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European Journal of Applied Sciences – Vol.10, No.5
Publication Date: October 25, 2022
DOI:10.14738/aivp.105.13015. Braide, S. L. (2022). Nigeria Generating Station Capacity and Load Demand Requirement Using Regression Analysis (Least Square)
and Artificial Neuro-Fuzzy Inference System (Anfis) for Reliable Power Supply. European Journal of Applied Sciences, 10(5). 516-
543.
Services for Science and Education – United Kingdom
Nigeria Generating Station Capacity and Load Demand
Requirement Using Regression Analysis (Least Square) and
Artificial Neuro-Fuzzy Inference System (Anfis) for Reliable
Power Supply
Sepiribo Lucky Braide
Department of Electrical Engineering, Rivers State University
Nkpolu Oroworukwo, Port Harcourt, Rivers State, Nigeria
ABSTRACT
The increasing demand of electrical energy is growing exponential rate while
there is mismatched between power supply and power demand. This means that
there is strong record to determine the existing state of the system for purpose of
future projection of power generation to avoid system collapse. Evidently, to
conduct the analysis of system forecast for energy demand, many mathematical
techniques and artificial machine learning which is based on artificial intelligence.
This paper considered the application of least – square regression technique and
artificial neuro-fuzzy inference system (ANFIS) and artificial neural network
(ANN) principle, on the view to train the data set for Nigeria energy consumption
(residential. Commercial and industrial) on the view to determine total system
consumption pattern for reliable power supply. The data used ranges from (2000-
2012) as input for the projection plan of 2013-2035, was determined for Nigeria
power system. This is modelled and simulated in matlab software tool (version
19.0). the purposed artificial intelligence system based on Artificial neural fuzzy
inference system (ANFIS) which provided high machine learning fixtures to
predicts close to the actual value as compared to the traditional least square
algorithms. The results were evaluated using mean absolute percentage error
(MAPE) of 5.73%. The proposed technique ANFIS predicted load forecast (2013-
2035) to provide energy data for electric utilities and to plan successfully for
efficient power generation. Application of Adaptive Neuron – fuzzy inference in
artificial intelligence involved both neural network (NN) and fuzzy logic (FL)
principle together an adaptive neuro-fuzzy inference system (ANFIS), is an
artificial neural network that is based on a fuzzy inference system. ANFIS is a very
useful system to extract numerical model from numerical data since it integrates
both neural network and fuzzy logic principle together, it is capable of adapting
the benefits of both in a single framework. An adaptive neuro-fuzzy inference
system (ANFIS) is an application of adaptive neuro-fuzzy logic that uses frame
work of artificial intelligence (AI). Hence its inference system corresponds to a
code of IF – THEN RULE in fuzzy neuro network. It has a learning capability to
approximate non-linear functions hence adaptive neurofuzzy inference system
(ANFIS) deserves to be considered as good estimator.
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517
Braide, S. L. (2022). Nigeria Generating Station Capacity and Load Demand Requirement Using Regression Analysis (Least Square) and Artificial
Neuro-Fuzzy Inference System (Anfis) for Reliable Power Supply. European Journal of Applied Sciences, 10(5). 516-543.
URL: http://dx.doi.org/10.14738/aivp.105.13015
INTRODUCTION
Load Forecasting
The future is uncertain; therefore, this uncertainty of energy demand necessitates the need
for electricity forecasting which in a way attempt to predict what the future electricity
demand will be, and probably recommend capacity addition (generating units) to compensate
the mismatches of inadequacies.
Forecasting is the prediction of energy consumption over a look ahead periods of five, ten and
twenty etc.
The first crucial step for planning: (Generation, transmission and distribution etc.), is to
predict for the study period, in this case: twenty years (2012 – 2032), projection.
Why the need and importance of Forecast
Energy demand forecasting is an essential activity of electrical providers. Without an
“accurate” picture of the future, which may be based upon, in the past, over-capacity or
shortage (under capacity) in the power system may seriously result, producing unexpected
high costs.
Power demand in a given place location etc. does vary with growth in population and
economic activities, this is because the fundamental challenges of an electricity utilities
companies is to forecast load requirements at various times, to meet up the ever-growing
economy; demand.
The future is uncertain, therefore there is need for consistent follow-up for load-forecasting
programme.
Similarly, to ascertain whether there is over or excessive abundance) of electricity supply, and
possibly the need for shutting down some of the generating (plants).
Data presentation: Load forecast result
The data used in this work cover electrical energy consumption in Nigeria from (2000-2012)
broken down into three categories: residential, commercial and industrial. They are collected
from the National Bureau of statistics and the Central Bank of Nigeria, Statistical Bulletin.
Data Input
The datas that served as the input for the generation expansion planning (decomposition
techniques): includes totaling the residential, commercial and industrial load demand which
gives 20,136.41MW.
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European Journal of Applied Sciences (EJAS) Vol.10, Issue 5, October-2022
Services for Science and Education – United Kingdom
Manual Computation of Loads Forecast
Table 1: Table of Energy Consumption (Mw)
ENERGY CONSUMPTION
YEAR RESIDE
NTIAL
COMME
RCIAL
INDUST
RIAL
TOTAL
2000 4608.4
0
2346.00 1011.60 8688.90
2001 7714.8
0
2439.00 1987.20 9034.40
2002 7668.5
0
3297.60 1830.00 12842.4
0
2003 7668.5
0
3583.00 1659.80 12866.6
0
2004 7725.3
0
3830.30 1605.00 13160.6
0
2005 7760.0
0
3851.00 1615.50 13226.6
0
2006 7650.0
0
3900.80 1575.00 13125.8
0
2007 7860.3
0
3915.00 1530.50 13305.8
0
2008 7910.0
8
3852.00 1502.50 13264.5
5
2009 8075.0
0
3865.50 1585.00 13525.5
0
2010 8205.2
0
3925.80 1589.40 13720.4
0
2011 8285.6
0
4004.70 1615.50 13905.8
0
2012 8350.0
0
4025.40 1648.00 14023.4
0
TOTA
L
99481.
68
46836.1 20755 164690
.8
Source: Central Bank of Nigeria STATISTICAL BULLETIN and National Bureau of Statistics (NBS).
DATA ANALYSIS: RESIDENTIAL FORECAST
The residential demand forecast is performed using the data below: computation of
residential load demand using regression analysis techniques.