The current research would focus on brief assessment of the project data related to “Solar city” that the authority of “Ballarat University” has undertaken. The intention is to analyse the changes aroused to energy consumption. In order to accumulate data, the primary targets are business and household recruitments, which include Grampians and Loddon Mallee. Moreover, in this paper, numerous factors would be highlighted having direct effect on the conventional power consumption in various households falling under the Australian suburbs. This research paper aims to obtain a critical insight of the important power consumption drivers. This is because the energy sources that are not renewable have engendered electric energy, which leads to huge emission of detrimental gases like carbon dioxide. The visualisations related to technology of solar energy along with data analysis focus on certain features, which are enumerated briefly as follows:
For meeting the criteria, two research questions are needed to be taken into consideration. Hence, the tool that would be most suitable in this case is “IBM Watson Analytic” through which it is possible to assess the provided set of data.
According to the above figure, it could be observed that additional usage of power is consumed by the roof with “dark colour” in the years 2012 and 2013. Moreover, the room with “dark colour” has consumed lesser power in comparison to the intermediate colour roof in 2014, However, the consumption of both the items has been identical in 2015 (Campbell et al., 2015). The roof having “Light” colour has low usage of power and the coloured roof that is unknown has the least consumption of power. The overall power consumption was least in 2012.
The Photo Voltaic capacity having lowest capacity ranging between 0 and 960 has the maximum usage of power. On the contrary, the Photo Voltaic capacity having the uppermost photo voltaic capacity ranging above 960 leads to minimised usage of power. The maximum power usage could be observed in 2014 because the PV capacity is the lowest in the year. In 2012, considerable decline in usage of power could be observed during the year in each type of photo voltaic capacity.
At 0-960 capacity of PV with an insulation of 1, the maximum power is used in 2014. The usage of power is followed in 2013 as well as 2015, while Photo Voltaic capacity and insulation remains same. The power usage is least for high Photo Voltaic capacity with low insulation (Joshi et al., 2017).
In accordance with the above figure, it could be observed that the maximum power usage is made by the residents of the dwellings aging “Sixty years and above”, followed by “Twenty to twenty-nine”. The estimated age of the household between “0 years and 4 years” is observed to have the lowermost usage of power.
It is evident from the above figure that in July 2015, power is used at the maximum level.
The above figure clearly states that in February, the use of power is minimal.
According to the above figure, “Wall Construction” and “Suburb” types can explain the variability of the amount of power usage mostly and simultaneously, which is 28%. In case of “One driver” of usage of power, “Suburb” type could be deemed as the strongest predictor.
The suburb having maximum houses with capacity of PV capacity is the “Portland” Suburb followed by “Heywood” suburb.
PV_capacity could be observed in six conditions for the below stated age houses:
At 0-960 photo voltaic capacity while the projected age of the dwellings is forty and above.
The houses that are self-owned consume lower amount of power in comparison to those houses that are taken on rent.
Among all the dwellings, the dwellings of “Portland” suburb uses maximum amount of power and after this, comes the “Heywood” suburb.
It is clearly inherent from the above figure that the “Single-storeyed” properties consumer much lower power than the “Double-storeyed” properties. For “Double-storeyed” dwellings, the usage of power on an average is greater in the area have 199 square metres. Room of this particular size, consumes more energy for both storied dwellings.
Additional power is consumed by the “Halogen Lights” for greater frequencies.
Different kinds of lights could be observed from the above figure and they constitute of halogen, fluorescent, LED or incandescent, in which the amount of power consumed is high with the increase in the overall number of lights. The maximum usage of power could be observed in the households having 11 and 17 lights. The usage is not maximum for the highest number of lights that is 32.
The houses that are made up of “Brick” walls are the oldest. In addition to this, the houses that are made up of “Fibre”, “Timber” and “Weatherboard” have high estimated age as well.
The suburb of “Heywood” consumes the maximum amount of power in respect of their bedrooms (1 to 20) and estimated age “Sixty and above”, the significant power usage is observed for “Casterton”. In case of the same amount of bedrooms and estimated age 15 and 19 years, the maximum usage of power could be observed in “Heywood”.
The maximum consumption of power is obtained in case of “Concrete block” walls with “Dark” roof colour. Such consumption is followed by Timber-made walls and “Dark” roof colour.
On an average, the lower amount of power is utilised on the part of the double glaze windows having or without coverings like curtains or blinds in opposition to the tented or single glaze windows having coverings.
Dashboards are considered as essential tools, which could not be replaced at all for business management (Chen, Argentinis & Weber, 2016).
The roofs that have intermediate and dark colours use excess energy in contrast to the roofs having light colour. It is observed that the consumption of energy is much lower in other dwellings than the houses. If a particular house has more number of lights, more energy is consumed by the same in comparison to those houses having limited lights. In addition, the homes that are built with the help of intermediate materials and bricks are observed to use maximum amount of energy. The power consumption is lower of the dwellings of lesser estimated age (Chen, Argentinis & Weber, 2016). The other constructing materials apart from bricks and concrete walls causes lesser amount of power usage. Single storied rooms are more responsible for higher power consumption. The houses without window coverings causes lesser power usage.
