Evidence is mounting that closing the blinds can reduce the rate of heat decline—at least when the wind is mostly still. The figure compares the heat loss rate to the indoor-outdoor contrast. Higher rates are bad.
Closing the blinds keeps reduces the temperature loss rate by about 0.1 degree F/hour. Integrate that loss rate over the course of the night and closing the blinds keeps the house 1 degree F warmer.
To keep the house 1 degree F warmer by turning up the thermostat 1 degree F during the night would increase energy use about 1%. This could save as much as 3% on the heating bill according to many sites who uniformly cite miserably.
My gas bill is about $70/month during the winter, assuming 4 months, I’ll save $8.40 per year by closing the blinds. This should pay for the blinds in only a little over 120 years.
I’ll continue collecting data, and may update this if the results change. Your mileage may vary.
I’ve been collecting various sets of data with my Arduino-based RIMU.1 environmental data logger. In particular, I have seven nights worth of overnight records showing both indoor and outdoor temperatures. The data were all taken with the recorder in the same locations. Some nights are contiguous, but the 7-night set spans about 10 days.
The indoor temperature, during the “night” setting on the thermostat tends to drop quite linearly until the furnace runs again around 6 am. The following graph shows the data, which looks like a black staircase, along with a linear fit of the data. The fit is quite good.
I capture only one important datum from this overnight trend—the slope. Later I’ll show how I use the rate of temperature decline, measured in degrees Fahrenheit per hour of decline.
The outdoor temperature sensor is a thermistor, and the measurement is markedly noisier. The temperature varies more too, presumably due to wind turbulence near the house. The linear trend is much less clear, though it is clearly cooling some during the night. I do measure the average difference between the indoor and outdoor temperature which I call the “mean temperature contrast”. This is done in degrees Fahrenheit, though I probably should have done it in Kelvin. The contrast is measured as a difference, not a ratio.
If I take the seven days of data and plot the temperature decline rate versus the temperature contrast I see the picture you might expect, below. Remember that bigger numbers mean faster falling indoor temperature. You can see the outlier caused by the windy night. There is another outlier (~28, ~0.60) which, if I recall correctly, is the one night I closed the blinds. A hint of the follow-on experiment.
The next question is, does closing the blinds cause this rate to be materially different?
Since the RIMU’s creation I’ve logged two noteworthy data sets along with several that are not yet ripe for sharing. I logged the refrigerator’s internal temperature overnight (using the thermistor) and I logged the oven’s internal temperature for a few minutes while making bread sticks today. The results are more interesting than I might have expected.
The following plot shows the oven temperature as measured with the RIMU’s thermocouple. The set point was 425, which appears not to be reached in steady state. In fact, typical operation shows a swing of about 37 degrees F. Recovery is quite fast, actually. These data were collected with the convection fan off, and the thermocouple near the front of the oven.
The second plot of interest is the refrigerator data, taken with the thermistor located about halfway back, one quarter of the way from the right edge, resting just above the top shelf.
The temperature oscillates locally, after about half an hour of quite the motor runs for about half an hour, creating a three degree local temperature swing. What is bizarre is the nightly decline (shown by the blue line) which measured an average temperature decline from 31 F to 29 F.