Tableau

A Friendly BC Hydro Electricity Consumption Analysis using Tableau

If there is something to appreciate about the Canadian West Coast, it is definitely in the way that it leads by example through environmentally friendly practices. One of the ways that British Columbia takes on this initiative is through administering electrical energy in the cleanest and most cost-efficient way.  This is all made possible through BC Hydro, a Canadian-controlled crown corporation responsible for providing British Columbia residences reliable and affordable electricity. British Columbia prides itself in delivering electrical energy and is known to have one of the lowest electricity consumer prices in Canada. One way to show appreciation for natural resource consumption is to, of course, take a look at our very own personal electricity consumption. Before diving into any analysis, first it might be useful to provide some context into how BC Hydro prices electricity consumption.

Electricity Pricing

BC Hydro uses a two-stage pricing algorithm where consumers are required to pay $0.0858 per kWh up to a max consumption threshold of 1350 kWh within a two-month period. This rate increases to the second stage at $0.1287 per kWh if the consumer uses over 1350 kWh within the two months. In addition to the two-stage pricing algorithm, consumers are required to pay a base rate of $0.1899 times the number of days in their billing period, and pay a rider-rate which is a buffer cost to consumers to cover unpredictable economic circumstances such as abnormal market prices or inaccurate water level forecasts. The entire cost becomes subject to GST and the total is the payable amount during every billing period.  If you want to read for knowledge’s sake, BC Hydro thoroughly explains the pricing of electricity consumption on their website.

Motivation

The motivation behind this post is to analyze a very special electricity consumption data set (special because I was given permission by my great friend Skye to analyze his electricity consumption data!) I will be analyzing Skye’s personal electricity consumption in the full calendar years of 2015 and 2016.

Data, Set, Go!

It is amazing how accessible BC Hydro makes personal electricity consumption data to its paying customers. Skye has revealed that to obtain your personalized data set, you would simply login to your MyHydro account, and request an exported .csv file. Within 24 hours of submitting a request, you will receive an e-mail with a personalized .csv file.

The file contains two data columns, the first column containing the Start Interval Time/Date representing the time stamp at the beginning of each hour of every day of the year, and the second column containing Net Consumption (kWh) representing the amount of electricity used up until the end of the hour measured in kilowatts per hour!

One thing I noticed within Skye’s data set is that there were some missing Net Consumption values.  Missing values were indicated with N/A attached to some time stamps. Further looking into this data and without any prior knowledge to Skye’s electricity consumption behavior, there is no way to really know for certain why the data was missing. To rectify this situation, I simply replaced missing values with the most recent level of net consumption. For example, if there was a missing value on September 13, 2016 at 10:00am, I would assume a forward-looking value such that this missing value would be replaced with the net consumption value from 9:00am. If there was trailing missing values, or consecutive missing values, I would replace them all with the most recent available net consumption value.

Without further ado, I shall begin reporting what I found from Skye’s electricity consumption data using Tableau Public. Pokemon Fans, please take notice in the Venusaur colour palette!

There was an increase of about 3% in net consumption expenditure from 2015 to 2016. Skye exhibits typical seasonal trends in electricity consumption.

Electricity Consumption Seasonal

Net Expenditure is what Skye is charged during the two-stage pricing algorithm on a per month basis. He does not actually ever step into stage-two of pricing as he is well below the 1350 kWh threshold every two months (which is amazing, save energy and save money!)

Visually, it seems that Skye exhibits a typical consumption behavior, where his expenditure in the first three quarters of each year has a decreasing trend, he hits a minimum in September, then scales back up in the Fall and Winter months. Is it possible that what we see visually is not verified statistically? We can validate this theory that Skye is behaving typically or the way he should be. Consider the following R code:

table2015 <- c(42.87, 39.01, 33.9, 29.25, 22.5, 26.68, 33.06, 24.45, 15.57,
 27.11, 49.37, 49.39)
table2016 <- c(44.46, 31.57, 32.68, 29, 25.53, 25.43, 24.53, 29.42, 20.43,
 32.03, 43.96, 67.62)
chisq.test(table2015, 2016)

Behavior Distribution

Here, I perform a simple chi-square test to validate that these data-points are in fact Skye’s typical behavior. The null hypothesis is that the expenditures in 2015 and 2016 are independent, or in simpler terms, we hypothesize the possibility that Skye’s behavior has changed and therefore has significantly changed his electrical consumption. Since the result presents a p-value of 0.2329 (much greater than 0.05,  a benchmark value to consider this null hypothesis), we reject the null hypothesis and conclude that there is evidence to suggest that Skye is behaving how he should be!

