Background

Discuss about the Required For Taking Decisions Regarding The Business.

We know that the analysis of different data sets is required for taking decisions regarding the business, management, etc. Now a day, industries and businesses generates a big data and analysis of these big data sets is required for understanding the characteristics of the production or service. For the analysis of these types of big data sets, we need to use different statistical tools and techniques for the analysis. It becomes necessary to analyse the data from different industries for making effective decisions. Also, this data analysis provides the proper estimates for future use. Here, we have to analyse one such a big data set by using the IBM Watson analytics tool. This data set is related to power use or energy consumption by different types of users. Statistical data analysis plays an important role in this new era of businesses and industries. It is important to use different statistical software’s for the analysis of big data. For optimization of the energy use, we need to implement several things such as use of efficient and modified electrical machines, use of CFL lights, etc. For the reduction in CO2 emissions Coal energy consumption should be minimized, because coal energy consumption produce CO2 emissions in a large proportion. The predictive model for the future energy use and CO2 emissions should include the nuclear energy, wind energy, solar energy, biomass energy, etc.

The Federation University conduct a Solar Cities project for study of consumption of energy. This project involved the recruitment of the different households and businesses across the Loddon Mallee and Grampians regions. During this research study, changes in energy consumption were monitored by the researchers. Researchers find out all related factors which affects the energy consumption. Researchers also find out the relationship exists between the energy consumption and different variables that could influence energy consumption. These possible factors were divided into set of their features. Then researchers were taken the measurements for these factors. Given data set includes the sets of features such as 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 goal of this research study or project is to understand the drivers of power consumption, For this research study, researchers wants to find out the combination of features which could useful in the reduction of energy consumption. Also, researchers want to predict the model for future demand of energy use and CO₂ emissions. Here, we have to study different patterns of energy uses and CO₂ emissions for the given data set. Also, we will develop a predictive model for future energy use by using the IBM Watson Analytics tool. We have to analyse entire data set by using IBM Watson Analytics tool and then we have to make some discoveries. We have to study any useful facts from this data set, interesting insights, trends, and patterns regarding the energy consumption.

Dashboard/Report

In this section we have to analyse the given big data set by using IBM Watson Analytics tool. Given data set for the energy consumption have different variables and the list of these variables is summarised as below:

Victorian Suburb names of the houses chosen for the study

Possible values: Portland, Narrawong, Heywood, Tyrendarra, Sandford, Digby, Myamyn, Condah, Casterton, Heathmeare, Drumborg, Allestree, Bolwarra, Nelson, Bahgallah, Heathmere, Dartmoo

Data type is text.

The status of the property in terms of how people are living in it

Possible values: “OWNED, RENTED, MORTGAGED, OTHER, RENT_FREE, LIFE_TENURE, UNKNOWN”

Estimated Age

The estimated age of the property as ordinal categorical intervals

The type of material and/or construction type used for the dwelling walls

List of remaining variables that are included in this research study is given as below:

Field Name	Data Type	Definition
ROOF_COLOUR	Text	The color of the roof, to test the absorption or reflection of the sun
STORIES	Integer	A count of the number of stories that the dwelling has, only 1 or 2 story dwellings recorded in this study
BEDROOMS	Integer	A count of the number of Bedrooms that the dwelling has. 99 signifies a missing count.
BATHROOMS	Integer	A count of the number of Bathrooms that the dwelling has. 99 signifies a missing count.
LIVING_ROOMS	Integer	A count of the number of Living rooms that the dwelling has. 99 signifies a missing count.
SIZE_SQM	Integer	An approximate size area of the dwelling, in 6 different sizes. 0 signifies a missing measurement.
WINDOW_TYPE	Text	The physical structure of the glass
WINDOW_COVERINGS	Text	What type of covering over the windows if any
STRUCTURE	Text	What type of dwelling it is
CFL COUNT	Integer	Number of compact fluorescent lamps
HALOGEN_COUNT	Integer	Number of Halogen lights in the dwelling
LED_COUNT	Integer	Number of LED lights in the dwelling
INCANDESCENT_COUNT	Integer	Number of Incandescent lights in the dwelling
FLUOR_COUNT	Integer	Number of fluorscent lights in the dwelling
INSULATION	integer	Where the insulation is situated 0 no insulation or unknown 1 ceiling only 2 wall and ceiling 3 wall, ceiling and floor
PV_CAPACITY		The amount of power being created by solar PV panels.
INTERVAL_DATE	TEXT	Date of power meter reading, collected daily
POWER_USAGE	Decimal	Amount of power being consumed on the given day

