Research
Areas
Artificial
Neural Networks in Engineering Design and Engineering Applications
Christos
N. Schizas and Costas Neocleous, Soteris Kalogirou, Silas Chr.
Michaelides.
The
power of artificial neural networks to model engineering
systems, primarily in engineering design and control,
is being applied to various problems. Applications have
been developed on the design of marine propellers, concentrating
solar collectors, and heating and airconditioning load
estimations, and power load forecasting.


Related Papers
Title
: 
Artificial
neural networks in marine propeller design. 
Authors
: 
Costas
Neocleous, Christos N. Schizas 
Abstract
: 
Various
neural network systems were developed for examining propeller
performance data that were derived experimentally. This study
aimed to establish an accurate mapping thus facilitating propeller
selection during the design process. Different neural network
architectures and learning rates were tested, aiming at establishing
a near optimum setup. It is evident from the findings, that
this technology can be used effectively in modeling the performance
of a series of marine propellers and thus may be used for
propeller selection, and for extrapolation to new designs. 
Title
: 
Comparative
study of methods for estimating intercept factor of parabolic
trough collectors. 
Authors
: 
Soteris
Kalogirou, Costas Neocleous, Christos N. Schizas 
Abstract
: 
One
of the parameters used for the evaluation of a parabolic
trough collector performance is optical efficiency. This
depends on the properties of the various materials employed
in the construction of the collector, the collector dimensions,
the angle of incidence and the intercept factor (ã).
The intercept factor depends on the size of the receiver,
the surface angle errors of the parabolic mirror, and on
solar beam spread. A raytrace computer code called EDEP
(Energy DEPosition computer code) is used
by Guven and Bannerot (1985) to calculate the intercept
factor. The intercept factor can also be calculated by a
closedform expression developed by Guven and Bannerot (1985).
This expression considers both random and nonrandom errors.
These errors are encountered in the construction and/or
in the operation of the collector. An artificial neural
network was trained to learn the ãvalues based on
the input data of collector rim angle, random and nonrandom
errors, and the EDEP results. The output is compared with
the EDEP results which are considered to be the most accurate,
the results of a simple program developed by Guven (1987)
using the trapezoidal integration method, and a multiple
linear regression analysis. From all the above it is shown
that the results obtained by the artificial neural network
system approximates the results of the raytrace model,
extremely well with an R^{2}value equal to 0.999. 
Title
: 
Building
heating load estimation using artificial neural networks. 
Authors
: 
Soteris
Kalogirou, Costas Neocleous, Christos N. Schizas 
Abstract
: 
A
number of commercial software programs are currently available
for the estimation of the heating loads of buildings. These
programs basically perform simple multiplications between
the areas of the various building envelope components with
the corresponding Uvalues and the effective temperature
difference. The results of these multiplications are then
added to obtain the actual heating load on which a 5% safety
factor is usually added. A novel approach is presented in
this paper. The objective is to train an artificial neural
network (ANN) to be able to predict the building heating
loads with the minimum of input data. An ANN has been trained
based on 250 actual data varying from very small rooms to
large spaces of 100 m^{2} floor area. The type of
rooms varied from small toilets to large classroom halls
while the room temperatures varied from 18°C to 23°C.
In addition to the above an attempt was made to use a large
variety of room characteristics e.g. rooms without windows,
rooms without external walls and windows etc. In this way
the network was trained to accept and handle a number of
unusual cases. The data input were the areas of windows,
walls, partitions and floors, the type of windows and walls,
and the classification on whether the space has roof or
ceiling. The output training data was the actual heating
load. Preliminary results on the training of the network
showed that the accuracy of the prediction could be improved
by separating the input data into two categories one with
spaces of floor areas up to 7 m^{2} and one with
spaces of floor areas from 7 to 100 m^{2}. The statistical
R^{2}value of the training data set was equal to
0.988 for the first case and 0.999 for the second. Unknown
data were subsequently used to investigate the accuracy
of prediction. Predictions within 10% for the first case
and 9% for the second were obtained. These results indicate
that the present method can successfully be used for the
prediction of a building’s heating load. 
Title
: 
Artificial
neural networks for modeling the startingup of a solar
steam generator. 
Authors
: 
Soteris
Kalogirou, Costas Neocleous, Christos N. Schizas

