Our role: Data scientist consultancy firm
Target audience: Natural Resources Canada renewable energy planning committee
The growth and development of renewable energy sources as replacements for fossil fuels is an important aspect of combatting climate change. In order to develop Canada's national strategy for renewable energy production in Canada, it is important to understand the current situation and the history of existing wind power production in Canada. To accomplish this, we propose building a data visualization app that allows members of Natural Resources Canada planning committees to visually explore a dataset detailing existing windpower capacity in Canada. Our dashboard will show the different variables that contribute to the overall existing windpower capacity within Canada, and allow the user to filter on different variables to explore different aspects of the data.
We will be visualizing a dataset of approximately 6,500 existing wind power turbines in Canada. Each turbine entry has 13 different numerical and categorical variables that describe the location within Canada (province/territory, latitude, longitude), the project to which the turbine belongs (project name, total project capacity, number within project), and the turbine itself (turbine id, rated capacity, rotor diameter, hub height, manufacturer, model, and commissioning year). Using this data, we will be able to calculate new aggregated variables such as total power capacity per province, and yearly total cumulative turbine count.
Francois is a bureaucrat in Canada's Department of Natural Resources, who sits on a planning committee that is drafting policy regarding the direction of future developments of wind power projects within Canada. As part of his research, he wants to explore a dataset in order to find patterns in how wind power generation in Canada has changed over time, and how the different provinces and territories of Canada differ from each other in respect to wind power capacity. When Francois first visits our windpower visualization dashboard, he will see an overview of the available data on a national level, including change in wind power generation capacity over time, and the locations of existing wind power projects within Canada. He can filter the data by province to see which provinces have the most or the least wind power capacity, for example. He can also filter the data by turbine capacity ranges to see where the turbines with the largest power generation capacities are located within the country. Francois may notice that certain provinces have much greater wind power capacity than others, or that the largest turbines tend to be clustered in specific areas of the country. He might hypothesize that these clustered pockets of wind power development in the country could be correlated with areas of increased population density that have a greater energy demand, or geographic regions which have a typically windy climate that is suitable for wind power generation. Francois may decide that he needs to conduct a follow-up investigation that includes examination of demographic and climate information that is not included in the current data set.