International Journal of Innovative Research in Computer and Communication Engineering

International Journal of Innovative Research in Computer and Communication Engineering

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ISSN ONLINE(2320-9801) PRINT (2320-9798)

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Electric Power Energy Distribution Estimate using Visualization Technique

Olagunju, Mukaila1, Jimoh, R. G.2
  1. Department of Computer Science, Kwara State Polytechnic, Ilorin, Nigeria
  2. Department Of Computer Science, University of Ilorin, Ilorin, Nigeria
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Abstract

The power energy distribution process is one of the great challenges, which both the consumers and power energy distributors normally face. Visualization has been recommended as useful approach to solve this problem. MATLAB is used to showcase the visualization via analysis of the data of energy production for the month of December 2012 and this was used to predict the energy estimate for each day in the month of January 2013.With the patterns generated, the pattern of energy distribution can easily be modeled. Such will assist in proper planning, sharing and management of energy to sustain the national need.

Keywords
Energy distribution, Visualization, MATLAB, Energy production

INTRODUCTION
Electric energy is energy newly derived from electrical potential energy. It is sometimes loosely used to describe energy absorbed or delivered by an electrical circuit for instance, one provided by an electrical power utility. Electrical energy refers to energy which has been converted from electrical potential energy. This energy is supplied by the combination of electric current and electrical potential that is delivered by the circuit. At the point that this electrical potential energy has been converted to another type of energy, it ceases to be electrical potential energy. Thus, all electrical energy is potential energy before it is delivered to the end-user. Once converted from potential energy, electrical energy can always be described as another type of energy i.e heat, light, motion, etc (Buchmann, 2008).
Power estimation generally refers to the problem of estimating the average power dissipation of a digital circuit. This is different from estimating the worst case instantaneous power often referred to as the voltage drop problem (Chowdhury & Barkatullah ,1990; Kriplani, Najm & Hajj, 1992. Chip heating and temperature are directly related to the average power.
It has been revealed that the most straight tforward method of power estimation is simulation with emphasis on circuit simulation of the design to monitor the power supply using current waveform. Subsequently, the average of the current waveform is computed and used to provide the average power; the advantage of this technique is its accuracy. It can be used to estimate the power of any circuit regardless of technology design style, functionality or architecture. The simulation results however are directly related to the specific input signals used to drive the simulator. Furthermore, complete and specific information about the input signals is required in the form of voltage waveforms. The researchers hereby describe these simulation-based techniques as being strong and pattern dependent.
Farid (2007) suggested that there are many ways of estimating the electrical power in which simulation one of them. Electric energy is very important in human life for home usage, industrial usage and many others. as a result, it is very important to know the patterns by which the electric power distribution is taken place which is the major focus of the paper. For estimation to be more meaningful, MATLAB program was used for analyzing the data in form of 2D visualization. Visualization according to Turfle (2001) is the systematic and visual display of information in the form of tables, diagrams and graphs.

MATERIALS AND METHODS
The material used in this work is the hourly power distribution for the month of December 2012 as shown in Table 1.1 while MATLAB is used to analyze the energy distribution for the month of December 2012 in order to forecast the distribution for everyday in the month of January 2013.

