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FORECASTING ENERGY DEMAND A MEANS FORTHE IMPLEMENTATION OF POWER

INFRASTRUCTURAL MASTER PLAN USINGNEURAL NETWORK

B. I. Gwaivangmin

To cite this version:B. I. Gwaivangmin. FORECASTING ENERGY DEMAND A MEANS FOR THE IMPLE-MENTATION OF POWER INFRASTRUCTURAL MASTER PLAN USING NEURAL NET-WORK. Continental Journal of Engineering Sciences, Wilolud Journals, 2016, 11 (2), pp.12 - 31.�10.5707/cjengsci.2016.11.2.12.31�. �hal-01425948�

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Continental J. Engineering Sciences 11 (2): 12 - 31, 2016 ISSN: 2141 – 4068 © Wilolud Journals, 2016 http://www.wiloludjournal.com Printed in Nigeria doi:10.5707/cjengsci.2016.11.2.12.31

RESEARCH ARTICLE

FORECASTING ENERGY DEMAND A MEANS FOR THE IMPLEMENTATION OF POWER INFRASTRUCTURAL MASTER PLAN USING NEURAL NETWORK

B.I Gwaivangmin

Directorate of Physical Facilities, University of Jos, Jos.

ABSTRACT The increasing global demand for electrical energy coupled with rise in cost of energy sources necessitates development of intelligent forecasting methods and algorithms. Underestimation of consumption would lead to potential power outages that are devastating to life and the economy, whereas overestimation would lead to unnecessary idle capacity resulting in wastage of financial resources. Therefore, the importance of accurate estimation of energy consumption cannot be over emphasized. Proper Forecast of energy consumption is key to successful planning and policy formulation/implementation. Modelling of energy consumption is usually based on historical consumption. The challenges of energy production and consumption have made energy modelling a subject of widespread interest among engineers and scientists concerned. This paper looks at the infrastructural development of 11KV dedicated feeder of the University of Jos which was provided to check the insufficient and epileptic power supply suffered by the University community for many years and the need for proper sustainability of the network through forecasting future energy demand. The study looks at the past power supply situation before and after construction of the dedicated line. Artificial Neural Network (ANN) has been used to forecast the future energy demand of the University. KEYWORDS: Demand, forecasting, Modelling, Dedicated, Sustainability. Received for Publication: 27/07/16 Accepted for Publication: 11/10/16 Corresponding Author: [email protected]


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B.I Gwaivangmin: Continental J. Engineering Sciences 11 (2): 12 - 31, 2016

INTRODUCTION Electrical energy demand is growing rapidly globally and the growth is expected to continue, especially in the developing countries. This increase is not unconnected with rising incomes, urbanisation and most importantly industrialization which will no doubt result into many years of high growth in electricity consumption. Global power generation as at 2012 was 22,504.3 TWh, with the U.S. generating 18.91%of total global output. According to (GEI, 2014) the annual electricity demand for the world is projected to increase up to 53.6TWh (Jazz Scenario) and 47.9 TWh (Symphony Scenario) by 2050 compared to 22TWh in 2011. This implies investment requirements in electricity generation between US$25billion (Jazz Scenario) and US$19billion (Symphony Scenario). If the history is anything to go by, the demand will grow even faster than expected in the high growth Scenarios as for example the total global electricity production increased from 6,100TWh in 1973 to 13,200TWh in 1995(~ 46% over 22 years) and from 13,200TWh to 22,200TWh in 2011(~ 60 % over 16 years)”. Guaranteeing access to sustainable electricity for the greatest possible number of people is one of the biggest challenges of our time”. World Watch Institute, WWI (2015) pointed out that Current consumption rates will cause world energy demand to increase 1.6% per year until 2030. The world can expect energy prices to continue their generally upward spiral in the years ahead, if global energy policies remain the same. The price of meeting the world's energy demands is estimated at $26.3 trillion through 2030, an average of more than $1 trillion a year. In view of high cost of energy, the need for the use of intelligent forecasting method for power consumption is of paramount importance. According to Kankal et.al (2010) traditionally, regression analysis has been the most popular modelling technique in predicting energy consumption. However, ANN approach proves to be a more viable technique. Apart from reducing the time required, adoption of this technique makes energy applications more viable and thus more attractive to potential users, such as energy engineers. Also, this approach has additional advantages of computational speed, low cost for feasibility, and ease of design by operators with little technical experience. Therefore, the use of ANN for modelling and prediction purposes is becoming increasingly popular in the recent decades. Further advantages of ANN technique include; very good approximation capabilities as well as short development and fast processing times. ANNs are especially useful in predicting problems where mathematical formulae and prior knowledge on the relationship between inputs and outputs are unknown. Owda et.al (2014) pointed out that Forecasting energy consumed within a particular geographical area greatly depends on several factors, such as, historical load, mean atmospheric temperature, mean relative humidity, Population, GDP per Capita. In order to meet these demands and efficiently utilize the limited energy, it is imperative to observe historic trends and make futuristic plans based on past data. In the past, computationally easier approaches like regression and interpolation, have been used, however, these methods may not give sufficiently accurate results.


