Is defrosting the freezer worth the trouble?

Short answer: No, not in my case.

Long answer: My refridgerator is about 5 years old, has a volume of 103 liters and a freezing compartment of 16 liters. Its energy class is “A”, the label states a power consumption of 219 kWh per year.

I borrowed a power meter, and measured consumption before and after defrosting. Here is a chart of the measured data. I measured only 5 values, one week before defrosting, 1 hour after turning the fridge on, and then 8, 23 and 47 hours after defrosting. The y-axis shows the average power consumption, i.e. the intensity the fridge sucks energy.

The data suggests that the power consumption is on average at 15.9 Watt. The difference before and after defrosting is 0.5 Watt. I do not want to attribute this to the defrosting. The outside temperature in the week before defrosting was higher, also I used the fridge more often because there were holidays and I was at home. But even if defrosting would have this effect, these 0.5 Watt add up to only 4.38 kWh or 1.50 EUR per year.

Another insight was that 15.9 Watt project to 139 kWh per year and this is quite below the value 219 kWh stated on the energy label. Even after 5 years! Maybe this is because I put the thermostat a rather low level (2 out of 5), or maybe the consumption in the summer will be much higher.

Comparing my expenses

Now that I have collected my expenditures via Twitter and bank transaction data, and categorized it according to COICOP, in this blog post I compare it with the typical expenditures of a household of my type with an income level like mine.

The German Federal Statistical Office conducts every five years (last time in 2008) a survey on Income and Consumption. On their website, you can find this nice visualization.

The dataset I used is provided here. It is also part of the pft package. The dataset is not identical with the official data, some information is lost by the processing.

Here is the chart comparing my expenses in 2011 with the typical expenses.

The main problem when comparing me with a typical consumer is that 15% of my expenses remained uncategorized. If I assume it goes in either Food, Recreation, Restaurants or Misc, and add those amounts, it turns that almost the half of my expenses goes in these four categories, while typical would be 35%. Especially I tend to spend more money on Recreation and Restaurants. On the other side, I spend less money on Housing (small apartement) and Transportation (no car).

Categorizing my expenses

In order to analyse my expenses, a classification scheme is necessary. I need to identify categories that are meaningful to me. I decided to go with the “Classification of Individual Consumption by Purpose” (COICOP), for three reasons:

• It is made by people who have thought more about consumption classification than I ever will.
• It is feasible to assign bank transactions and tracked cash spendings to one of the 12 top level categories.
• It is widely used by statistics divisions, e.g. the Federal Statistical Office of Germany, Eurostat, and the UN. This means I can do social comparisons: In which categories do I spend more money than the average? Do the prices I pay rise faster than the price indices suggest?

So I classified my last year’s expense data according to COICOP. Here is a chart showing the portions of the categories for each month:

For me, the holidays, prepared in August and traveled in September (shown as unknown expenses), are much more dominant than I expected. Except for the new glasses in September I did not make any larger investments.

I like this kind of chart more than stacked bar charts because the history for each category is very visible. This chart is called inkblot chart. I stumbled on it on junk charts, asked how to implement it in R on StackOverflow, and included a revised version in the latest pft package. See below for more information.

Tracking my expenses

One new-year resolution I made last year was to understand where my money goes. From previous experiments I know that expense tracking has to be as simple as possible. My approach is to

• Use my cash card as often as possible. This automatically tracks the date and some information on the vendor.
• Use twitter to track my cash expenses. This supplements the bank account statement data.
• Edit, enrich, merge and visualise the two data sources with R. Because it is fun playing with R!

Now after more than one year of expense tracking, I can now analyse the results. The first result however, was disappointing. My cash tracking with twitter was not as complete as I thought it is. Below is a figure that displays the sum tracked with twitter divided by the sum withdrawn from my bank account for each month of 2011.

If I had tracked my cash expenses completely, the ratio would be around 100, the gray dashed line. However, it is systematically below. For September, there is an explanation: I was on holidays and did intentionally not track the expenses. But even considering that, there remain 18 percent of my cash spendings unexplained!