Although we have statistical evidence regarding his typical behavior, one still needs to question what happened in December 2016, where Skye’s expenditure increased by almost 37% from 2015! Could this probably be a result of one of the coldest winters that Lower Mainland has experienced in years?

Skye has more expenditure control in 2016. His net daily expenditures are more sporadic in 2015 with monthly averages between $0.80 and $1.40 per day, whereas 2016 was less sporadic with monthly averages between $0.70 and $1.20 (exception, December 2016 at average of $2.20).

2015

Daily Expenditure 2015

2016

Daily Expenditure 2016

Here, I use the term sporadic to describe the range in distributions of net daily expenditures per month.  For example, the box-plot ranges in the first quarter of 2015 are much wider than the box-plots in the first quarter of 2016. This is especially evident in the summer months. To put it simply, Skye has more consistency and better control of his electricity consumption and expenditures in 2016.

We have seen that Skye’s expenditure has increased by 3.42% from 2015 to 2016. One would think that with more controlled electricity consumption in 2016, his expenditure would be lower. By taking a look at December 2016, we can see that his expenditure is abnormally higher (by almost an additional $0.70 per day!) It is interesting to see that electricity consumption behavior was definitely more different in 2016 and kept at all-time low until the winter season.

Another thing to point out is that outlying value of about $4.00 in December 2016. Anecdotally, Skye says it is most likely because he forgot to turn off the stove that day!

Skye’s favourite days of electricity consumption are Tuesdays, Wednesdays, Saturdays and Sundays. Net hourly expenditure is seasonally and consistently higher (more expenditures are greater than $0.04 per hour) during these days.

Net Hourly Expenditure 2015 ($)

Monthly Daily Expenditure 2015

Net Hourly Expenditure 2016 ($)

Monthly Daily Expenditure 2016

Consistent with what we have been seeing, the winter months continue to observe the highest expenditure per hour. In addition, it seems that Skye has more of a liking to use higher levels of electricity on Tuesdays, Wednesdays and on the weekends with expenditures ranging between $0.03 to $0.09 per hour.

To account for a smaller difference, we could see that in 2015, Skye used more electricity throughout November everyday in 2015, but has decreased his hourly expenditure over November weekends in 2016.

Skye exhibits behavioral change in 2016 mornings and mid-evenings with higher net hourly expenditures compared to 2015.

Net Hourly Expenditure 2015 ($)

Daily Hour Charge 2015

Net Hourly Expenditure 2016 ($)

Daily Hour Charge 2016

It seems that Skye is consuming more electricity in the morning in 2016, evidently with an additional $0.01 to $0.02 per hour from 2015 rates. A behavioral change is signaled by the 7AM to 8AM mark in the morning and by the 6PM to 7PM mark in the evening. Even times like Mondays in 2016 at 11PM in the late night exhibit increase in electricity consumption.

Skye spends about 57% less than the average British Columbian household.

Compare Rates

According to BC Hydro, the average BC household consumes an average of about 900 kWh of electricity per month (not including seasonality). If we apply the exact same logic by taking the average consumption of Skye’s past 2 years, we see that he actually consumes way less than the average household.

Watt to Consider for the Future

This analysis was a great way to continue displaying fun data in Tableau. With just a two-column personalized electricity consumption data set, I was able to dig a little deeper on the spending behavior of Skye. Some things came to mind as I was conducting this analysis which could be used as motivation for further analysis posts.

This analysis serves as a perfect transition into utilizing machine learning methods to forecast future expenditures. We can definitely come to understand what exactly our forecasting machine learning models intend to capture and how these behaviors will help us predict future behavior. In this analysis, 2015 and 2016 data was used but in reality, data up to the current date and data before 2015 can be obtained. This gives more opportunity to build and test forecasting models accordingly.

In addition to the amazing visualizations produced by Tableau, a perfect consideration for future time-series modelling is to plot the data using ggplot2 in R. Interchangeably using these two visualization tools can serve as good practice and could provide more insights when used in conjunction with one another.