Abstract
: 
An
experimental solar steam generator, consisting of a parabolic
trough collector, a high pressure steam circuit, and a
suitable flash vessel has been constructed and tested
in order to establish the thermodynamic performance during
heatup. Preliminary tests demonstrated that the heatup
energy requirement has a marked effect on the system performance
since solar energy collected during the heatingup period
is lost at night due to the diurnal cycle. This depends
mostly on the dimensions and the inventory of the flash
vessel, and the prevailing environmental conditions. Experimental
data were obtained and used to train an artificial neural
network in order to implement a mapping between easily
measurable features (environmental conditions, water content,
vessel dimensions) and the system temperatures. Such mapping
may be useful to system designers when seeking to find
the optimum vessel dimensions. The trained network predicted
very well the response of the system, as indicated by
an obtained statistical Rsquared value of 0.999 and a
maximum deviation between predicted and actual values
confined to less than 3.9 %. This degree of accuracy is
acceptable in the design of such systems. The results
are
important, because the system was tested during its heatup
cycle, under transient conditions, which is quite difficult
to model analytically.

Title
: 
Artificial
neural networks in energy applications: A review. 
Authors
: 
Soteris
Kalogirou, Costas Neocleous, Christos N. Schizas 
Abstract
: 
Artificial
neural networks are widely accepted as a technology offering
an alternative way to tackle complex and ill specified problems.
They can learn from examples, are fault tolerant in the
sense that they are able to handle noisy and incomplete
data, are able to deal with nonlinear problems, and once
trained can perform prediction and generalisation at high
speed. They have been used in diverse applications in control,
robotics, pattern recognition, forecasting, medicine, power
systems, manufacturing, optimisation, signal processing,
and social/psychological sciences. They are particularly
useful in system modelling such as in implementing complex
mappings and system identification. This paper presents
various applications of neural networks in energy problems
in a thematic rather than a chronological or any other order.
Artificial neural networks have been used by the authors
in the field of solar energy, for modelling the heatup
response of a solar steam generating plant, for the estimation
of a parabolic trough collector intercept factor, for the
estimation of a parabolic trough collector local concentration
ratio and for the design of a solar steam generation system.
They have also been used for the estimation of heating loads
of buildings. In all those models a multiple hidden layer
architecture has been used. Errors reported in these models
are well within acceptable limits, which clearly suggest
that artificial neural networks can be used for modelling
in other fields of energy production and use. The work of
other researchers in the field of energy is also reported.
This includes the use of artificial neural networks in heating
ventilating and airconditioning systems, solar radiation,
modelling and control of power generation systems, and load
forecasting. 
Title
: 
Artificial
neural networks for the estimation of the performance
of a parabolic trough collector steam generation system. 
Authors
: 
Soteris
Kalogirou, Costas Neocleous, Christos N. Schizas