More analysis results will follow. If you are interested in technical aspects of the expense tracking, such as importing the tweets and bank statements, read on. However, there is no R code today, since there is no example data.

Electricity prices rose by 16% in two years

With about 490 kWh electricity consumption I am a rather small customer. Over the year this sums up to about 160 EUR. So I had a look at the costs.

I was suprised to learn that the prices rose quite clearly. My tariff is two-part, a base price and a kilowatt-hour rate. If I look at the total costs and divide them by my consumption, I get

year	EUR-Cent/kWh
2008	29.8
2009	33.1
2010	34.6

This means, in 2010 I had to pay 4% more than in 2009 and 16% more than in 2008. My energy use per day, on the other side, remained quite stable at 1.35 kWh/day.

What can I do about it? The first impulse was to collect more data, maybe by using a WiFi energy sensor. Would be really fun. However, I can not see many options to reduce my consumption. Maybe I can turn off the internet router while at work.

It seems more sensible to me to switch my energy provider, which I did today. My new energy provider will charge a bit more, but gives me 50 EUR new-customer bonus and claims to deliver certified renewable energy.

How much is a shower?

After looking at my heating expenses, I turned to the costs for water heating. For some time, I looked at my water meter before and after taking a shower or a bath. Quite often, I forgot one or the other measurement, but I collected about 40 observations. Here is what they look like:

The data suggest that for a shower, it takes between 17 and 26.5 liters hot water and between 11 and 16.5 liters cold water. For a bath, it is 60 to 77 liters hot water and 24 to 32.5 liters cold water. (The numbers refer to the 25% and 75% percentiles, respectively.) The larger share of cold water for a shower makes sense, since I use cold water at the end of the shower for its “invigorating effect”.

Multiplied with the average costs, as charged by my landlord the last three years, a bath takes 0.94 to 1.22 EUR and a shower costs 0.29 to 0.45 EUR (again, first and third quartiles). So taking a shower for 0.50 EUR at the fitness club is not optimal, but also not very expensive.

There are water saving shower heads for 30 EUR. It is advertised that such a shower head uses 6.5 liter per minute instead the usual 15 to 16 liters per minute. I believe 15 liters per minute is too much. So let’s assume I save 3.5 liters per minute or (using the median water use of the data) 12 liters per shower. Is it cost-efficient?

Twelve liters less per shower at 10.5 EUR/cbm means a saving of 0.126 EUR per shower. I assume 20 showers a month. This is tentative, since with this assumption the costs sum up to 70 EUR a year for hot water while my bills amount in average to 110 EUR total costs for hot water. So the new shower head saves 20*0.126=2.52 EUR per month.

Let’s calculate the payback period. With an interest rate of 4% p.a. and 5 years expected serviceable life the monthly gain calculates to

2. 52 - 30 * 0. 04 / 12 - 30 / (5 * 12) = 1. 92

so after 30 / 1. 92 = 16 months, the investment is repaid. This seems acceptable. But just to be sure, let’s calculate the internal rate of return, too. In excel, there is a function IRR for this procedure, which is implemented in three lines in R. For convenience, the irr function is stored in the pft package. The result is 8.3% which seems decent.

Just for reference, here comes the R code for the plot and the irr function:

Heating costs

In 2010, my heating costs exceeded my advance payments by about 25%. This motivated me to decompose the costs to see what drove the changes. Here is the result:

The numbers refer to Euros. Read von right to left: 2010 was a cold year (+102EUR), but gas consumption in this house was relatively low (–89EUR). Also, running costs and gas price were below previous year’s value (–49EUR). The main driver (+183EUR) for the increased costs was my share on the total costs. In 2009, I had to pay 1.7% of the total costs, which rose to 2.3% in 2010. This means my landlord assumes I used more energy than the other tennants in 2010. I doubt that, and one aspect to support my doubts is that the values of the two heat meters in my living room were nearly at the same value, while I used only one heater.

Thus I believe it was a measurement error. I am not going to complain about this, but will check next measurements very carefully.

Not a direct result of the decomposition, but also interesting is the (temperature adjusted) enery use per square meter, which was in 2010 by about 110 kWh/m2, which is nearly average.

Here’s the R code to produce the chart: