Big Data And Analytics: Towards the reduction of power consumption by using Watson Analytics

Conclusion 14 References 14 Introduction The main objective of this projects is to understand concepts of power consumption drivers and to determine that high amount of electrical energy consumption from the coal fired plants. The research will be based on which combination of features highlight where efficiencies could be made in the energy consumption reduction and analyze the predictive model along with the discussion about demand on future energy use and CO2 gas emission. The accompanying are sets of highlights incorporated into the given informational collection: Adoption of sunlight based energy innovations Geographic attributes

BIG DATA AND
ANALYTICS

Table of Contents
1. Introduction.......................................................................................................................................1
1.1 Background of the Project........................................................................................................2
1.2 Scope...........................................................................................................................................2
2. Factors for energy consumption.......................................................................................................3
3. Predictive Analysis By using Watson Analytics..............................................................................3
4. Recommendations to Reduce Power Consumption.......................................................................14
5. Conclusion........................................................................................................................................14
References................................................................................................................................................14
1

1. Introduction
The main objective of this projects is to understand concepts of power consumption
drivers and to determine that high amount of electrical energy consumption from the coal fired
plants. As well as we need to examine the drivers of CO2. Throughout this project we are going
to research about solar cities project. The research will be based on which combination of
features highlight where efficiencies could be made in the energy consumption reduction and
analyze the predictive model along with the discussion about demand on future energy use and
CO2 gas emission. This predictive analysis will be done by using Watson Analytics. Then the
factors that contribute to power usage will be determined. To reduce the energy consumption and
CO2 emission, some recommendations will be provided.
1.1 Background of the Project
The Solar urban communities project was a task drove by the University of Ballarat,
(previous name of Federation University), which included the enlistment of family units and
organizations over the LoddonMallee and Grampians areas to screen changes in energy
utilization. The undertaking took a gander at various variables that could impact energy
utilization. These components were separated into sets of highlights, and estimations were taken
for each particular element. For instance, a factor could be identified with a home's development
materials. In which case an element could be "staying development compose" and an estimation
would be taken to decide the development write for each abode and put away in the
informational collection. For instance "abiding development compose" could contain the
qualities block, block facade and so forth... A large number of these highlights are incorporated
inside the given Solar Cities informational collection.
The accompanying are sets of highlights incorporated into the given informational
collection:
 Adoption of sunlight based energy innovations
 Geographic attributes
 Physical qualities of the abodes, including such things as the homes age, estimate,
number of stories, number of lights, protection and so on.
2

1.2 Scope
The primary goal of this project is to comprehend the drivers of energy utilization, and as
a huge level of electrical energy is made by coal terminated plants, at that point then again the
drivers of CO2.
2. Factors for energy consumption
Building structures can be utilized for an assortment of capacities: regulatory workplaces,
personnel workplaces, classrooms, labs for research and classes, nourishment administrations,
gathering rooms, ponder territories and on and on.We've assembled these utilizations into four
sorts that we call classrooms, labs, group, and workplaces. Each compose has anenergy profile.
For instance, workplaces, classrooms and group spaces by and large utilize less energy
contrasted with research centers since a portion of the air is recycled all through the building.
The distribution of air takes into consideration less molding (warming and cooling) of the air and
results in less energy being utilized (Balan & Otto, 2017).
Then again, building structures with lab spaces normally utilize a great deal of energy
since they regularly require significantly higher ventilation rates than an office, and the air can't
be recycled. The air coming into a lab must be 100% outside air (not recycled), and after that it
should totally leave the working through the fumes frameworks. Moving this amount of air with
fans, and warming and cooling the air, is anenergy serious process.
A portion of the elements that influence energy use on our rundown above are building
attributes that can't be changed, for example, the kind of development, age of the building and
outside air temperature. Factors, for example, the kind of development (e.g. solid, block,
surrounded dividers, and so on), windows and protection are influenced by the California Energy
Code. The California Energy Code was made in 1978 and a few more up to date forms have been
discharged from that point forward, each increasing present expectations for energy productivity
somewhat higher.
3. Predictive Analysis By using Watson Analytics
By predictive analysis, some ideas have provided for energy consumption in buildings
that are given below. The analysis has been done through IBM Watson analysis and visualization
tool. The predictive analysis helps us to plan future CO2 emission reduction in buildings.
3

The above chart shows that the contribution of power usage according to various
roof color throughout the year. Intermediate type roof color is contributed in high power usage
and unknown type is utilized least power.
The above shown chart describes the contribution of power usage over a year by the PV
capacity. In 2014, the usage is increased rather than other years.
What is the contribution of power usage over a year by PV_Capacity and Insulation? The
contribution of power usage over a year by both of PV_Caoacity and Insulation attributes are
estimated and shown in the above analysis chart.
4

Then the power usage is estimated according to the age group people. The people from
Sixty and over has consuming high power energy.
The highest power is consumed in January 2015 and the less amount of power is
consumed in November 2015.
Heywood, Portlant and Myamyn are top three stuburbs of power usage. It is estimated
and shown in the above chart.
5

The above chart shows that the value of PV capacity and it is compared by the estimated
age. The highest consumption of the power is consumed by the 20 to 29 age people.
The count of owned and rented houses were estimated. When comparing to Rented, the
owned houses count is high.
The highest power usage was done by Portland suburb. Then Heywood consumes the
second high power energy.
6

When analyzing the stories and by its structure, one story type of house is mostly
constructed rather than two story house.
The above shown chart describes that the windows type compared by the roof color and
the color is light. Single glaze window type is determined as maximum rather than the double
glaze window type.
7

The window type is compared by its roof color and chosen roof color is light. When
comparing to semidetached one, the house type has highest count.
The window type is compared with LED count throughout the year of 2012, 2013, 2014
and 2015. In 2014, the power usage is high rather than others.
The wall construction is estimated based on the age different age group people and the
window type. Brick type of wall is mostly constructed.
8

The values of bedrooms were compared by house structure and the estimated structure.
The people whose age is between 60 and 70 has used the highest number of house structure with
bedroom.
The value of LED count is estimated based on the roof colors such as dark, intermediate
and light. The light colored roof houses have used.
9

The above shown chart describes about the comparison of window type based on
the window coverings.
Insulating the rooftop, floor and dividers
The test in including protection levels in domestics is to pick the correct materials that fit
for reason. Understanding the properties of the materials and when it is suited to fitting in the
upper room space is basic (Bruns, Weller & Lewandowski, 2014)
. In addition, value, fire wellbeing, chemicals included and end of life ought to likewise
be considered while picking the material.
Floor
Phenolic froth protection would offer the best execution of any promptly accessible
board. It likewise has less effect on room measure where dry-covering is considered. Kingspan's
Kooltherm phenolic is the great alternative for floors. The composed U esteem for floor is 0.2
W/m2K.
Cavity divider protection
Filling the cavity hole between the inward and external squares with fitting protection
material has been considered. Nonetheless, it is exceedingly likely that protecting the pit alone
won't be sufficient to accomplish the required U-values. Thus, including inside or outside
protection ought to likewise be worried to accomplish the focused on U-esteem. The planned u
esteem for infused protection with outside divider protection (Rockwool) is 0.49 (W/m2K).
10

Roofs
Mineral fleece protection is very suggested for rooftop protection, 160 mm is included
amongst rafters and 100 mm underneath them. The planned U esteem for this situation is 0.15
(W/m2K).
Minimizing penetration
It is recommended that the first entryways on the property, with a U-estimation of 2.8
W/m2.K, ought to be supplanted with a superior triple coated entryway, diminishing the U-
estimation of the surface by 65%.
The above graph shows that the breakdown of LED count for each structure of building
such as house, semi detach one and attached house (Dutton, 2016).
Ventilation is required in conventional structures to enable the texture 'to relax'.
Smokestacks would have contributed enormously to the ventilation rate, so it is exhorted,
sometimes, that stacks could to be left open to permit regular ventilation. Be that as it may,
neglected stack, if left completely open, will regularly cause more warmth misfortune. It
additionally can give water access, on the off chance that it is uncapped. A few techniques can be
suggested:
Tops: a top at the highest point of a stack will avoid water entrance yet permit through
ventilation. Be that as it may, tops can cause tremendous harm and be extremely hazardous in the
event that they brush off if there should arise an occurrence of breezy climate.
Inflatables: is the fastest and least expensive approach to close of a pipe that isn't being
utilized, however it is unbalanced and messy while expelling and reinstalling them set up
(Feinleib, 2014).
11