Now, we have to analyse this data set by using the IBM Watson Analytics tool. By using this tool, some of the discoveries were made which are presented below:

First of all we have to discover the top CFL count by the estimated age and analysis is given as below:

From this analysis, it is observed that the CFL count for the estimated age of sixty and over is highest and it is given as 190k. Bar graph for the CFL count by month indicates that the highest CFL count is noted in the month of October, median CFL count is observed in the month of March, while lowest CFL count is observed in the month of November. It is observed that there is a 55% growth in the CFL count from the year 2012 to year 2015. This means, the energy consumption is increasing rapidly and it is important to use other sources of energy such as solar, wind, etc.

Now, we have to analyse the given data set for the CFL count by the roof colour. The IBM Watson discovery is given as below:

It is observed that 2458 is the lowest total bathrooms by estimated age fifteen to nineteen. The top CFL count is observed for the Dark colour. The highest total flour count is observed in the month of October and it is given as 22.8k.

From this discovery or research study, it is observed that the Halogen count is observed highest in the month of October and it is observed lowest in the month of February. The median Halogen count is observed for the month of March.

The IBM Watson discovery for the top CFL count by estimated age

Now, we have to see the contribution of the power usage over the given years by a roof colour. The IBM Watson discovery for this analysis is summarised as below:

From above IBM Watson output, it is observed that the power usage is increasing from the year 2012 to year 2014 and again it decreases from year 2014 to year 2015. This means, after the year 2014, there is a significant decrement is observed in the power usage.

Now, we have to see the relationship exists between the LED count and CFL count by the year. Required output is given as below:

From above output, it is observed that there is some linear relationship exists between the LED count and CFL count.

Now, we have to see the predictive model for the CFL count by using IBM Watson analytics tool. Required output for this predictive model is given as below:

From the above output, it is observed that the predictive value for CFL count is varies as per the different values for the different suburban, size, incandescent count, living rooms, etc. So, these factors are important in prediction of the CFL count.

There are so many combinations of features available for the reduction in energy consumption. The first main combination of features is to use of efficient and modified electrical machines which consume low energy. Also, use of CFL, LED will be helpful in reducing power consumption. It is important to take significant actions for reduction in energy consumption. There would be list of several do’s and don’ts for the reduction in energy consumption. For example, one may suggest a low use of AC in the rainy or winter season. The local authorities and different government organizations should take proper actions for the awareness of people regarding the low energy consumption. There are several factors which would explain the demand on future energy use and CO2 emissions. For optimization of the energy use, we need to implement several things such as use of efficient and modified electrical machines, use of CFL lights, etc. For the reduction in CO2 emissions Coal energy consumption should be minimized, because coal energy consumption produce CO2 emissions in a large proportion. The predictive model for the future energy use and CO2 emissions should include the nuclear energy, wind energy, solar energy, biomass energy, etc. If proper precautions were taken, then use of nuclear power is a better alternative for the coal energy or other forms of energy. If nuclear power plants will be used with proper care, then there is a possibility of reduction in CO2 emissions. Due to previous accidents with nuclear power plants, peoples are not in favour of these nuclear plants. Several factors are needed to predict the future energy use and CO2 emissions. The significance of these factors should be test and if factor found significant, then it would be include in the predictive model.

