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ITECH1103 Big Data And Analytics

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  • Course Code: ITECH1103
  • University: Federation University Australia
  • Country: Australia


Data set background:

The Solar cities project was a project led by the University of Ballarat, (former name of Federation University), which involved the recruitment of households and businesses across the Loddon Mallee and Grampians regions to monitor changes in energy consumption. The project looked at a number of factors that could influence energy consumption. These factors were broken up into sets of features, and measurements were taken for each respective feature. For example, a factor could be related to a dwelling's construction materials. In which case a feature could be “dwelling construction type” and a measurement would be taken to determine the construction type for each dwelling and stored in the data set. For example “dwelling construction type” could contain the values brick, brick veneer etc... Many of these features are included within the provided Solar Cities data set.

The following are sets of features included in the provided data set:

  • Adoption of solar energy technologies
  • Geographic characteristics
  • Physical characteristics of the dwellings, including such things as the dwellings age, size, number of stories, number of lights, insulation etc.

The main aim of this project is to understand the drivers of power consumption, and as a large percentage of electrical energy is created by coal fired plants, then conversely the drivers of CO2

Therefore two main questions that need to be asked as a researcher of the Solar Cities project are:

  1. Which combination of features highlight where efficiencies could be made in the reduction in energy consumption?
  2. What would you include in a predictive model that would explain the demand on future energy use and CO2emissions?

i.e. which features and their measurements contribute to either higher or lower energy consumption? (For a predictive model, Watson Analytics does have a rule based engine...)

You have been commissioned as a research assistant with the main aim to find the drivers of power usage, understand the factors that contribute to this and provide any useful and interesting insights, trends and patterns that could be presented to the stakeholders of this project. Your intended audience are the project manager and associated stakeholders.

Your findings will be presented to the project manager and the other stakeholders, where you will outline the factors that contribute to power usage and what your recommendations will be to reduce power consumption, given the current dwellings that exist in the given geographical locations in country Victoria (i.e found in the data set).

You are expected to present the data findings in visual forms (i.e., charts and graphs).

Some Starting Questions

  1. What is the contribution of power usage over a year by roof colour?
  1. What is the contribution of power usage over a year by PV_Capacity?
  1. What is the contribution of power usage over a year by PV_Capacity and Insulation?
  1. What is the power usage by estimated age?
  1. Over which months is the most power used?
  1. Over which months is the least power used?
  1. What are the top drivers of power usage?
  1. Which suburbs have the most houses with pv_capacity
  1. Which age houses are more likely to have pv_capacity?
  1. Are houses that are owned more likely to use less power than the ones that are rented?
  1. Which suburb dwellings use the most power?
  1. Do houses with larger square meterage use more power than smaller houses, also does double story make a difference?
  1. Which light types in dwellings use more power?
  1. Does having more lights of any type mean the house will use more power?
  1. What age houses have what type of wall construction?
  1. What age houses and from which areas and with how many bedrooms use the most power?
  1. Does Roof colour and roof material make difference to power consumption?
  1. Do dwellings that have double glazed windows and with window coverings use less power?

Task 1- Background information

Write a description of the selected dataset and its importance given the Solar Cities project, i.e. the Solar Cities project is attempting to understand power usage in dwellings, in order to then recommend how to improve the overall efficiency of power consumption in dwellings. Information must be appropriately referenced.

Task 2 – Reporting / Dashboards

For your project, perform the relevant data analysis tasks by

  • answering the above questions
  • finding new relevant questions
  • answering the two main questions

You should also identify the visualization and dashboards you need to develop to best communicate your findings to the stakeholders.

Task 3 – Research

Justify why these BI reporting solution/dashboards are chosen in Task 2 (Reporting / Dashboards) and why those data set features are present and laid out in the fashion you proposed (feel free to include all other relevant justifications).

Note: To ensure that you discuss this task properly, you must include visual samples of the reports you produce (i.e. the screenshots of the BI report/dashboard must be presented and explained in the written report; use ‘Snipping tool’), and also include any assumptions that you may have made about the analysis in your Task2.

Task4 – Recommendations for the project manager and project stake holders

Based on your BI analysis and the insights gained from the “Data Set”, given your analysis performed in previous tasks, make some logical recommendations to the stake holders. Justify the answers to the two main questions, and show where power consumption efficiencies could be made, and what future consumption will look like. Give 2 possible scenarios. Also, if you have a predictive model show which features and measurements lead to power reductions/increases, to add to your logical recommendations. Do this with the help of appropriate references from peer-reviewed sources.

Task 5 - The Reflection: Each Team member is expected to write a brief reflection about this project in terms of challenges, learning and contribution.

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Cite This Work

To export a reference to this article please select a referencing stye below:

My Assignment Help (2020) Big Data And Analytics [Online]. Available from:
[Accessed 21 January 2021].

My Assignment Help. 'Big Data And Analytics' (My Assignment Help, 2020) <> accessed 21 January 2021.

My Assignment Help. Big Data And Analytics [Internet]. My Assignment Help. 2020 [cited 21 January 2021]. Available from:

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