In order to carry out the analysis on the solar cities project, the data set defined under the Solar cities database, generated by Ballarat University, has been considered. The provided database is comprised of various elements that have direct or indirect impact on the process of consumption of energy by individual users based on various factors. The mentioned factors are consisted of approximate user age, the type of construction of wall, kinds of houses, the colour of the roof, bedroom quantity, construction of wall and many more. Moreover, cfl_count, halogen_count, pv_capacity, incandescent_count, fluor_count, led_count, insulation, interval_date, power_usage is significant too for identifying the type of consumption on energy alterations in the Grampians region and Loddon Mallee region.
The IBM Watson data analytics allows uploading data sets and determining results of below queries. These queries are represented through various graphs.
contribution of power usage over a year by roof colour
The graph represents that the least power sonication occurred in the year 2012. The most consumption of power has occurred in 2014. In 2014 only, the power consumption has crossed 140k. The analysis shows that the intermediate and dark roof colours have great impact on consumption of energy.
Contribution of power usage over a year by PV capacity
Through the graph it can be illustrated that the top drivers of the PV_CAPACITY are Flour_Count and LED_Count, and CFL_Count and LED_Count and SUBURB.The mist PVP capacity is in the year 2014.
Contribution of Power usage over a year by PV_Capacity and Insulation
After analysing the power usage based on Insulation and PV capacity, the following diagram has been discovered.
Power usage by estimated age
The breakdown regarding usage of power based on different aged persons has been shown in the above diagram. The diagram clearly entails that sixty and over sixty aged persons have used power more than any other aged groups.
Months with most energy consumption
The most energy composition has been occurred in the month of August in 2012. The unit of consumed energy is 28802.64 units.
Months in which least power used
Through analysing the energy consumption values, the above graph has been generated. The least energy consumption occur in February, 2012. The units of power consumed was 5595.71 units.
WALL_CONSTRUCTION and SUBURB (28%)
SIZE_SQM and SUBURB (27%)
BEDROOMS and SUBURB (27%)
INCANDESCENT_COUNT and SUBURB (27%)
FLUOR_COUNT and SUBURB (27%)
ESTIMATED_AGE and SUBURB (27%)
FLUOR_COUNT and ESTIMATED_AGE (26%)
CFL_COUNT and WALL_CONSTRUCTION (25%)
CFL_COUNT and ESTIMATED_AGE. (25%)
The LED_Count and SUBURB has the majority with 65% strength.
The PV_CPACITY in terms of houses has been shown the above diagram. The diagram shows the twenty to twenty-nine has majority in PV_CAPACITY.
Power usage by owned and rented Houses in the Given dataset
The above diagram shows the usage of power for rented, mortgaged, owned and many more house types. The power usage by owned houses is way more than others.
suburb dwellings that uses most power
From the analysis we found that, the dwellings in the Portland uses most power in the given data set.
Power usage by the size of the house
The above graph deceits that the smaller houses which belongs under 199 Square meter consume more power in comparison with greater SQM houses. The small houses consume 650889.28 units power in total.
Wall construction type with the age of the houses
The most common element in the wall construction in the houses regardless of age of house is Brick.
What age houses and from which areas and with how many bedrooms use the most power
Numerically the total power usage is summed up to 119096.38 units
Relation between power usage and roof colour and roof material
Utilization of power by the houses that have double glazed windows and window coverings
Regarding the project of data analytics, The IBM Watson tools has been used. This tool is very assisting in terms of understating the organizational data. The cognitive computing is also very helpful in supporting the decisions. The decision makers put this computing processing a very high place. The cognitive computing also improves taking decisions based on available data (Chen,Argentinis & Weber, 2016).
The prime drivers in power usage are as following.
WALL_CONSTRUCTION and SUBURB
INCANDESCENT_COUNT and SUBURB
FLUOR_COUNT and SUBURB
ESTIMATED_AGE and SUBURB
In order to modify the power consumption efficiency, it will be crucial to modify the wall construction component. The colours of the roofs also has great impact on the power consumption.
Working on the IBM Watson analytics is a complex task. The charts and graphs shows the perfect visualization of data sets if the factors and conditions are chosen properly.
References
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Chen, Y., Argentinis, J. E., & Weber, G. (2016). IBM Watson: how cognitive computing can be applied to big data challenges in life sciences research. Clinical therapeutics, 38(4), 688-701.
Derico, A., Leader, A. C., Kather, R., Engineer, O. S. A., & West, D. (2017). Make Data Simple: IBM Watson Data Platform & Data Science Experience.
Devarakonda, M., & Tsou, C. H. (2015, January). Automated Problem List Generation from Electronic Medical Records in IBM Watson. In AAAI (pp. 3942-3947).
Diamond, M., & Mattia, A. (2017). Data Visualization: An Exploratory Study into the Software Tools Used by Businesses. Journal of Instructional Pedagogies, 18.
Gao, T., Dontcheva, M., Adar, E., Liu, Z., & Karahalios, K. G. (2015, November). Datatone: Managing ambiguity in natural language interfaces for data visualization. In Proceedings of the 28th Annual ACM Symposium on User Interface Software & Technology (pp. 489-500). ACM.
High, R. (2012). The era of cognitive systems: An inside look at IBM Watson and how it works. IBM Corporation, Redbooks.
Lak, P., Kavaklioglu, C., Sadat, M., Petitclerc, M., Wills, G., Miranskyy, A., & Bener, A. B. (2017, November). A probabilistic approach for modelling user preferences in recommender systems: a case study on IBM watson analytics. In Proceedings of the 27th Annual International Conference on Computer Science and Software Engineering (pp. 38-47). IBM Corp..
Mylopoulos, J. (2017, November). Goal-Oriented Regulatory Intelligence: How Can Watson Analytics Help?. In Conceptual Modeling: 36th International Conference, ER 2017, Valencia, Spain, November 6–9, 2017, Proceedings (Vol. 10650, p. 77). Springer.
Tsoi, K. K., Chan, F. C., Hirai, H. W., Keung, G. K., Kuo, Y. H., Tai, S., & Meng, H. M. (2018). Data Visualization with IBM Watson Analytics for Global Cancer Trends Comparison from World Health Organization. International Journal of Healthcare Information Systems and Informatics (IJHISI), 13(1), 45-54.
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