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B.I Gwaivangmin: Continental J. Engineering Sciences 11 (2): 12 - 31, 2016 As advances in technology and sophisticated tools are made, complex algorithmic approaches are introduced and higher accuracies, at the expense of heavy computational burden can be observed. Several algorithms have been proposed by several researchers to tackle electric energy consumption forecasting issues. Forecasting methods could be handled through three basic approaches; time series approach, functional based approach and soft computing based approach. Other forecasting methods were pointed out by Nasr et.al (2001). Various techniques for forecasting energy consumption have been proposed in the last decade. Specifically, Multivariate modelling along with Co-integration techniques is used to study the impact of different determinants on energy demand in different countries. Also, univariate modelling such as the Autoregressive Moving Average (ARMA) modelling technique has been successfully used for forecasting. Neural and adductive network models have also been successfully used for energy forecasting. Forecasting electrical energy demand requires that the input data are obtained from a reliable source as underestimation will lead to a severe challenges in the society. According to Dinkelman (2010) Electricity is pervasive in all industrialised countries and largely absent in the developing world: about 1.6 billion people world-wide lack access to electricity. The accuracy of the long-term load forecast has significant effects on developing future generation and distribution planning. An expensive overestimation of load demand will result in substantial investment for the construction of excess power facilities, while underestimation will result in customer discontentment. Unfortunately, it is difficult to forecast load demand accurately over a planning period of several years. This fact is due to the uncertain nature of the forecasting process. There are a large number of variables that characterize and directly or indirectly affect the underlying forecasting process; all of them uncertain and uncontrollable (Ghoda and Kalantar, 2011). According to International Energy Agency, IEA (2014) Electricity is the fastest-growing final form of energy, yet the power sector contributes more than any other to the reduction in the share of fossil fuels in the global energy mix. In total, some 7 200 Gigawatts (GW) of capacity needs to be built to keep pace with increasing electricity demand while also replacing existing power plants due to retire by 2040 (around 40% of the current fleet). Those who have no access to modern energy suffer from the most extreme form of energy insecurity. An estimated 620 million people in sub-Saharan Africa do not have access to electricity, and for those that do have it, supply is often insufficient, unreliable and among the costliest in the world. Energy used for power generation grows by 49% in 2011 – 30 (2.1% p.a.) and accounts for 57% of global primary energy growth. Primary energy used directly in industry grows by 31% (1.4% p.a.), accounting for 25% of the growth of primary energy consumption. BP (2013). WEC (2013a) in its submission reported that practically all technologies run on electricity and therefore the share of electricity is increasing rapidly, faster than Total Primary Energy Supply (TPES). Global Electricity Initiative, GEI (2014) pointed out that “Sustaining an efficient and


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competitive electricity market in Nigeria from the ruins of a public monopoly that could only generate a mere 4500MWh at best times is an exciting challenge. If we succeed in resolving this challenge, we will be unleashing the creative potential of more than 170 million people and realizing the hope of becoming an economic power that can end extreme poverty in Africa’s largest population and economy. There is need for infrastructural development in the electrical energy sector. Establishment of energy infrastructure in the least developed countries will need a major effort on behalf of the global energy community. It will also require political, legal and institutional structures, which today do not exist. Rising energy demand, declining public investment and the evolving role of the multilateral financial institutions need increased efforts by governments to be reversed in order to create an enabling business environment that will attract private investment, both domestic and international(WEC,2013b).With over 162 million people, Nigeria is the 7th largest country population in the world. It represents nearly half the total population of West Africa and more than 15% of the total population of the entire continent. But as of August 2013, only 40% of the population had access to electricity pointing to insufficient funding, corruption and the outdated state-owned utilities in the country The University of Jos was founded in November 1971 as a College affiliated to the University of Ibadan. In September 1975, University of Jos was granted autonomy consequent upon the promulgation of the Decree 82 of 1979 establishing it. The University is located in Jos the plateau state capital in central Nigeria. The University community was faced with lack of adequate and stable power supply which has adversely affected the operations of the University. Over the years, the growing population of the University community has led to an increase in the demand for electrical energy; this has resulted to load-shedding of the university feeder which was formally being shared with the Katako market and environs. In a move to check the continuous shortfall in electrical power supply an 11KV dedicated feeder was provided. This effort resulted in significant improvement on electric power supply in the University community. The objectives of this paper are to:(i) investigate power supply situation in the University of Jos community before construction of the 11KV dedicated feeder (ii) analyse current power supply situation in the University community and (iii) forecast the future energy demand of the 11KV dedicated feeder with a view to implementing the power infrastructural master plan. University of Jos 11KV Dedicated Feeder. The case study is the 11KV dedicated feeder and its distribution networks which covers Bauchi road Main Campus, Bauchi road Senior Staff Quarters, Students Village Hostel, Naraguta Hostels, Abuja Hostel, Permanent Site Senior Staff Quarters etc. as shown in Figure1.


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Figure 1: University of Jos 11KV Dedicated Feeder

The University of Jos 11KV dedicated feeder is made up of 14 transformers and about 8.7 km of distribution line. The main Power transformer is 7.5MVA, the other transformers range from 100KVA to 1000KVA. Power supply Situation Before the 11KV Feeder The Power supply infrastructure of the University had its main source of supply from a 15MVA power transformer which also supplies two major 11KV feeders of Jos township; Katako and Gada


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B.I Gwaivangmin: Continental J. Engineering Sciences 11 (2): 12 - 31, 2016 Biyu. The 11KV line was about 3.4 km distance, with old distribution panels. This arrangement did not provide adequate power supply to the University community, arising from frequent load-shading. The University had barely 10 hours of low voltage (150 V single phase) power supply in a day, on the average This situation was experienced most of the time, leading to low productivity Power Supply Situation after the Construction of 11KV Feeder Upon construction of the 11KV dedicated feeder, the University power was supplied from a 7.5MVA dedicated transformer, for use only by the University community. To cover wider area within the university community, the high tension line has been extended to 8.7Km. The old 11KV switch panel has been replaced by a new 11KV Switch panel. The power supply availability within the network is now 20-21 hours on the average and the voltage is 215-240V single phase. National Integrate Infrastructure Master Plan According to Usman (2013) The National Integrated Infrastructure Master Plan (NIIMP) has a 30 year horizon, 2014-2043; 3Nos. 10-year strategic plans; 6Nos. 5-year operational plans. The key objectives of NIIMP are to ensure coordinated approach to infrastructure development in Nigeria, help to integrate diverse infrastructure plans and projects across all sectors and regions in Nigeria. Other objectives are to: Strengthen linkage between infrastructure sector components and the national economy, review, upgrade and harmonise existing subsector master plans. It is also to prioritise projects and programmes for implementation in the medium to long-term, promote private sector participation in infrastructure development, and enhance performance and efficiency of the economy. NIIMP is to also to provide the capital allocation framework for the required investments and bring infrastructure in Nigeria in line with the country’s growth aspirations. National Targets and Investment Requirements Current inadequate and inefficient infrastructure poses threats to daily economic life and productivity in Nigeria. To close current infrastructure gap and reach desired optimal investment, Nigeria must aggressively increase core infrastructure stock from 35-40% of GDP in 2012 to 70% by 2043. Consequently, the need to increase investment spending in infrastructure. The Target investment requirement is approximately $2.9 trillion over next 30 years and the projected debt/GDP at 2043 - 25%. Power Infrastructure Master Plan Power generation plants, transmission networks, electrical grids, substations and distribution networks make up the infrastructure necessary for an efficient power sector. Financing the development of the Power infrastructure will require a great deal of money, hence the need for private sector participation. Recent privatisation trends in the power sector indicate that private sector can control about 48% shares of the power infrastructure. Improvements in power generation capacity in Nigeria have been made due to the output from the National Integrated Power Plants (NIPPs) and Independent Power Producers (IPPs). New power plants commissioned


