temperature (°C)

    • Tb: boundary temperature (°C)

    • Qgh: solar irradiance (W)

    • Qh: HVAC system heat (W)

    • Qv: heat from the ventilation system (W)

  • Outputs

    • xi: indoor temperature (°C)

  • States

    • xw: envelope temperature (°C)

    • xi: indoor temperature (°C)

    • xb: boundary wall temperature (°C)

Model Parameters#

  • Thermal resistance

    • Ro: between the outdoor and the envelope (°C/W)

    • Ri: between the envelope and the indoor (°C/W)

    • Rb: between the indoor and the boundary (°C/W)

  • Thermal capacity

    • Cw: of the envelope (J/°C)

    • Ci: of the indoor (J/°C)

    • Cb: of the wall between the indoor and the boundary (J/°C)

  • Solar aperture

    • Aw: of the envelope (m²)

    • Ai: of the windows (m²)

  • Coefficient

    • cv: scaling of the heat from the ventilation (any)

  • State deviation

    • sigw_w: of the envelope dynamic (any)

    • sigw_i: of the indoor dynamic (any)

    • sigw_b: of the boundary wall dynamic (any)

  • Measure deviation

    • sigv: of the indoor temperature measurements (any)

  • Initial mean

    • x0_w: of the envelope temperature (any)

    • x0_i: of the infoor temperature (any)

    • x0_b: of the boundary wall temperature (any)

  • Initial deviation

    • sigx0_w: of the envelope temperature (any)

    • sigx0_i: of the infoor temperature (any)

    • sigx0_b: of the boundary wall temperature (any)

class pysip.statespace.thermal_network.TwTiTb_RoRiRibRbbAwAicv(parameters=None, hold_order=0, method='mfd', name='', latent_forces='')[source]#

Bases: RCModel

Third order RC model

Model Variables#

  • Inputs

    • To: outdoor temperature (°C)

    • Tb: boundary temperature (°C)

    • Qgh: solar irradiance (W)

    • Qh: HVAC system heat (W)

    • Qv: heat from the ventilation system (W)

  • Outputs

    • xi: indoor temperature (°C)

  • States

    • xw: envelope temperature (°C)

    • xi: indoor temperature (°C)

    • xb: boundary wall temperature (°C)

Model Parameters#

  • Thermal resistance

    • Ro: between the outdoor and the envelope (°C/W)

    • Ri: between the envelope and the indoor (°C/W)

    • Rib: between the indoor and the boundary wall (°C/W)

    • Rbb: between the boundary wall and the boundary space (°C/W)

  • Thermal capacity

    • Cw: of the envelope (J/°C)

    • Ci: of the indoor (J/°C)

    • Cb: of the wall between the indoor and the boundary (J/°C)

  • Solar aperture

    • Aw: of the envelope (m²)

    • Ai: of the windows (m²)

  • Coefficient

    • cv: scaling of the heat from the ventilation (any)

  • State deviation

    • sigw_w: of the envelope dynamic (any)

    • sigw_i: of the indoor dynamic (any)

    • sigw_b: of the boundary wall dynamic (any)

  • Measure deviation

    • sigv: of the indoor temperature measurements (any)

  • Initial mean

    • x0_w: of the envelope temperature (any)

    • x0_i: of the infoor temperature (any)

    • x0_b: of the boundary wall temperature (any)

  • Initial deviation

    • sigx0_w: of the envelope temperature (any)

    • sigx0_i: of the infoor temperature (any)

    • sigx0_b: of the boundary wall temperature (any)

class pysip.statespace.thermal_network.TwTiTh_RoRiRhAwAi(parameters=None, hold_order=0, method='mfd', name='', latent_forces='')[source]#

Bases: RCModel

Third order RC model

Model Variables#

  • Inputs

    • To: outdoor temperature (°C)

    • Qgh: solar irradiance (W)

    • Qh: HVAC system heat (W)

  • Outputs

    • xi: indoor temperature (°C)

  • States

    • xw: envelope temperature (°C)

    • xi: indoor temperature (°C)

    • xh: heaters temperature (°C)

Model Parameters#

  • Thermal resistance

    • Ro: between the outdoor and the envelope (°C/W)

    • Ri: between the envelope and the indoor (°C/W)

    • Rh: between the heaters and the indoor (°C/W)

  • Thermal capacity

    • Cw: of the envelope (J/°C)

    • Ci: of the indoor (J/°C)

    • Ch: of the heaters (J/°C)

