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?


What’s going on with load factor?

For the last couple of years the commonly accepted view is that load factor growth is a major threat to the economical operation of electricity utilities in Australia. The problem is that peak demand has been growing but consumption has not been growing at the same rate. For utilities, the main capital expenditure cost is providing enough network capacity for maximum demand, but revenue from sales of electricity comes from what is sold.

Part of the hypothesis for why load is growing is that consumers are buying more energy efficient devices and are bombarded with energy conservation messages which means that ordinarily they use less electricity but on peak hot days they forgo conservation for comfort and contribute to peak demand. The result is that electricity utilities still have high capital expenditure but the revenue base shrinks due to non-peak energy saving by consumers. But is this really true or is it a false assumption based on a subtle variation of the Khazzoom-Brookes postulate?

In terms of an energy paradox, there are a couple of observations that I can make from the analytics we have been doing recently. As one industry leader told me recently, network forecasting was very easy until about 2006. The period from about 2003 to 2008 did not only include some of the hottest years on record but also was notable for record population growth.

Then everything changed.

Since the late-2000s many substation forecasts have started to break down. This has been a problem common to a number of utilities, but it has been of particular interest to networks that have been experiencing growth in connections. It is compounded by growing political pressure to reduce capital expenditure.

Firstly, we had a global downturn which depressed economic activity and caused widespread job losses that encouraged consumers to save money. In Australia, we also had what is starting to look like a long cycle change in the climate with a series of wetter, cooler summers which means that peak air conditioner use has fallen in many places. And in mid-2008 the rate of population growth started to slow. Even more curiously, the pattern of consumption by housing age starts to change. From about 1980 until the mid-2000s, the newer the house the higher the consumption, but around 2005-2008 all that changed. After this date, fewer houses start to consume relatively less electricity and possibly also less peak demand. Daniel Collins at Ausgrid has done some interesting analysis in this regard which he presented at the 2011 ANZ Smart Utilities Conference.

And then there are solar photo voltaic panels which have been subsidised by government and in effect increase load factor and therefore electricity prices. Solar panels reduce net consumption by feeding electricity back into the grid, and this usually happens during the middle of the day when the sun is brightest but peak demand happens in the early evening on hot days when people get home to a hot house and turn on the air conditioning. This leads to lower revenue but the same or higher network costs.

Electricity is perhaps the oldest post-Industrial revolution technology still in widespread use and this means that there is a long body of thought and experience in understanding its consumption and the mechanisms for its delivery. This strength may also however prove to be a weakness. Many ideas have been around for a long time and are no longer routinely challenged. The energy paradox is one of them. I am not saying that the underlying of the premise is wrong, but there is certainly room to reinterpret this idea in relation to the current situation in Australia. Challenging orthodox thinking. You need to be very sure of your facts. This is where analytics and a wide spread of both data and data mining methodologies can help. As I have said, analytics is at its strongest when it is not hypothesis driven but is working without an explicit hypothesis or trying to decide between many competing ones.

The answer is that I don’t really know what is going on with load factor. I have yet to be convinced that anyone (including myself) has worked how to properly account for the climatic variance of peak demand, or fully understand the relationship between housing age and consumption, or what the true relationship is with population growth, or how many speeds our multi-speed economy has, but something is definitely happening and investigative analytics has excellent potential in being able greatly develop our understanding of how all of these effects interact.

Melbourne Water and Weekend Flooding

Courtesy of the weekend flooding in Melbourne I have used the Melbourne Water instantaneous flow rates published on their website to check out the peak flooding of Plenty River at Greensborough.

This is a shot from standing next to the automatic reporting station. The small white post sticking out of the water in the middle of the photo is the road side flood level indicator which I think tops at 3.5 metres which is about consistent with the automatically reported river level.

And this is photo from a short way downstream showing a sign stating the obvious.