Therefore, the housing age is an important predictive factor of the usage of power. The double driver related to power usage is estimated by the kinds of Wall Construction materials and types of suburbs. In this case, the sole predictive factor in the scenario is the type of suburb (Rajaraman, 2016). The single user “Suburb” mostly predicts the power usage while the power usage is moderately predicted by all he 14 variables with the strength 52%. “Wall construction” and “Suburb” jointly explains the variability of power usage by 30%. The power usage is maximum in 2014 and minimum in 2012. Hence, the years is also a significant factor of power usage. Low photo voltaic capacity and lower insulation causes a huge amount of power consumption.
In this case, information is provided in the initial dashboard, in which brick-developed houses, minimised capacity of PV, rooms having dark colour, higher lights and lower insulation result in additional consumption of energy. The other dwellings, in which the estimated age is above 30 years, are indicated with the help of the second dashboard, in which the estimated and the Suburb type, particularly Heywood and Portland, is the initial important driver of greater consumption of electric energy. Secondly, the roof having intermediate or dark colour and wall construction kinds like weatherboard or bricks cause waste of additional energy. Another important estimation related to consumption of energy is an important estimator of the type of Suburb. Finally, the spiral dashboard chart denotes that Wall Construction and Suburb type are two significant combined drivers and the Suburb type and dwelling follow them.
With the help of dashboard, it is possible to carry out grouping, accumulation, visualisation and organising of research project for gaining an insight of expectations, well-being and basic level actual situation (Tsoi et al., 2018). Therefore, dashboards help in effective indication like tools used for tracking metric and advanced solutions related to business intelligence of real time so that informed decisions could be undertaken to accomplish future targets (Kambatla et al., 2014).
Particularly, the buildings developed with the help of bricks and concrete blocks in “Heywood” and “Portland” suburbs use greater electric energy consumption. The officials involved in the project need to concentrate regarding the increasing consciousness of individuals in the regions. Moreover, the project team needs to recommend the individuals in redeveloping the older buildings to new ones, since the buildings having greater projected age results in higher consumption of electric energy. Such consumption of high energy has impact on environment in the form of emission of carbon dioxide (Vogel et al., 2014). Based on the above evaluation, it is to be borne in mind that the stakeholders and the project managers need to keep track of the rooms with dark colour having lower capacity of PV would use additional electric energy in near future. The light coloured roof without window coverings is preferable. Not only that, the dwellings with higher insulation of small size rooms are to be adopted by the concerned people.
From this study, it has been found that there are various causes and importance for consumption of electric energy of various suburbs. In addition, the report covers brief overview of the trend as well as consumption of energy year by year. As a result, adequate elaboration has been provided regarding the two formulated research questions. Moreover, the effective recommendations have been provided from the perspectives of the stakeholders as well as the project managers. The accumulation of data for evaluation has consumed plenty of time and it is a laborious task (Wakeling et al., 2015). This has been evaluated with the help of team analysis and officials’ contribution enabled in representing the actual scenario along with reasons of wastage of energy and heavy emission of carbon dioxide in a clear manner. The group evaluation has been strengthened further with the help of digital dashboards (Zakir, Seymour & Berg, 2015). Finally, IBM Watson Analytics is selected as the technique of data analysis for arriving at the final outcome.
References:
Campbell, K. M., Nonez, R. Y., Saha, B., & Zhong, X. S. (2015). U.S. Patent No. 8,957,908. Washington, DC: U.S. Patent and Trademark Office.
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.
Hoyt, R. E., Snider, D., Thompson, C., & Mantravadi, S. (2016). IBM Watson analytics: automating visualization, descriptive, and predictive statistics. JMIR public health and surveillance, 2(2).
Joshi, K., Masurkar, S., Tawde, A., & Gharat, J. (2017). Business Intelligence (BI) Dashboard Utility.
Kambatla, K., Kollias, G., Kumar, V., & Grama, A. (2014). Trends in big data analytics. Journal of Parallel and Distributed Computing, 74(7), 2561-2573.
Rajaraman, V. (2016). Big data analytics. Resonance, 21(8), 695-716.
Tsai, C. W., Lai, C. F., Chao, H. C., & Vasilakos, A. V. (2016). Big data analytics. In Big Data Technologies and Applications(pp. 13-52). Springer, Cham.
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.
Vogel, A., Hehn, D., Zietzschmann, M., Kunstein, A.M., Henkel, S., Gottlieb, S., Tsognov, A. & Maier, S., SAP, SE. (2014). Personalized dashboard architecture for displaying data display applications. U.S. Patent 8,713,446.
Wakeling, S., Clough, P. D., Wyper, J., & Balmain, A. (2015). Graph literacy and business intelligence: Investigating user understanding of dashboard data visualizations. Business Intelligence Journal, 20(4), 8-19.
Zakir, J., Seymour, T., & Berg, K. (2015). BIG DATA ANALYTICS. Issues in Information Systems, 16(2).
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