  • Solar aperture

    • Aw: of the envelope (m²)

    • Ai: of the windows (m²)

  • State deviation

    • sigw_w: of the envelope dynamic (any)

    • sigw_i: of the indoor dynamic (any)

    • sigw_h: of the heaters dynamic (any)

  • Measure deviation

    • sigv: of the indoor temperature measurements (any)

  • Initial mean

    • x0_w: of the envelope temperature (any)

    • x0_i: of the infoor temperature (any)

    • x0_h: of the heater temperature (any)

  • Initial deviation

    • sigx0_w: of the envelope temperature (any)

    • sigx0_i: of the infoor temperature (any)

    • sigx0_h: of the heater temperature (any)

class pysip.statespace.thermal_network.TwTiTh_RoRiRhAwAicv(parameters=None, hold_order=0, method='mfd', name='', latent_forces='')[source]#

Bases: RCModel

Third order RC model

Model Variables#

  • Inputs

    • To: outdoor temperature (°C)

    • Qgh: solar irradiance (W)

    • Qh: HVAC system heat (W)

    • Qv: heat from the ventilation system (W)

  • Outputs

    • xi: indoor temperature (°C)

  • States

    • xw: envelope temperature (°C)

    • xi: indoor temperature (°C)

    • xh: heaters temperature (°C)

Model Parameters#

  • Thermal resistance

    • Ro: between the outdoor and the envelope (°C/W)

    • Ri: between the envelope and the indoor (°C/W)

    • Rh: between the heaters and the indoor (°C/W)

  • Thermal capacity

    • Cw: of the envelope (J/°C)

    • Ci: of the indoor (J/°C)

    • Ch: of the heaters (J/°C)

  • Solar aperture

    • Aw: of the envelope (m²)

    • Ai: of the windows (m²)

  • Coefficient

    • cv: scaling of the heat from the ventilation (any)

  • State deviation

    • sigw_w: of the envelope dynamic (any)

    • sigw_i: of the indoor dynamic (any)

    • sigw_h: of the heaters dynamic (any)

  • Measure deviation

    • sigv: of the indoor temperature measurements (any)

  • Initial mean

    • x0_w: of the envelope temperature (any)

    • x0_i: of the infoor temperature (any)

    • x0_h: of the heater temperature (any)

  • Initial deviation

    • sigx0_w: of the envelope temperature (any)

    • sigx0_i: of the infoor temperature (any)

    • sigx0_h: of the heater temperature (any)

class pysip.statespace.thermal_network.TwTiTh_RoRiRhAwAicv_bis(parameters=None, hold_order=0, method='mfd', name='', latent_forces='')[source]#

Bases: RCModel

Third order RC model

Model Variables#

  • Inputs

    • To: outdoor temperature (°C)

    • Qgh: solar irradiance (W)

    • Qh: HVAC system heat (W)

    • Qv: heat from the ventilation system (W)

    • Ql: internal heat gain (W)

  • Outputs

    • xi: indoor temperature (°C)

  • States

    • xw: envelope temperature (°C)

    • xi: indoor temperature (°C)

    • xh: heaters temperature (°C)

Model Parameters#

  • Thermal resistance

    • Ro: between the outdoor and the envelope (°C/W)

    • Ri: between the envelope and the indoor (°C/W)

    • Rh: between the heaters and the indoor (°C/W)

  • Thermal capacity

    • Cw: of the envelope (J/°C)

    • Ci: of the indoor (J/°C)

    • Ch: of the heaters (J/°C)

  • Solar aperture

    • Aw: of the envelope (m²)

    • Ai: of the windows (m²)

  • Coefficient

    • cv: scaling of the heat from the ventilation (any)

  • State deviation

    • sigw_w: of the envelope dynamic (any)

    • sigw_i: of the indoor dynamic (any)

    • sigw_h: of the heaters dynamic (any)

  • Measure deviation

    • sigv: of the indoor temperature measurements (any)

  • Initial mean

    • x0_w: of the envelope temperature (any)

    • x0_i: of the infoor temperature (any)

    • x0_h: of the heater temperature (any)

  • Initial deviation

    • sigx0_w: of the envelope temperature (any)

    • sigx0_i: of the infoor temperature (any)

    • sigx0_h: of the heater temperature (any)

class pysip.statespace.thermal_network.TwTiTm_RoRiAwAi(parameters=None, hold_order=0, method='mfd', name='', latent_forces='')[source]#