The IBM Watson discovery for the top CFL count by roof colour

In this section, we have to analyse different results from the IBM Watson Data analysis tool. We analyse the power consumption data set by using IBM Watson and finds out different discoveries. We analyse different variables by year, count, consumption, etc. some of the research points from this research study are summarised as below:

1. It is observed that the CFL count for the estimated age of sixty and over is highest and it is given as 190k.
2. From this IBM Watson Data analysis, it is observed that the highest CFL count is noted in the month of October, median CFL count is observed in the month of March, while lowest CFL count is observed in the month of November.
3. It is observed that there is a 55% growth in the CFL count from the year 2012 to year 2015. This means, the energy consumption is increasing rapidly and it is important to use other sources of energy such as solar, wind, etc.
4. It is observed that 2458 is the lowest total bathrooms by estimated age fifteen to nineteen. The top CFL count is observed for the Dark colour. The highest total flour count is observed in the month of October and it is given as 22.8k.
5. It is observed that the Halogen count is observed highest in the month of October and it is observed lowest in the month of February. The median Halogen count is observed for the month of March.
6. It is observed that the power usage is increasing from the year 2012 to year 2014 and again it decreases from year 2014 to year 2015. This means, after the year 2014, there is a significant decrement is observed in the power usage.
7. It is observed that there is some linear relationship exists between the LED count and CFL count.
8. It is observed that the predictive value for CFL count is varies as per the different values for the different suburban, size, incandescent count, living rooms, etc.

From this research study, most important recommendations are summarised as below:

1. The use of energy is increasing day by day and therefore it is important to start use of alternative energy sources such as solar energy, wind energy, etc.
2. Coal energy consumption should be minimized for avoidance of CO₂
3. Power consumption would be minimized by using instruments with less energy consumption such as CFL, LED, etc.
4. Most of the electrical instruments should be optimized for energy use.
5. Some changes in infrastructure would help in minimizing power consumption.

From this research study for the big data set, it is reflected that the use of energy is increasing continuously and also demand for energy use is continuously increasing. For overcoming these increasing demands of energy, it is required to use some other sources of energy such as solar energy, wind energy, etc. These alternative sources of energy will help in reduction in the environmental pollution. The level of CO₂ emissions will be decreasing if we use eco-friendly sources of energy. Also, we can reduce the energy use by improving electrical instruments. Now a day there is tremendous changes in the electrical gadgets and instruments and these instruments are working on low energy. From this study, it is reflected that the CFL count for the estimated age of sixty and over is highest and it is given as 190k. Also, it is reflected that the highest CFL count is noted in the month of October, median CFL count is observed in the month of March, while lowest CFL count is observed in the month of November. It is observed that there is a 55% growth in the CFL count from the year 2012 to year 2015. This means, the energy consumption is increasing rapidly and it is important to use other sources of energy such as solar, wind, etc.

There are so many combinations of features available for the reduction in energy consumption. The first main combination of features is to use of efficient and modified electrical machines which consume low energy. Also, use of CFL, LED will be helpful in reducing power consumption. It is important to take significant actions for reduction in energy consumption. There would be list of several do’s and don’ts for the reduction in energy consumption. For example, one may suggest a low use of AC in the rainy or winter season. The local authorities and different government organizations should take proper actions for the awareness of people regarding the low energy consumption.

There are several factors which would explain the demand on future energy use and CO₂ emissions. For optimization of the energy use, we need to implement several things such as use of efficient and modified electrical machines, use of CFL lights, etc. For the reduction in CO₂ emissions Coal energy consumption should be minimized, because coal energy consumption produce CO₂ emissions in a large proportion. The predictive model for the future energy use and CO₂ emissions should include the nuclear energy, wind energy, solar energy, biomass energy, etc. If proper precautions were taken, then use of nuclear power is a better alternative for the coal energy or other forms of energy. If nuclear power plants will be used with proper care, then there is a possibility of reduction in CO₂ emissions. Due to previous accidents with nuclear power plants, peoples are not in favour of these nuclear plants. Several factors are needed to predict the future energy use and CO₂ emissions. The significance of these factors should be test and if factor found significant, then it would be include in the predictive model.

References

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