Yes, there are at least two ways to do it.

###Static, deterministic analysis One is to look at total energy in over a long time period, such that the internal temperature is the same at the start and the end. And if you take the time period to be long enough (30 days or more, for many buildings), then any changes in heat content of the thermal mass will be negligible when you divide out over the whole period. You can then integrate $T_{internal}-T_{external}$ over time, to find the total number of Kelvin-seconds of temperature difference. And divide total energy put in by the heating system plus insolation, by your number of Kelvin seconds, to get the house's thermal conductivity in W/K. Once you've got that, you can then look at individual night's cooling curves to estimate the thermal mass of the house, e.g. by using a simple one-box simulation.

###Bayesian, probabilistic time-series analysis Another method, and one I've had a little involvement in myself, is the Bayesian approach, which makes the most of all the available data, and jointly estimates probability distributions for both parameters simultaneously. This method is harder to implement, but is a lot more powerful: you will probably only need a few days' worth of data; and you will get much more information out than you would from the static method.

You write down the equation for the probability of observing the temperature series $T_{internal}$ for arbitrary values of the house's thermal conductivity and thermal mass R, and given external temperature time series $T_{external}$; you add in prior distributions for your thermal mass, thermal conductivity, observation errors, and calculate a posterior distribution for thermal mass and thermal conductivity.

The more information you've got, the more elaborate you can make the model. Add in time-series of heating-system energy use first, as that will be the most useful information. Then insolation, and wind speed and direction. Build up the complexity of your model one variable at a time, to ensure you've got sane, plausible results at each stage.

For an example of this Bayesian approach for a single building element, see "Inferring the thermal resistance and effective thermal mass of a wall using frequent temperature and heat flux measurements" by some of my colleagues. There will be more papers in this series - keep an eye out for new papers by the first two of those authors in particular.

Yes, there are at least two ways to do it.

Static, deterministic analysis

One is to look at total energy in over a long time period, such that the internal temperature is the same at the start and the end. And if you take the time period to be long enough (30 days or more, for many buildings), then any changes in heat content of the thermal mass will be negligible when you divide out over the whole period. You can then integrate $T_{internal}-T_{external}$ over time, to find the total number of Kelvin-seconds of temperature difference. And divide total energy put in by the heating system plus insolation, by your number of Kelvin seconds, to get the house's thermal conductivity in W/K. Once you've got that, you can then look at individual night's cooling curves to estimate the thermal mass of the house, e.g. by using a simple one-box simulation.

Bayesian, probabilistic time-series analysis

Another method, and one I've had a little involvement in myself, is the Bayesian approach, which makes the most of all the available data, and jointly estimates probability distributions for both parameters simultaneously. This method is harder to implement, but is a lot more powerful: you will probably only need a few days' worth of data; and you will get much more information out than you would from the static method.

You write down the equation for the probability of observing the temperature series $T_{internal}$ for arbitrary values of the house's thermal conductivity and thermal mass R, and given external temperature time series $T_{external}$; you add in prior distributions for your thermal mass, thermal conductivity, observation errors, and calculate a posterior distribution for thermal mass and thermal conductivity.

The more information you've got, the more elaborate you can make the model. Add in time-series of heating-system energy use first, as that will be the most useful information. Then insolation, and wind speed and direction. Build up the complexity of your model one variable at a time, to ensure you've got sane, plausible results at each stage.

For an example of this Bayesian approach for a single building element, see "Inferring the thermal resistance and effective thermal mass of a wall using frequent temperature and heat flux measurements" by some of my colleagues. There will be more papers in this series - keep an eye out for new papers by the first two of those authors in particular.

Yes, there are at least two ways to do it.

###Static, deterministic analysis One is to look at total energy in over a long time period, such that the internal temperature is the same at the start and the end. And if you take the time period to be long enough, then any changes in heat content of the thermal mass will be negligible when you divide out over the whole period. You can then take the integral of $T_{internal}-T_{external}$, to find the total number of Kelvin-seconds of temperature difference. And divide total energy put in by the heating system plus insolation, by your number of Kelvin seconds, to get the house's thermal conductivity in W/K. Once you've got that, you can then look at individual night's cooling curves to estimate the thermal mass of the house, e.g. by using a simple one-box simulation.

###Bayesian, probabilistic time-series analysis Another method, and one I've had a little involvement in myself, is the Bayesian approach, which makes the most of all the available data, and jointly estimates probability distributions for both parameters simultaneously. This method is harder to implement, but is a lot more powerful: you need much less data in, and get much more information out.

You write down the equation for the probability of observing the temperature series $T_{internal}$ for arbitrary values of the house's thermal conductivity and thermal mass R, and given external temperature time series $T_{external}$; you add in prior distributions for your thermal mass, thermal conductivity, observation errors, and calculate a posterior distribution for thermal mass and thermal conductivity.

The more information you've got, the more elaborate you can make the model. Add in time-series of heating-system energy use first, as that will be the most useful information. Then insolation, and wind speed and direction. Build up the complexity of your model one variable at a time, to ensure you've got sane, plausible results at each stage.

For an example of this Bayesian approach for a single building element, see "Inferring the thermal resistance and effective thermal mass of a wall using frequent temperature and heat flux measurements" by some of my colleagues. There will be more papers in this series - keep an eye out for new papers by the first two of those authors in particular.

Yes, there are at least two ways to do it.

###Static, deterministic analysis One is to look at total energy in over a long time period, such that the internal temperature is the same at the start and the end. And if you take the time period to be long enough (30 days or more, for many buildings), then any changes in heat content of the thermal mass will be negligible when you divide out over the whole period. You can then integrate $T_{internal}-T_{external}$ over time, to find the total number of Kelvin-seconds of temperature difference. And divide total energy put in by the heating system plus insolation, by your number of Kelvin seconds, to get the house's thermal conductivity in W/K. Once you've got that, you can then look at individual night's cooling curves to estimate the thermal mass of the house, e.g. by using a simple one-box simulation.

###Bayesian, probabilistic time-series analysis Another method, and one I've had a little involvement in myself, is the Bayesian approach, which makes the most of all the available data, and jointly estimates probability distributions for both parameters simultaneously. This method is harder to implement, but is a lot more powerful: you will probably only need a few days' worth of data; and you will get much more information out than you would from the static method.

You write down the equation for the probability of observing the temperature series $T_{internal}$ for arbitrary values of the house's thermal conductivity and thermal mass R, and given external temperature time series $T_{external}$; you add in prior distributions for your thermal mass, thermal conductivity, observation errors, and calculate a posterior distribution for thermal mass and thermal conductivity.

The more information you've got, the more elaborate you can make the model. Add in time-series of heating-system energy use first, as that will be the most useful information. Then insolation, and wind speed and direction. Build up the complexity of your model one variable at a time, to ensure you've got sane, plausible results at each stage.

For an example of this Bayesian approach for a single building element, see "Inferring the thermal resistance and effective thermal mass of a wall using frequent temperature and heat flux measurements" by some of my colleagues. There will be more papers in this series - keep an eye out for new papers by the first two of those authors in particular.