Bases: RCModel

Third order RC model

Model Variables#

  • Inputs

    • To: outdoor temperature (°C)

    • Qgh: solar irradiance (W)

    • Qh: HVAC system heat (W)

  • Outputs

    • xi: indoor temperature (°C)

  • States

    • xw: envelope temperature (°C)

    • xi: indoor temperature (°C)

    • xm: internal mass temperature (°C)

Model Parameters#

  • Thermal resistance

    • Ro: between the outdoor and the envelope (°C/W)

    • Ri: between the envelope and the indoor (°C/W)

  • Thermal capacity

    • Cw: of the envelope (J/°C)

    • Ci: of the indoor (J/°C)

    • Cm: of the internal mass (J/°C)

  • Solar aperture

    • Aw: of the envelope (m²)

    • Ai: of the windows (m²)

  • State deviation

    • sigw_w: of the envelope dynamic (any)

    • sigw_i: of the indoor dynamic (any)

    • sigw_m: of the internal mass dynamic (any)

  • Measure deviation

    • sigv: of the indoor temperature measurements (any)

  • Initial mean

    • x0_w: of the envelope temperature (any)

    • x0_i: of the infoor temperature (any)

    • x0_m: of the internal mass temperature (any)

  • Initial deviation

    • sigx0_w: of the envelope temperature (any)

    • sigx0_i: of the infoor temperature (any)

    • sigx0_m: of the internal mass temperature (any)

class pysip.statespace.thermal_network.TwTiTm_RoRiAwAicv(parameters=None, hold_order=0, method='mfd', name='', latent_forces='')[source]#

Bases: RCModel

Third order RC model

Model Variables#

  • Inputs

    • To: outdoor temperature (°C)

    • Qgh: solar irradiance (W)

    • Qh: HVAC system heat (W)

    • Qv: heat from the ventilation system (W)

  • Outputs

    • xi: indoor temperature (°C)

  • States

    • xw: envelope temperature (°C)

    • xi: indoor temperature (°C)

    • xm: internal mass temperature (°C)

Model Parameters#

  • Thermal resistance

    • Ro: between the outdoor and the envelope (°C/W)

    • Ri: between the envelope and the indoor (°C/W)

  • Thermal capacity

    • Cw: of the envelope (J/°C)

    • Ci: of the indoor (J/°C)

    • Cm: of the internal mass (J/°C)

  • Solar aperture

    • Aw: of the envelope (m²)

    • Ai: of the windows (m²)

  • Coefficient

    • cv: scaling of the heat from the ventilation (any)

  • State deviation

    • sigw_w: of the envelope dynamic (any)

    • sigw_i: of the indoor dynamic (any)

    • sigw_m: of the internal mass dynamic (any)

  • Measure deviation

    • sigv: of the indoor temperature measurements (any)

  • Initial mean

    • x0_w: of the envelope temperature (any)

    • x0_i: of the infoor temperature (any)

    • x0_m: of the internal mass temperature (any)

  • Initial deviation

    • sigx0_w: of the envelope temperature (any)

    • sigx0_i: of the infoor temperature (any)

    • sigx0_m: of the internal mass temperature (any)

class pysip.statespace.thermal_network.TwTiTm_RoRiRbAwAicv(parameters=None, hold_order=0, method='mfd', name='', latent_forces='')[source]#

Bases: RCModel

Third order RC model

Model Variables#

  • Inputs

    • To: outdoor temperature (°C)

    • Tb: boundary temperature (°C)

    • Qgh: solar irradiance (W)

    • Qh: HVAC system heat (W)

    • Qv: heat from the ventilation system (W)

  • Outputs

    • xi: indoor temperature (°C)

  • States

    • xw: envelope temperature (°C)

    • xi: indoor temperature (°C)

    • xm: internal mass temperature (°C)

Model Parameters#

  • Thermal resistance

    • Ro: between the outdoor and the envelope (°C/W)

    • Ri: between the envelope and the indoor (°C/W)

    • Rb: between the indoor and the adjacent space (°C/W)

  • Thermal capacity

    • Cw: of the envelope (J/°C)

    • Ci: of the indoor (J/°C)

    • Cm: of the internal mass (J/°C)

  • Solar aperture

    • Aw: of the envelope (m²)

    • Ai: of the windows (m²)

  • Coefficient

    • cv: scaling of the heat from the ventilation (any)

  • State deviation

    • sigw_w: of the envelope dynamic (any)