A special thanks to Skye for letting me use his data, it was fun!

 

My Personal Vancouver Transit Usage: Analysis using Tableau

One of the things I love about Vancouver is its public transportation system, Translink. I grew up loving trains, and so it only seemed natural that riding the Skytrain be one of the funnest things I have experienced when I first came to Vancouver. It has been around two years since I have moved here and I still use it to go everywhere in and out of the Vancouver area. A cool feature of the Translink system is the Compass Card, a re-loadable fare pass in which frequent riders will use to tap themselves on and off the transit system through fare gates. Part of the reason why I love the idea of tapping on and off the fare gates or on bus rides is because of how the system records data of where and when you have tapped.

The thought of Translink’s ability to easily conduct commuter analysis using the millions of data recorded everyday for strategic pricing and vehicle allocation is intriguing. As such, this is what motivates riders like me to analyze my personal rider behavior. Conveniently, the Compass Card website allows you to download your own personal .csv file. The file contains lines of transactions representing every single time you have tapped on and off the system.

The motivation behind this post is to showcase some data analysis. I would love to present what I have learned about my transit behavior between September 2016 and August 2017 using Tableau Public. For any Pokemon fans out there, visualizations take on a Charizard colour palette.

On average, I began my travels with the bus 2.7 times more than the train each month. Equivalently, the bus began 73% of my trips.

Rider Usage Growth

73 Percent Bus Usage

This makes sense as the bus begins my commute to almost everywhere I go when I begin at home. It is interesting to see that my ridership has consistently increased up until the second quarter in 2017. The slight kink in the graph is due to the fact that I spent most of the month of May 2017 travelling (I went to Japan for the first time!)

I used the transit system in 289 out of 365 days and most days, I took 2-3 trips.

Trip Data

Here, I defined a trip as one where I would be required to make a new full fare payment. It is possible that multiple forms of transit may be used within an hour and a half time interval before having to pay again. These potential ways of transferring between types of transit (ie. bus to a train) are not considered as trips.

I tried avoiding the morning transit rush. I am more likely to use transit during evening rush hour. Weekend usage often starts in the late morning.

Daily Trip Schedule

This huge morning spread in my transit usage behavior reflects my choice to go to the gym before I go to work, especially during the spring and summer seasons when it gets brighter outside earlier in the day. Therefore, I can begin using transit as early as 5:00am! Also, being given a flexible work schedule, sometimes I choose to head to work as late as anywhere between 8:00am and 9:00am.

I often go out Friday and Saturday evenings and as much as I love taking transit, there is no clear increase in transit usage behavior during this time because depending on the activity, I may already be in walking distance of what I want to do, or transit may not be my ideal form of transportation.

I saved money getting a Zone 1 Monthly Pass at $91.00 with my behavior! I would have spent on average, $103.00 a month on individual fares.

Fare Usage

Often times, I don’t think about how many times I tap on and off the system and overlook what I would be paying if I did not have a monthly pass. This is full proof that getting a zone 1 monthly pass is worth it as a frequent transit user and I do not have to worry about other financial alternatives.

If I was more nit-picky, I could definitely save more money by not getting the monthly pass during the months where it would not be worth it. For example, every December,  I fly out to Toronto for two weeks to visit family for the holidays. One might think that this could signal a behavioral change to pay closer attention to my budget allocation towards public transit. In reality, I actually prefer not having to worry about loading my compass card every month. Hence, I have it set to auto-load where the system automatically charges my credit card and loads a monthly pass to my compass card.

Further Considerations

This analysis was a great introductory way for me to explore Tableau as an analytical tool. I will definitely be using it more often to create vibrant visualizations and hone in on insights from interesting data. Some future considerations I have for these kinds of analysis is to utilize maps and locations to enhance the visualization and stories behind transit data. In this particular case, almost all of my trips began in Vancouver and rarely in any other surrounding city so geographic visuals may have not been much use.

Another future consideration is to augment the existing transit data with other data sources such as the distances traveled using transit possibly obtained from Google Maps for example. Some analysis on how much it would cost per kilometer traveled or personal summary statistics on distances traveled also sounds interesting.

Amidst the world of available data in everyday life, one last future consideration is that the next time you tap off the transit system, think about how that is one more data point for your next analysis!