RESULTS AND DISCUSSION
The analysis to predict the daily power energy estimate for the month of January is given below ranging from figures 1.1 to 1.32. Figure 1.1 above, shows the power energy distribution estimate for the month of January
Figure 1.2 above, shows the energy distribution estimate for 1st Of January, 2013. The 18hr has the highest energy estimate while the lowest energy estimate is between the 7hr and 11th hour. Figure 1.3 above, shows the energy distribution estimate for 2nd of January, 2013. The 5th hour has the highest energy estimate while the lowest energy estimate is 21st hour.
Figure 1.4 above, shows the energy distribution estimate for 3rd of January, 2013. The 15th hour has the highest energy estimate while the lowest energy estimate is 21st hour. Figure 1.5 above shows the energy distribution estimate for 4th of January, 2013. The 18th hour has the highest energy estimate while the lowest energy estimate is 9th hour.
Figure 1.6 above shows the energy distribution estimate for 5th of January, 2013. The 2nd hour has the highest energy estimate while the lowest energy estimate is 6th hour. Figure 1.7 above shows the energy distribution estimate for 6th of January, 2013. The 14th hour has the highest energy estimate while the lowest energy estimate is 23rd hour.
Figure 1.8 above shows the energy distribution estimate for 7th of January, 2013. The 2nd hour, 16th hour and2 4th hour has the highest energy estimate while the lowest energy estimate is 4th hour. Figure 1.9 above shows the energy distribution estimate for 8th of January, 2013. The 24th hour has the highest energy estimate while the lowest energy estimate is 11th hour.
Figure 1.10 above shows the energy distribution estimate for 9th of January, 2013. The 6th hour has the highest energy estimate while the lowest energy estimate is 8th hour and 11th hour respectively. Figure 1.11 above shows the energy distribution estimate for 10th of January, 2013. The 4th hour and 14th hour has the highest energy estimate while the lowest energy estimate is 12th and 16th hour respectively.
Figure 1.12 above, shows the power energy distribution estimate for 11th of January, 2013. The 19th hour has the highest energy estimate while the lowest energy estimate is 9th hour. Figure 1.13 above shows the power energy distribution estimate for 12th of January, 2013. The 5hr mark has the highest energy estimate while the lowest energy estimate is 21st hour.
Figure 1.16 above, shows the power energy distribution estimate for 15th of January, 2013. The 1st hour mark has the highest energy estimate while the lowest energy estimate is 6th hour. Figure 1.17 above, shows the power energy distribution estimate for 16th of January, 2013. The 14th hour has the highest energy estimate while the lowest energy estimate is 23rd hour.
Figure 1.18 above shows the energy distribution estimate for 17th of January, 2013. The 1st hour, 16th hour and 24th hour has the highest energy estimate while the lowest energy estimate is 4th hour. Figure 1.19 above, shows the power energy distribution estimate for 18th of January, 2013. The 24th hour has the highest energy estimate while the lowest energy estimate is 11th hour.
Figure 1.20 above shows the energy distribution estimate for 19th of January, 2013. The 6th hour mark has the highest energy estimate while the lowest energy estimate is 8hr and 11th hour. Figure 1.21 above, shows the power energy distribution estimate for 20th of January, 2013. The 4th hour and14th hour has the highest energy estimate while the lowest energy estimate is 12th hour and 16th hour.
Figure 1.22 above shows the energy distribution estimate for 21st of January, 2013. The 18th hour has the highest energy estimate while the lowest energy estimate is 9th hour. Figure 1.23 above, shows the energy distribution estimate for 22nd of January, 2013. The 5th hour has the highest energy estimate while the lowest energy estimate is 21st hour
Figure 1.24 above shows the energy distribution estimate for 23rd of January, 2013. The 15th hour has the highest energy estimate while the lowest energy estimate is 21st hour. Figure 1.25 above shows the energy distribution estimate for 24th of January, 2013. The 18th hour has the highest energy estimate while the lowest energy estimate is 9th hour.
Figure 1.26 above, shows the power energy distribution estimate for 25th of January, 2013. The 2nd hour has the highest energy estimate while the lowest energy estimate is 24th hour. Figure 1.27 above shows the power energy distribution estimate for 26th of January, 2013. The 14th hour has the highest energy estimate while the lowest energy estimate is 23rd hour. Figure 1.28 above shows the energy distribution estimate for 27th of January, 2013. The 1st hour, 16th hour and 24th hour has the highest energy estimate while the lowest energy estimate is 4hr mark. Figure 1.29 above shows the power energy distribution estimate for 28th of January, 2013. The 24th hour mark has the highest energy estimate while the lowest energy estimate is 11th hour.
Figure 1.30 above shows the energy distribution estimate for 29th of January, 2013. The 6th hour has the highest energy estimate while the lowest energy estimate is 8th hour and 11th hour. Figure 1.31 above shows the power energy distribution estimate for 30th of January, 2013. The 4th and 14th hour has the highest energy estimate while the lowest energy estimate is 12th hour and 16th hour.
Figure 1.32 above, shows the power energy distribution estimate for 31st of January, 2013. The 16th hour has the highest energy estimate while the lowest energy estimate is 7th hour.

CONLUSION AND RECOMMENDATION
With the importance of electrical energy distribution, a proper way must be channeled to determine the patterns of energy distributions so as to ensure efficient planning and management of the scarce resources. From the findings, it can be observed that one cannot say one time is the peak or lowest as the supply varies from time to time. It is therefore concluded that estimation should be a continuous process.
The recommendations are as follows:
1. This type of program can be used to know the particular hour that energy is much needed
2. It can be used for proper planning in order to avoid overloading of energy distribution and dropping of energy supply.
3. It gives a summary report for all the energy distribution within the day.

Figures at a glance
Figure 1 Figure 2 Figure 3 Figure 4 Figure 5
Figure 1 Figure 2 Figure 3 Figure 4 Figure 5
Figure 1 Figure 2 Figure 3 Figure 4 Figure 5
Figure 6 Figure 7 Figure 8 Figure 9 Figure 10
Figure 1 Figure 2 Figure 3 Figure 4 Figure 5
Figure 11 Figure 12 Figure 13 Figure 14 Figure 15
Figure 1 Figure 2 Figure 3
Figure 16 Figure 17 Figure 18

References