    • sigw_i: of the indoor dynamic (any)

    • sigw_m: of the internal mass dynamic (any)

  • Measure deviation

    • sigv: of the indoor temperature measurements (any)

  • Initial mean

    • x0_w: of the envelope temperature (any)

    • x0_i: of the infoor temperature (any)

    • x0_m: of the internal mass temperature (any)

  • Initial deviation

    • sigx0_w: of the envelope temperature (any)

    • sigx0_i: of the infoor temperature (any)

    • sigx0_m: of the internal mass temperature (any)

class pysip.statespace.thermal_network.TwTiTm_RoRiRmAwAi(parameters=None, hold_order=0, method='mfd', name='', latent_forces='')[source]#

Bases: RCModel

Third order RC model

Model Variables#

  • Inputs

    • To: outdoor temperature (°C)

    • Qgh: solar irradiance (W)

    • Qh: HVAC system heat (W)

  • Outputs

    • xi: indoor temperature (°C)

  • States

    • xw: envelope temperature (°C)

    • xi: indoor temperature (°C)

    • xm: internal mass temperature (°C)

Model Parameters#

  • Thermal resistance

    • Ro: between the outdoor and the envelope (°C/W)

    • Ri: between the envelope and the indoor (°C/W)

    • Rm: between the indoor and the internal mass (°C/W)

  • Thermal capacity

    • Cw: of the envelope (J/°C)

    • Ci: of the indoor (J/°C)

    • Cm: of the internal mass (J/°C)

  • Solar aperture

    • Aw: of the envelope (m²)

    • Ai: of the windows (m²)

  • State deviation

    • sigw_w: of the envelope dynamic (any)

    • sigw_i: of the indoor dynamic (any)

    • sigw_m: of the internal mass dynamic (any)

  • Measure deviation

    • sigv: of the indoor temperature measurements (any)

  • Initial mean

    • x0_w: of the envelope temperature (any)

    • x0_i: of the infoor temperature (any)

    • x0_m: of the internal mass temperature (any)

  • Initial deviation

    • sigx0_w: of the envelope temperature (any)

    • sigx0_i: of the infoor temperature (any)

    • sigx0_m: of the internal mass temperature (any)

class pysip.statespace.thermal_network.TwTiTm_RoRiRmAwAiAm(parameters=None, hold_order=0, method='mfd', name='', latent_forces='')[source]#

Bases: RCModel

Third order RC model

Model Variables#

  • Inputs

    • To: outdoor temperature (°C)

    • Qgh: solar irradiance (W)

    • Qh: HVAC system heat (W)

  • Outputs

    • xi: indoor temperature (°C)

  • States

    • xw: envelope temperature (°C)

    • xi: indoor temperature (°C)

    • xm: internal mass temperature (°C)

Model Parameters#

  • Thermal resistance

    • Ro: between the outdoor and the envelope (°C/W)

    • Ri: between the envelope and the indoor (°C/W)

    • Rm: between the indoor and the internal mass (°C/W)

  • Thermal capacity

    • Cw: of the envelope (J/°C)

    • Ci: of the indoor (J/°C)

    • Cm: of the internal mass (J/°C)

  • Solar aperture

    • Aw: of the envelope (m²)

    • Ai: of the windows (m²)

    • Am: of the internal mass (m²)

  • State deviation

    • sigw_w: of the envelope dynamic (any)

    • sigw_i: of the indoor dynamic (any)

    • sigw_m: of the internal mass dynamic (any)

  • Measure deviation

    • sigv: of the indoor temperature measurements (any)

  • Initial mean

    • x0_w: of the envelope temperature (any)

    • x0_i: of the infoor temperature (any)

    • x0_m: of the internal mass temperature (any)

  • Initial deviation

    • sigx0_w: of the envelope temperature (any)

    • sigx0_i: of the infoor temperature (any)

    • sigx0_m: of the internal mass temperature (any)

class pysip.statespace.thermal_network.TwTiTm_RoRiRmAwAicv(parameters=None, hold_order=0, method='mfd', name='', latent_forces='')[source]#

Bases: RCModel

Third order RC model

Model Variables#

  • Inputs

    • To: outdoor temperature (°C)

    • Qgh: solar irradiance (W)

    • Qh: HVAC system heat (W)

    • Qv: heat from the ventilation system (W)

  • Outputs

    • xi: indoor temperature (°C)

  • States

    • xw: envelope temperature (°C)

    • xi: indoor temperature (°C)

