Appliance Penetration and the Wisdom of Crowds

Some of the burning questions for electricity utilities in Australia have to do with appliance take up. I decided to see what the wisdom of crowds could tell us about the take-up of some key appliances which are affecting load profiles and consumption trends. My crowd-sourced data comes from Google Insights for Search. I have taken the weekly search volume indexes for three search terms: “air conditioner”, “pool pump” and “solar pv”. In addition, I also took the search volumes for “energy efficient” to see if there has been a fundamental change in the zeitgeist in terms of energy efficiency.

Firstly, let’s have a look at Google “air conditioner” search data.

The graph shows strong seasonality with people searching more for air conditioners in summer which makes sense. We see indications of how profound the growth of air conditioners has been in Australia (and South East Queensland in particular), I decided to compare growth in air conditioner searching by country and city. Since 2004, Australia ranks second behind the US for air conditioner searches. For cities, Brisbane and Sydney rank fourth and fifth in the world, but if we adjust for population they rank second and third respectively behind Huston. This has been one of the causes behind the recent difficulties in forecasting demand. One of the big questions is will air conditioning load continue to grow or has air conditioner penetration reached saturation point? Read on to find some insights that I think this data may have uncovered.

When we look at the data for the search term “energy efficient”, we get the opposite temperature effect with dips in searches during summer and maybe winter is noticeable in this graph.

This tells us that people become less concerned with energy efficiency as comfort becomes more important which has also been shown in other studies. But if we want to look for underlying changes in behavior then we need to account for temperature sensitivity in this data and the first thing we need to do is come up with a national temperature measure that we can compare with the Google data. To do this I get temperature data for Australia’s five largest cities from the Bureau of Meteorology and create national daily maximum temperatures for 2004-2011 comprised of a population weighted mean of the maximum temperatures of the five largest Australian cities. This accounts for about 70% of Australia’s population and an even greater proportion of regular internet users. Now we can quantify the relationship between our appliances, energy efficiency and temperature.

Below are the scatter charts showing the R2 correlations. “Solar PV” is uncorrelated with temperature but all of the other search terms show quite good correlation. You may also notice that I have tried to account for the U-curve in the relationship between “Energy Efficient” and temperature by correlating with the absolute number of degrees from 21C. The main relationship is with hot weather; accounting for the U-curve only adds slightly to the R2. Interestingly, people don’t start searching for air conditioners until the temperature hits 25C, and then there is a slightly exponential shape to the increase in searches. For the purposes of this post I will stick to simple linear methods, but further analysis may consider a log link GLM or Multiple Adaptive Regression Splines (MARS) to help explain this shape in the data.

Now to the central question that this post is trying to answer: what are the underlying trends in these appliances, can we find this out from Google and BOM data and can we get some insight into current underlying trends and how this might help uncover the underlying trends in consumption and load factor.  To do this I create a dummy variable to represent time and regress this with temperature to see to what extent each factor separately describes the number of Google searches. I build separate models for each year which separates the trend over time in searches from the temperature related ones.

But before I do that I can look at the direct relationship between annual “solar PV” trends.

There was not enough search data to go all the way back to 2004 (which is of itself interesting) so we only go back 2007. What we see is a large growth in searches in 2008, statistically insignificant trend in 2009 and 2010 and a distinct decline during 2011. It looks like removal of incentives and changes to feed in tariffs are having an effect. The error bars show the 95% confidence interval.

Now on to pool pumps. Here we see a steady rise on searching for pool pumps which indicates that we can expect growth in pool pump load to also grow nationally. If anything it looks like the search rate is increasing and maybe apart from 2008 maybe not affected by the 2008 global downturn.

Once we account for temperature variability we see really no trend in terms of energy efficiency until 2010. This came after the collapse of Australia’s carbon trading legislation and the collapse of internally accord on climate change policy. It stands to seems to me that this is also reflected in the public concern with energy efficiency. It seems to me that if there is widespread public concern about the contribution of electricity to cost of living then it should be reflected here but it isn’t. This also seems to suggest that for consumers the motivation towards energy efficiency is driven by a sense of social responsibility rather than being an economic decision.

Finally, air conditioning. What we see represented here is the rapid growth in air conditioning that happened in 2004-2005 with a slowing in growth from 2006-2008. It looks like maybe the government rebates of 2009 may have been partially spent on air conditioning. But what we see is that from 2010 onwards there has been no significant trend in search term growth. Does this suggest that we have finally reached saturation some time during 2010?

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One thought on “Appliance Penetration and the Wisdom of Crowds

  1. Pingback: Have We Seen the End of Peak Demand? | Inside Smartgrid Analytics

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