Abstract
: 
The
parabolic trough collector (PTC) is the most preferred
type of solar collecting system employed for steam generation.
This is due to the fact that the collector can work with
high efficiencies at high temperature. Such a system consists
of a PTC, a flash vessel and an associated circulation
and control system. The amount of steam produced depends
on the solar radiation available, ambient air temperature,
collector area, total system water capacity, and the diameter
and height of the flash vessel. Data for a number of cases
were used to train an artificial neural network in order
to generate a mapping between the above easily measurable
inputs and the desired output system performance. The
collectors used had areas varying from 3.5 m^{2}
to 2160 m^{2}. Different neural architectures
have been used in order to find the one with the best
possible performance. The multilayer feedforward architecture
using the standard backpropagation learning algorithm
have been the most successful so far. Many other architectures
are still under investigation. The results obtained for
the training set are such that they yield a statistical
R^{2 }= 0.999. The network was used subsequently
for predictions of the performance of cases other than
the ones used for training, both within and outside the
above range. Typical value of the accuracy obtained was
95% (at an R^{2 }= 0.999). This is considered
as acceptable for such estimations 
Title
: 
Artificial
neural networks for the estimation of Uvalues. 
Authors
: 
Soteris
Kalogirou, Costas Neocleous, Christos N. Schizas 
Abstract
: 
An
important parameter for the estimation of building thermal
loads is the Uvalue. An accurate value will enable a more
correct estimation of the load. Neural networks are widely
accepted as a technology offering an alternative way to
tackle complex and ill specified problems. They can learn
from examples, are fault tolerant in the sense that they
are able to handle noisy and incomplete data, are able to
deal with nonlinear problems, and once trained can perform
prediction and generalization at high speed. Artificial
neural networks have been used in diverse applications in
control, robotics, pattern recognition, forecasting, medicine,
power systems, manufacturing, optimisation, signal processing,
and social/psychological sciences. They have shown to be
particularly useful in system modelling such as implementing
complex mappings and for system identification. The objective
of this study is to investigate the suitability of artificial
neural networks as tools for the estimation of Uvalues
quickly and accurately. This will facilitate the work of
design engineers in the field. The data presented as input
to the network were the thicknesses of hollow and solid
bricks, concrete, various types of insulating materials,
air gap, and plaster. Materials which are not used in a
construction are given a thickness equal to zero. The network
output is the Uvalue. The data provided to the network
for training are values of U obtained from well established
references. The statistical R^{2}value for the
training set was equal to 0.9999. The network then, will
hopefully learn the basic mechanics of calculation, and
thus be able to generalise to an unknown set of data. Unknown
data, i.e., constructions with material thicknesses not
known to the network, were subsequently used to investigate
the accuracy of the prediction. Predictions with R^{2}value
equal to 0.9817 were obtained. This is considered satisfactory
for design purposes. Although estimated Uvalues have been
used in this work, if experimental values are available
these will enhance the capability of the method, as the
prediction will not depend on handbook recorded data but
on real values. It should be noted that in this case it
would not be required to perform experiments with all combinations
of materials but only a limited number for each different
combination of materials. 
Title
: 
An
artificial neural network for the shortterm forecasting
of electric power load 
Authors
: 
Thalis
Papazoglou, Costas Neocleous, Christos N. Schizas 
Abstract
: 
A
feedforward multilayer neural network has been used for
the estimation of the fourhour ahead power load in Crete.
An attempt was made to use few variables for the input
vector, while keeping the degree of accuracy to acceptable
levels. Different neural network architectures, activation
functions and learning rates were tried, aiming at finding
the network that resulted in the best possible estimation.
A feedforward two hidden layer architecture using backpropagation
was chosen. The correlation coefficient obtained for both
the training and the unknown testing data sets was 0.984.
The prediction error for 90% of the cases was confined
to less than 10 MW, which is considered adequate. The
neural network results have also been compared to a multiple
linear regression and a multiple nonlinear regression
analysis. In both cases the neural network approach is
slightly better. 
Title
: 
Wind
Speed Prediction Using Artificial Neural Networks 
Authors
: 
Soteris
Kalogirou, Costas Neocleous, S. Pashiardis, Christos
N. Schizas

Abstract
: 
A
multilayered artificial neural network has been used for
predicting the mean monthly wind speed in regions of Cyprus
where data are not available. Data for the period 19861996
have been used to train a neural network, whereas data
for the year 1997 were used for validation. Both learning
and prediction were performed with adequate accuracy.
Two network architectures of the similar type have been
tried. One with eleven neurons in the input layer and
one with five. The second one proved to be more accurate
in predicting the mean wind speed. The maximum percentage
difference for the validation set was confined to less
than 1.8% on an annual basis, which is considered by the
domain expert as adequate. 