    • xm: internal mass temperature (°C)

Model Parameters#

  • Thermal resistance

    • Ro: between the outdoor and the envelope (°C/W)

    • Ri: between the envelope and the indoor (°C/W)

    • Rm: between the indoor and the internal mass (°C/W)

  • Thermal capacity

    • Cw: of the envelope (J/°C)

    • Ci: of the indoor (J/°C)

    • Cm: of the internal mass (J/°C)

  • Solar aperture

    • Aw: of the envelope (m²)

    • Ai: of the windows (m²)

  • Coefficient

    • cv: scaling the heat from the ventilation (any)

  • State deviation

    • sigw_w: of the envelope dynamic (any)

    • sigw_i: of the indoor dynamic (any)

    • sigw_m: of the internal mass dynamic (any)

  • Measure deviation

    • sigv: of the indoor temperature measurements (any)

  • Initial mean

    • x0_w: of the envelope temperature (any)

    • x0_i: of the infoor temperature (any)

    • x0_m: of the internal mass temperature (any)

  • Initial deviation

    • sigx0_w: of the envelope temperature (any)

    • sigx0_i: of the infoor temperature (any)

    • sigx0_m: of the internal mass temperature (any)

class pysip.statespace.thermal_network.TwTiTm_RoRiRmRbAwAi(parameters=None, hold_order=0, method='mfd', name='', latent_forces='')[source]#

Bases: RCModel

Third order RC model

Model Variables#

  • Inputs

    • To: outdoor temperature (°C)

    • Tb: boundary temperature (°C)

    • Qgh: solar irradiance (W)

    • Qh: HVAC system heat (W)

  • Outputs

    • xi: indoor temperature (°C)

  • States

    • xw: envelope temperature (°C)

    • xi: indoor temperature (°C)

    • xm: internal mass temperature (°C)

Model Parameters#

  • Thermal resistance

    • Ro: between the outdoor and the envelope (°C/W)

    • Ri: between the envelope and the indoor (°C/W)

    • Rm: between the indoor and the internal mass (°C/W)

    • Rb: between the indoor and the adjacent space (°C/W)

  • Thermal capacity

    • Cw: of the envelope (J/°C)

    • Ci: of the indoor (J/°C)

    • Cm: of the internal mass (J/°C)

  • Solar aperture

    • Aw: of the envelope (m²)

    • Ai: of the windows (m²)

  • State deviation

    • sigw_w: of the envelope dynamic (any)

    • sigw_i: of the indoor dynamic (any)

    • sigw_m: of the internal mass dynamic (any)

  • Measure deviation

    • sigv: of the indoor temperature measurements (any)

  • Initial mean

    • x0_w: of the envelope temperature (any)

    • x0_i: of the infoor temperature (any)

    • x0_m: of the internal mass temperature (any)

  • Initial deviation

    • sigx0_w: of the envelope temperature (any)

    • sigx0_i: of the infoor temperature (any)

    • sigx0_m: of the internal mass temperature (any)

class pysip.statespace.thermal_network.TwTiTm_RoRiRmRbAwAicv(parameters=None, hold_order=0, method='mfd', name='', latent_forces='')[source]#

Bases: RCModel

Third order RC model

Model Variables#

  • Inputs

    • To: outdoor temperature (°C)

    • Tb: boundary temperature (°C)

    • Qgh: solar irradiance (W)

    • Qh: HVAC system heat (W)

    • Qv: heat from the ventilation system (W)

  • Outputs

    • xi: indoor temperature (°C)

  • States

    • xw: envelope temperature (°C)

    • xi: indoor temperature (°C)

    • xm: internal mass temperature (°C)

Model Parameters#

  • Thermal resistance

    • Ro: between the outdoor and the envelope (°C/W)

    • Ri: between the envelope and the indoor (°C/W)

    • Rm: between the indoor and the internal mass (°C/W)

    • Rb: between the indoor and the adjacent space (°C/W)

  • Thermal capacity

    • Cw: of the envelope (J/°C)

    • Ci: of the indoor (J/°C)

    • Cm: of the internal mass (J/°C)

  • Solar aperture

    • Aw: of the envelope (m²)

    • Ai: of the windows (m²)

  • Coefficient

    • cv: scaling of the heat from the ventilation (any)

  • State deviation

    • sigw_w: of the envelope dynamic (any)

    • sigw_i: of the indoor dynamic (any)

    • sigw_m: of the internal mass dynamic (any)

  • Measure deviation

    • sigv: of the indoor temperature measurements (any)

  • Initial mean

    • x0_w: of the envelope temperature (any)

    • x0_i: of the infoor temperature (any)

    • x0_m: of the internal mass temperature (any)

  • Initial deviation

    • sigx0_w: of the envelope temperature (any)

    • sigx0_i: of the infoor temperature (any)

    • sigx0_m: of the internal mass temperature (any)

class pysip.statespace.thermal_network.TwTi_RoRi(parameters=None, hold_order=0, method='mfd', name='', latent_forces='')[source]#

Bases: RCModel

Second order RC model

Model Variables#

  • Inputs

    • To: outdoor temperature (°C)

    • Qh: HVAC system heat (W)

  • Outputs

    • xi: indoor temperature (°C)

  • States

    • xw: envelope temperature (°C)

    • xi: indoor temperature (°C)

Model Parameters#

  • Thermal resistance

    • Ro: between the outdoor and the envelope (°C/W)

    • Ri: between the envelope and the indoor (°C/W)

  • Thermal capacity

    • Cw: of the envelope (J/°C)

    • Ci: of the indoor (J/°C)

  • State deviation

    • sigw_w: of the envelope dynamic (any)

    • sigw_i: of the indoor dynamic (any)

  • Measure deviation

    • sigv: of the indoor temperature measurements (any)

  • Initial mean

    • x0_w: of the envelope temperature (any)

    • x0_i: of the infoor temperature (any)

  • Initial deviation

    • sigx0_w: of the envelope temperature (any)

    • sigx0_i: of the infoor temperature (any)

class pysip.statespace.thermal_network.TwTi_RoRiAwAi(parameters=None, hold_order=0, method='mfd', name='', latent_forces='')[source]#

Bases: RCModel

Second order RC model

Model Variables#

  • Inputs

    • To: outdoor temperature (°C)

    • Qgh: solar irradiance (W)

    • Qh: HVAC system heat (W)

  • Outputs

    • xi: indoor temperature (°C)

  • States

    • xw: envelope temperature (°C)

    • xi: indoor temperature (°C)

Model Parameters#

  • Thermal resistance

    • Ro: between the outdoor and the envelope (°C/W)

    • Ri: between the envelope and the indoor (°C/W)

  • Thermal capacity

    • Cw: of the envelope (J/°C)

    • Ci: of the indoor (J/°C)

  • Solar aperture

    • Aw: of the envelope (m²)

    • Ai: of the windows (m²)

  • State deviation

    • sigw_w: of the envelope dynamic (any)

    • sigw_i: of the indoor dynamic (any)

  • Measure deviation

    • sigv: of the indoor temperature measurements (any)

  • Initial mean

    • x0_w: of the envelope temperature (any)

    • x0_i: of the infoor temperature (any)

  • Initial deviation

    • sigx0_w: of the envelope temperature (any)

    • sigx0_i: of the infoor temperature (any)

class pysip.statespace.thermal_network.TwTi_RoRiAwAicv(parameters=None, hold_order=0, method='mfd', name='', latent_forces='')[source]#

Bases: RCModel

Second order RC model

Model Variables#

  • Inputs

    • To: outdoor temperature (°C)

    • Qgh: solar irradiance (W)

    • Qh: HVAC system heat (W)

    • Qv: heat from the ventilation system (W)

  • Outputs

    • xi: indoor temperature (°C)

  • States

    • xw: envelope temperature (°C)

    • xi: indoor temperature (°C)

Model Parameters#

  • Thermal resistance

    • Ro: between the outdoor and the envelope (°C/W)

    • Ri: between the envelope and the indoor (°C/W)

  • Thermal capacity

    • Cw: of the envelope (J/°C)

    • Ci: of the indoor (J/°C)

  • Solar aperture

    • Aw: of the envelope (m²)

    • Ai: of the windows (m²)

  • Coefficient

    • cv: scaling of the heat from the ventilation (any)

  • State deviation

    • sigw_w: of the envelope dynamic (any)

    • sigw_i: of the indoor dynamic (any)

  • Measure deviation

    • sigv: of the indoor temperature measurements (any)

  • Initial mean

    • x0_w: of the envelope temperature (any)

    • x0_i: of the infoor temperature (any)

  • Initial deviation

    • sigx0_w: of the envelope temperature (any)