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B.I Gwaivangmin: Continental J. Engineering Sciences 11 (2): 12 - 31, 2016 by the NIPP in 2013 have added an extra 1,300 MW to the national grid. It is estimated that Nigeria needs an annual investment of US$3.5bn to achieve its generation capacity target of 40,000 kilowatt hour (kWh) by 2020.Closing the power infrastructure gap offers many opportunities in the Nigerian power sector for investors, lenders and corporations. Potential entrants in the Nigerian power sector could own and operate power assets, finance assets (debt, equity, and mezzanine) or offer value-added services in the sector. Infrastructural development of the 11KV Dedicated Feeder The power infrastructure in the University of Jos had not been significantly upgraded since inception. This led to significant shortfall in electricity supply within the University. The University management in consultation with the Directorate of Physical Facilities took steps towards improving power supply situation by installing an 11KV dedicated line in June 2013. This has led to a significant improvement in power supply to the University. In order to ensure that the power supply situation in the University does not revert to the status of serious short fall, there is need for forecast of the energy demand of the 11KV dedicated feeder to meet up with future energy demands. Prediction of Energy Demand Energy Demand Analysis The energy demand of the 11KV dedicated line depends on many different factors. Generally the energy demand is influenced by seasonal data, climate parameters, and economic boundary conditions. Seasonal factors influence the energy consumption. Vacation and holidays have a significant impact on the energy consumption. The quality of the energy demand forecast depends significantly on the availability of historical consumption data and on the knowledge about the main influence parameters on energy demand. The functional relationship is non-linear and there are more or less complex interactions between different data types. Due to the large number of influence factors and their uncertainty, it is impossible to build up an’ exact’ physical model for energy demand. Data Collection The forecast data for the study is 30 days energy consumption of each of the 14 energy demand on the dedicated feeder for 38 months. The energy consumption for 38 months for each of the major installations was obtained from the records of bills of the 14 energy demand nodes from the power supply company and the electrical division of the Directorate of Physical Facilities, University of Jos. These were used as inputs to the back propagation Neural Network to forecast (predicted) future energy demands. Introduction to Neural Network Artificial Neural Networks have seen an explosion of interest over the last few years, and are being successfully applied across an extraordinary range of problem domains, in areas as diverse as


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B.I Gwaivangmin: Continental J. Engineering Sciences 11 (2): 12 - 31, 2016 finance, medicine, engineering, geology and physics. There have been many attempts to formally define neural networks. According to Mizun et al. (1998) “A neural network is a system composed of many simple processing elements operating in parallel whose function is determined by network structure, connection strengths, and the processing performed at computing elements or nodes”. An artificial neural network (ANN) is an interconnected group of artificial neurons that have a natural property for storing experiential knowledge and making it available for use. The first simplest form of feed forward neural network, called perceptron has been introduced by Rosenblatt in 1957. This original perceptron model contained only one layer, inputs are fed directly to the output unit via the weighted connections. Although the perceptron initially seemed promising, it was eventually proved that perceptrons could not be trained to recognize many classes of patterns. After that, multilayer perceptron (MLP) model was derived in 1960 and gradually became one of the most widely implemented neural network topologies. Multilayer perceptron means a feed forward network with one or more layers of nodes between the input and output nodes. The MLP overcomes many limitations of the single layer perceptron, their capabilities stem from the non-linear relationships among the nodes (Lippmann, 1987). Neural Network Prediction Model The objective of this model is to implement the mechanism and systems that can be employed to forecast both short – term and long - term energy demands. In the increasing growing field of computational intelligence technique, neural network has been used as an efficient tool to forecast the energy demand in the 14 energy demand nodes. The back propagation training algorithm could be employed to train the neural network. This is because it is efficient, easy to implement and does not consume time. The back propagation is a feed forward scheme that is interconnected by layers. If a set of inputs is applied to the input layer, it propagates through hidden layer until an output is generated from the output layer. The output is then compared with the desired output in which an error signal is computed for each output node. The error signals are then transmitted backwards from the output layer to each node in the hidden layers to update the connection weights. This process continues until the error signal is as desired. The computation steps for the back propagation algorithm are as follows:

• Initialize the interconnection weights and the node biases randomly. • Calculate the hidden layer output as:

+= ∑

=+

i

i

n

i

hjn

hj

hjii

hj wbwxfx

1,1, )( (1)


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B.I Gwaivangmin: Continental J. Engineering Sciences 11 (2): 12 - 31, 2016 Where h

jx is the output of the hidden node j , ix is the ith input, hjiw , is the weight connecting

input node i with hidden node j , hjb is the input bias to hidden node j (normally 1=h

jb ), h

jniw ,1+ is the weight connecting the bias to the hidden node j , in is the number of input nodes,

and

xexf −+

=1

1)( (Sigmoid function) (2)

Calculate the outputs of the output layer nodes as:

+= ∑

=+

h

hi

n

j

okn

ok

okj

hj

ok wbwxfx

1,1, )( (3)

Where o

kx is the output of the output node k , okjw , is the weight connecting the hidden node

j with output node k , okb is the input bias to the output node k (normally 1=o

kb ), oknh

w ,1+ is

the weight connecting the bias to the output node k , and hn is the number of hidden nodes.

• Calculate o

kδ of each of the output nodes as:

))(1( o

kTk

ok

ok

ok xxxx −−=δ (4)

Where o

kδ is the error (target-output) at the output of the neuron multiplied by the derivative

of )(xf , Tkx is the target output (desired output) of the node k .

• Calculate h

jδ of each of the hidden node as follows:

∑=

−=on

k

okj

ok

hj

hj

hj wxx

1,)1( δδ (5)

Where h

jδ the derivative of is )(xf multiplied by the summation of the weights multiplied by

the output delta.

• Adapt the weights of the output layer as:


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)()()1( .., twxtwtw okj

hj

ok

okj

okj ∆++=+ αηδ (6)

Where 10 <<η is the learning constant, 10 << α is the momentum constant, and

)1()()( ,,, −−=∆ twtwtw okj

okj

okj . (7)

• Adjust the weights of the hidden layer as:

)()()1( ,,, twxtwtw hjii

hj

hji

hji ∆++=+ αηδ (8)

Where 10 <<η is the learning constant, 10 << α is the momentum constant, and

)1()()( ,,, −−=∆ twtwtw hji

hji

hji . (9)

• Repeat the above steps consecutively until the square of the error becomes less than a preset

value (designer’s choice), which is defined as:

∑=

−=on

k

ok

Tk xxe

1

2)( (10)

The structure of the neural network remains fixed during training and only the weights are adapted. These algorithms should be subject to a rigorous analysis in order to determine their advantages and disadvantages when compared with other model - based algorithm (Gwaivangmin and Jiya, 2013). Data normalization The data is normalized before being input to the ANN. The input vectors of the training data are normalized such that all the features are zero-mean and unit variance. The target values are normalized such that if the activation function is Unipolar sigmoid, they are normalized to a value between 0 and 1 (since these are the minimum and maximum values of the activation function, and hence the output of the ANN), and if the activation function is Bipolar sigmoid or Tan hyperbolic, they are normalized to a value between -1 and 1 and 0 and 1/ 2�� when the activation function is RBF. The test data vector is again scaled by the same factors with which the training data was normalized. The output value from the ANN for this test vector is also scaled back with the same factor as the target values for the training data.


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B.I Gwaivangmin: Continental J. Engineering Sciences 11 (2): 12 - 31, 2016 In order to simplify the task of the neural network, the dataset was normalized to [0, 1] range using equation (2).

��

��

(11)

Where ��the normalized data point isX� is the original data point, Xmin and Xmax are the global minimum and maximum values of the data set respectively. This is done in order to ensure that the training is faster and the chance of getting stuck in the local optima is reduced.

SIMULATION RESULTS AND DISCUSSIONS Predicted Energy Demand In the study the forecasting tool used to predict the energy demand of the area covered by this investigation is the Neural network software called NeuNet Pro. Version 2.3 developed by Cormac Technologies (1999). The NeuNet Pro 2.3 uses a 3 layer Back propagation neural network with a bias of 1.0. Training begins with all weights set at random numbers .For each data record, the forecast (predicted) value is compared to the desired (actual) value and the weights are adjusted to move the prediction closer to the desired value. Many cycles are made through the entire set of training data with the weights being continually adjusted to produce more accurate forecasts (predictions). The data set used in this study is the 38 months, monthly energy demand recorded for the 14 nodes in University of Jos. The data is manipulated in two forms, namely, ‘normalization’ and ‘partition’. The parameter used for the Back propagation Neural network is shown in Table 1. The distribution of data point for the neural network used is shown in Table 2.


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Table 1: Energy Demand Data for the 11Kv Dedicated Feeder

Month Juth Clinical

Murtala Moh Way

Medical Sciences

Natural Science

Naraguta Female

Central Library

SSQ Perm Site

Student Village

Apr-12 0 2135 11178 4287 14077 3461 22468 42320 Jul-12 2301 3036 9439 2359 7754 3243 11139 25530 Aug-12 3110 1250 11507 5229 11248 3393 19830 56410 Jun-12 1429 1557 13450 6408 16640 4280 25018 48860 Sep-12 3105 2045 15289 6671 16084 3484 14303 77290 Oct-12 0

2301

1482 20039 9608 19548 5514 43799 64000 Nov-12 3036 16715 6582 15967 5370 36644 47930 May-12 0 1760 14726 8667 17320 5166 23334 47240 Jan-13 1590 2042 7070 4200 2500 3000 25945 15720 Dec-12 1679 3012 115347 6620 5717 3430 18442 29800 Feb-13 1697 2886 9587 1562 1361 2485 32720 64000 Mar-13 1394 3380 9487 4146 3583 2224 21953 25120 Apr-13 1188 3195 12527 1946 6025 3823 21400 22830 May-13 2109 1673 15694 9717 5757 3200 25622 30490 Jun-13 1443 3368 13683 7300 1157 5094 32895 22360 Jul-13 1366 2529 17978 109854 11682 5046 2904 35584 Aug-13 953 1916 19848 11611 775 4056 31064 29410 Sep-13 1168 1901 11625 9228 4724 4403 39319 27540 Oct-13 609 1781 8899 9940 3978 4364 39189 21120 Nov-13 894 1786 11309 10344 2517 2099 2731 2880 Dec-13 1168 1845 15041 13382 3616 3941 41491 29630


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Jan-14 860 2179 10862 7749 7100 2912 29164 27040 Mar-14 871 2982 14685 14004 16285 7424 47874 58560 Apr-14 1088 3438 20489 17929 12046 8560 44451 46200 May-14 844 3408 18426 18701 5763 7081 44744 30880 Jun-14 1075 3198 21364 16017 10295 7688 34488 43430 Jul-14 986 3198 19454 16444 9330 7357 39114 40331 Aug-14 741 3198 16772 15672 13344 5964 37632 36980 Sep-14 741 3198 18324 16539 16340 6205 39133 44930 Oct-14 406 3198 17968 15882 17634 5744 45088 60435 Nov-14 586 0 18151 19011 14586 6472 42944 61040 Dec-14 539 3198 18848 18704 1586 6272 43473 57054 Jan-15 570 5229 18402 20588 13406 7136 44454 62152 Feb-15 350 6502 18739 20692 8077 7047 44102 46911 Mar-15 250 5681 12490 15676 6250 4500 46330 33310 Apr-15 180 3020 15545 16282 6118 4818 34149 39760 May-15 180 3200 14657 14675 5623 3468 41200 36800 Jun-15 186 1563 17136 22112 3739 7015 36456 47070

Table 2: Parameters used in the Back propagation Neural Network (NeuNet Pro 2.3)

Parameter Value Number of Hidden Nodes 8 Learn Rate 50 Momentum 50 Jog Weights Nil


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Figure 2: A typical Training Process fort Students Village Hostels

Table 3: The distribution of data point

Data Set Distribution of data points Training set 28 Testing set 30

Figure 3: Splitting of Data for Training and Testing


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The predicted energy demand data for the 14 energy demand nodes are shown in Tables 4-7 and was plotted using NeuNet Pro software and the plots are shown in Figures 4 – 7 for Students village, permanent site senior staff quarters, Medical Sciences Faculty and Naraguta Female Hostels respectively.

Table 4: Students Village Hostels Energy Data

Figure 4: Graph for Students Village Hostels Predicted Energy Demand


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Table: 5 Permanent Staff Senior Staff Quarters Energy Data

Figure. 5: Graph for Permanent Staff Senior Quarters Predicted Energy Demand


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Table 6: Predicted Energy Demand Data for Medical Sciences Faculty

Fig. 6: Graph for Predicted Energy Demand of Medical Sciences Faculty


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Table 7: Predicted Energy Demand Data for Naraguta Female Hostels

Fig. 7: Graph for Predicted Energy Demand of Naraguta Female Hostels


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B.I Gwaivangmin: Continental J. Engineering Sciences 11 (2): 12 - 31, 2016 The predicted energy demand for the various demand nodes gave an idea of the power needs in all the various energy demand locations. These results influenced the decision to provide an 11KV dedicated feeder that could serve the energy need of the University. The predicted results were not very far from the actual demand; this is seen from 10 a.m to midnight in the results plotted on the graph.

CONCLUSIONS The Study showed each of the 14 energy demand nodes predicted using the Back propagation neural network, with the aid of NeuNet Pro2.3 software. The infrastructural development carried out by the University improved the power supply situation of the University community which no doubt has improved productivity. Forecasting the energy demand for the 11KV dedicated feeder will provide data that may help towards a power supply master plan for the University to avert future energy crisis that will result from increase in population and other infrastructural development within the 11KV dedicated feeder network.

REFERENCES British Petroleum (2013). BP Energy Outlook 2030. Dinkelman.T (2010). The Effects of Rural Electrification on Employment: New Evidence from South Africa. Princeton University South Africa. Global Electricity Initiative (2014). GEI Report. Ghods.L and Kalantar.M (2011). Different Methods of Long-term Electric Load Demand Forecasting; A Comprehensive Review. Iranian Journal of Electrical and Electronic Engineering. Vol 7 (No.4), 13-23. Gwaivangmin B.I and Jiya J.D (2013) ‘’Neural Network Based Prediction of Water Demand of a Water Distribution Network for Supervisory Control. Nigerian Society of Engineers, International Conference and Annual General Meeting. IEA (2014). World Energy Outlook, Executive Summary. IEA Publications, 9 rue dela Federation, 7739 Paris Cedex 15. Kankal.M., Akpinar.A.,Komurcu.M.I and Ozsahin (2010). Modelling and Forecasting of Turkey’s Energy Consumption Using Socio-economic and Demographic Variables.WWW.elsevier.com/locate/apenergy. Lippmann. R.P (1987). An Introduction to Computing with Neural Nets. IEEE ASSP Magazine 4, 4.22.


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B.I Gwaivangmin: Continental J. Engineering Sciences 11 (2): 12 - 31, 2016 Mizun.H., Kosaka.M.,Yajima.H and Komoda.N (1998). Application of Neural Network to Technical Analysis of Stock Market Prediction. Studies in Information and Control. Vol.7 .No.3 pp.111-120. Nasr.G.E., Badr.E.A., and Younes M.R (2001). Neural Networks in Forecasting Electrical Energy Consumption. FLAIRS -01 Proceedings www.aaai.org. Owda. H.M.H., Omoniwa. B., Shahid. A.R and Ziauddin .S (2014). Using Artificial Neural Network Techniques for Prediction of Electric Energy Consumption. Arxiv: 1412.2186v1. Taylor.J. W., DeMerezes. L.M and McSharry. P.E (2006). A Comparison of Univariate Methods for Forecasting Electricity Demand Up to Day Ahead. International Journal of Forecasting, Vol.22.pp.1-16. Tier. J (2015). World Watch Institute, Energy Agency Predict High Prices in Future. Usman. S (2013). Financing Infrastructure through the Capital Market. Presentation at the Infrastructure Roundtable Organised by the Securities and Exchange Commission (SEC) in Collaboration with the National Planning Commission (NPC) August 5. World Energy Council (2013). World Energy Resources 2013 Survey. World Energy Council (2013). World Energy Perspective Cost of Energy Technologies. World Watch Institute (2015). Energy Agency Predicts High Prices in Future.

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