Reducing the vitality utilization for lighting (LED)
The above graph shows that the LED count which is compared with storied type of
building.
Driven lighting is greatly vitality productive innovation and has changed the eventual fate
of lighting around the world. The private LED lighting use less 70% vitality and last 25 times
longer life. The estimation of lighting pick up in the SAP count (67) was lessened by 70% of the
first esteem (Hurwitz, Kaufman & Bowles, 2015).
Installing inexhaustible sources (PV board)
The above graph shows that the Fluor count and PV capacity of the buildings.
The sun oriented photovoltaic (PV) has numerous preferences that a householder can
profit by. The primary advantage is to cut power charge and additionally pitch the left finished
power to the framework. Also, the daylight is free and that implies almost zero carbon
impression (8). It is exhorted the Monocrystalline framework with 20% productivity is required
to create around 2700kWh yearly. This framework requires 21 square meter rooftop space and
makes sparing around 12p/kWh from power charge. Be that as it may, if a householder doesn't
utilize the power created, traded to matrix or store it in batteries are the main two choices
accessible. The capacity innovation has been overlooked in light of the fact that the cost and
12

intricacy with introducing at staying. The traded duty is 3.1 p/kWh, however this figure could be
disregarded as the power created by the framework would be consumed by the site request (Ibm
Redbooks., 2014).
UPVC triple coating windows
The above graph shows that the attribute SIZE_SQM is compared by Interval date of the
month.
The immense warming misfortune in domestics happens through windows. There is a
developing extent around there to enhance the warm execution of this component. Triple coating
is the best choices that could be considered to accomplish the decrease target. This component
has a fantastic U-esteem (one or less) which give a diminishing in vitality utilization and in
addition lessening CO2 discharges (Jackson, 2016).
Here are at present three primary CO2 catch approaches. The most ordinary approach is
to catch the CO2 from ignition items in control plant vent gas or modern fumes. This is known as
post combustion catch. Two different ways to deal with catching CO2 occur before non-
renewable energy source ignition. In the oxygen burning (as a rule called oxy-fuel ignition)
approach, O2 and reused vent gas is utilized to increment CO2 fixations in pipe gas before catch.
In the hydrogen/syngas approach, coal is gasified or flammable gas is transformed to deliver
blend gas (syngas) of carbon monoxide (CO) and H2; a water/CO move at that point happens to
create H2 and CO2 for CO2 catch. Both methodologies increment CO2 focuses in the fumes gas
stream making CO2 less demanding to catch. The catch step brings about the majority of the cost
of carbon catch and capacity forms. Subsequently, the principle challenges related with catching
CO2 are decreasing expenses and the measure of vitality required for catch.
13

4. Recommendations to Reduce Power Consumption
Lessening a building's carbon impression decreases its running expenses, enhances
worker confidence, raises property estimations and enhances LEED scores. Structures turn out to
be ecologically dependable, productive and more beneficial spots to live and work in. The
accompanying tips can help lessen a building's impression.
Begin early
Assess and measure a building plan's carbon impression as ahead of schedule in the
process as could reasonably be expected. This data is ending up progressively accessible through
ecological item assertions coordinate from makers.
HVAC
Since HVAC involves 40 percent of all carbon outflows, consolidating the most proficient
warming, ventilation and cooling frameworks, alongside effective tasks and booked upkeep of
such frameworks, diminishes carbon impression.
Reused content
Indicate reused content building and inside materials. Pick recyclable building materials that
have more positive impact on the earth. Bolster green providers and sellers that grasp green
practices. Metal building frameworks are the perfect item for maintainability and green as steel is
the most reused material on the planet (Mohanty, Jagadeesh & Srivatsa, 2013)
.
5. Conclusion
In this project we haveresearched the Solar Cities project. The research is based on which
combination of features highlight where efficiencies could be made in the energy consumption
reduction and analyzed the predictive model along with the discussion about demand on future
energy use and CO2 gas emission. This predictive analysis is done by using Watson Analytics.
Then the factors that contribute to power usage is determined. To reduce the energy consumption
and CO2 emission, some recommendations are provided.
References
Balan, S., & Otto, J. (2017). Business intelligence in healthcare with IBM Watson Content
Analytics. [San Bernardino, CA]: Shilpa Balab and Joseph Otto.
14

Bruns, A., Weller, K., & Lewandowski, D. (2014). Twitter data analytics. Bradford: Emerald
Group Publishing Limited.
Dutton, C. (2016). Learning Watson Analytics. [Carpinteria, Calif.]: Lynda.com.
Feinleib, D. (2014). Big Data Bootcamp. Berkeley, CA: Apress.
Hurwitz, J., Kaufman, M., & Bowles, A. (2015). Cognitive computing and big data analytics.
Indianapolis: John Wiley & Sons.
IBM Redbooks. (2014). Ibm watson content analytics. [Place of publication not identified]:
Vervante.
Jackson, S. (2016). Cult of analytics. London: Routledge.
Mohanty, S., Jagadeesh, M., & Srivatsa, H. (2013). Big Data Imperatives. Berkeley, CA: Apress.
15