    • sigx0_i: of the infoor temperature (any)

class pysip.statespace.thermal_network.TwTi_RoRiRbAwAi(parameters=None, hold_order=0, method='mfd', name='', latent_forces='')[source]#

Bases: RCModel

Second order RC model

Model Variables#

  • Inputs

    • To: outdoor temperature (°C)

    • Tb: boundary temperature (°C)

    • Qgh: solar irradiance (W)

    • Qh: HVAC system heat (W)

  • Outputs

    • xi: indoor temperature (°C)

  • States

    • xw: envelope temperature (°C)

    • xi: indoor temperature (°C)

Model Parameters#

  • Thermal resistance

    • Ro: between the outdoor and the envelope (°C/W)

    • Ri: between the envelope and the indoor (°C/W)

    • Rb: between the indoor and the boundary space (°C/W)

  • Thermal capacity

    • Cw: of the envelope (J/°C)

    • Ci: of the indoor (J/°C)

  • Solar aperture

    • Aw: of the envelope (m²)

    • Ai: of the windows (m²)

  • State deviation

    • sigw_w: of the envelope dynamic (any)

    • sigw_i: of the indoor dynamic (any)

  • Measure deviation

    • sigv: of the indoor temperature measurements (any)

  • Initial mean

    • x0_w: of the envelope temperature (any)

    • x0_i: of the infoor temperature (any)

  • Initial deviation

    • sigx0_w: of the envelope temperature (any)

    • sigx0_i: of the infoor temperature (any)

class pysip.statespace.thermal_network.TwTi_RoRiRbAwAicv(parameters=None, hold_order=0, method='mfd', name='', latent_forces='')[source]#

Bases: RCModel

Second order RC model

Model Variables#

  • Inputs

    • To: outdoor temperature (°C)

    • Tb: boundary temperature (°C)

    • Qgh: solar irradiance (W)

    • Qh: HVAC system heat (W)

    • Qv: heat from the ventilation system (W)

  • Outputs

    • xi: indoor temperature (°C)

  • States

    • xw: envelope temperature (°C)

    • xi: indoor temperature (°C)

Model Parameters#

  • Thermal resistance

    • Ro: between the outdoor and the envelope (°C/W)

    • Ri: between the envelope and the indoor (°C/W)

    • Rb: between the indoor and the boundary space (°C/W)

  • Thermal capacity

    • Cw: of the envelope (J/°C)

    • Ci: of the indoor (J/°C)

  • Solar aperture

    • Aw: of the envelope (m²)

    • Ai: of the windows (m²)

  • Coefficient

    • cv: scaling of the heat from the ventilation (any)

  • State deviation

    • sigw_w: of the envelope dynamic (any)

    • sigw_i: of the indoor dynamic (any)

  • Measure deviation

    • sigv: of the indoor temperature measurements (any)

  • Initial mean

    • x0_w: of the envelope temperature (any)

    • x0_i: of the infoor temperature (any)

  • Initial deviation

    • sigx0_w: of the envelope temperature (any)

    • sigx0_i: of the infoor temperature (any)

Gaussian processes#

class pysip.statespace.gaussian_process.GPProduct(gp1, gp2)[source]#

Bases: GPModel

Product of two Gaussian Process Covariance

gp1GPModel

GPModel instance

gp2GPModel

GPModel instance

The MEASURE_DEVIATION and MAGNITUDE_SCALE of the gp2 are fixed because they are already defined in gp1.

Model Variables#

  • Inputs

  • Outputs

  • States

class pysip.statespace.gaussian_process.GPSum(gp1, gp2)[source]#

Bases: GPModel

Sum of two Gaussian Process model

gp1GPModel

GPModel instance

gp2GPModel

GPModel instance

The MEASURE_DEVIATION of the gp2 is fixed because it is already defined in gp1.

Model Variables#

  • Inputs

  • Outputs

  • States

class pysip.statespace.gaussian_process.Matern12(parameters=None, hold_order=0, method='mfd', name='')[source]#

Bases: GPModel

Matérn covariance function with smoothness parameter = 1/2

Model Variables#

  • Inputs

  • Outputs

    • f(t): stochastic process (any)

  • States

    • f(t): stochastic process (any)

Model Parameters#

  • Magnitude scale

    • mscale: control the overall variance of the function (any)

  • Length scale

    • lscale: control the smoothness of the function (any)

  • Measure deviation

    • sigv: measurement standard deviation (any)

class pysip.statespace.gaussian_process.Matern32(parameters=None, hold_order=0, method='mfd', name='')[source]#

Bases: GPModel

Matérn covariance function with smoothness parameter = 3/2

Model Variables#

  • Inputs

  • Outputs

    • f(t): stochastic process (any)

  • States

    • f(t): stochastic process (any)

    • df(t)/dt: derivative stochastic process (any)

Model Parameters#

  • Magnitude scale

    • mscale: control the overall variance of the function (any)

  • Length scale

    • lscale: control the smoothness of the function (any)

  • Measure deviation

    • sigv: measurement standard deviation (any)

class pysip.statespace.gaussian_process.Matern52(parameters=None, hold_order=0, method='mfd', name='')[source]#

Bases: GPModel

Matérn covariance function with smoothness parameter = 5/2

Model Variables#

  • Inputs

  • Outputs

    • f(t): stochastic process (any)

  • States

    • f(t): stochastic process (any)

    • df(t)/dt: derivative stochastic process (any)

    • d²f(t)/d²t: second derivative stochastic process (any)

Model Parameters#

  • Magnitude scale

    • mscale: control the overall variance of the function (any)

  • Length scale

    • lscale: control the smoothness of the function (any)

  • Measure deviation

    • sigv: measurement standard deviation (any)

class pysip.statespace.gaussian_process.Periodic(parameters=None, hold_order=0, method='mfd', name='', J=7)[source]#

Bases: GPModel

Periodic covariance function

iv is the modified Bessel function of the first kind. Useful relation: iv(J+1, x) = iv(J-1, x) - 2*J/x * iv(J, x)

J: int

Degree of approximation (default=7)

Arno Solin and Simo Särkkä (2014). Explicit link between periodic covariance functions and state space models. In Proceedings of the Seventeenth International Conference on Artifcial Intelligence and Statistics (AISTATS 2014). JMLR: W&CP, volume 33.

Model Variables#

  • Inputs

  • Outputs

    • sum(f(t)): sum of stochastic processes (any)

  • States

Model Parameters#

  • Period

    • period: period of the function (any)

  • Magnitude scale

    • mscale: control the overall variance of the function (any)

  • Length scale

    • lscale: control the smoothness of the function (any)

  • Measure deviation

    • sigv: measurement standard deviation (any)

class pysip.statespace.gaussian_process.QuasiPeriodic12(parameters=None, hold_order=0, method='mfd', name='', J=7)[source]#

Bases: GPModel

Quasi Periodic covariance function, e.g. Periodic x Matern12

iv is the modified Bessel function of the first kind. Useful relation: iv(J+1, x) = iv(J-1, x) - 2*J/x * iv(J, x)

J: int

Degree of approximation (default=7)

Arno Solin and Simo Särkkä (2014). Explicit link between periodic covariance functions and state space models. In Proceedings of the Seventeenth International Conference on Artifcial Intelligence and Statistics (AISTATS 2014). JMLR: W&CP, volume 33.

Model Variables#

  • Inputs

  • Outputs

    • sum(f(t)): sum of stochastic processes (any)

  • States

Model Parameters#

  • Period

    • period: period of the function (any)

  • Magnitude scale

    • mscale: control the overall variance of the function (any)

  • Length scale

    • lscale: control the smoothness of the function (any)

    • decay: control the decay of the periodicity (any)

  • Measure deviation

    • sigv: measurement standard deviation (any)

class pysip.statespace.gaussian_process.QuasiPeriodic32(parameters=None, hold_order=0, method='mfd', name='', J=7)[source]#

Bases: GPModel

Quasi Periodic covariance function, e.g. Periodic x Matern32

iv is the modified Bessel function of the first kind. Useful relation: iv(J+1, x) = iv(J-1, x) - 2*J/x * iv(J, x)

Args:

J: Degree of approximation (default=7)

References:

Arno Solin and Simo Särkkä (2014). Explicit link between periodic covariance functions and state space models. In Proceedings of the Seventeenth International Conference on Artifcial Intelligence and Statistics (AISTATS 2014). JMLR: W&CP, volume 33.

Model Variables#

  • Inputs

  • Outputs

    • sum(f(t)): sum of stochastic processes (any)

  • States

Model Parameters#

  • Period

    • period: period of the function (any)

  • Magnitude scale

    • mscale: control the overall variance of the function (any)

  • Length scale

    • lscale: control the smoothness of the function (any)

    • decay: control the decay of the periodicity (any)

  • Measure deviation

    • sigv: measurement standard deviation (any)