Development of distributed topographical forecasting model for wind

Development of distributed topographical forecasting model for wind
resource assessment using artificial neural networks
P.Badari Narayana*a, Dr. S.Srinivasa Raob, Dr. K. Hemachandra Reddya
*a Director, Green Life Energy Solutions LLP, Secunderabad, A.P,
b
Professor, Dept. of Mechanical Engineering, National Institute of Technology, Warangal,A.P
a
Professor- Mechanical Engineering, JNTUCEA & Registrar, JNT University, Anantapur, A.P
ABSTRACT
Economics of wind power projects largely depend on the availability of wind power density. Wind resource assessment
is a study estimating wind speeds and wind power densities in the region under consideration. The accuracy and
reliability of data sets comprising of wind speeds and wind power densities at different heights per topographic region
characterized by elevation or mean sea level, is important for wind power projects. Indian Wind Resource Assessment
program conducted in 80’s consisted of wind data measured by monitoring stations at different topographies in order to
measure wind power density values at 25 and 50 meters above the ground level. In this paper, an attempt has been made
to assess wind resource at a given location using artificial neural networks. Existing wind resource data has been used to
train the neural networks. Location topography (characterized by longitude, latitude and mean sea level), air density,
mean annual wind speed (MAWS) are used as inputs to the neural network. Mean annual wind power density (MAWPD)
in watt/m2 is predicted for a new topographic location. Simple back propagation based neural network has been found to
be sufficient for predicting these values with suitable accuracy. This model is closely linked to the problem of wind
energy forecasting considering the variations of specific atmospheric variables with time horizons. This model will help
the wind farm developers to have an initial estimation of the wind energy potential at a particular topography.
Keywords: wind speed, Mean annual wind power density (MAWPD), neural network and Forecasting.
1. INTRODUCTION
Wind energy has emerged as the most attractive renewable energy source for generation electrical power. The number of
wind power installations in India is growing rapidly, and the rate is faster than ever before. Wind resource is a prime
factor for decision making in wind power projects. Wind power varies in cubic proportion of wind speed. Hence, wind
power extraction estimation will be highly sensitive to small changes in wind speeds, topographies or wind patterns [1].
Wind pattern in India is strongly affected by monsoons. From March to August, winds are uniformly stronger over the
whole Indian coast. Wind resource assessment is a continuous activity with a view to provide suitable wind data inputs to
the wind energy sector. Wind resource statistics at more than 200 wind monitoring stations is available as a data bank for
conducting simulation studies. To achieve best possible forecasting performance, wind resource data pertaining to
different regions has been utilized for simulation in this work. Atmospheric energy across space and time varies with
region, season and topographic location. This will have a significant implication for forecast performance [2].
Data processing tools are the major component of wind resource assessment and forecasting. Conventional
numerical and statistical methods taking data and generating predictions have been found to be inferior to neural
computing models. These neural computing models learn from the actual data through the defined non-linear mapping
methodologies and exhibit reasonably better performance during validation and testing.
1.1 Wind Energy Data
The fundamental data needed to estimate wind energy potential for any geographic region consists of wind
speed and wind power density – preferably at altitudes comparable to the hub heights of the wind turbines that are likely
to be installed and at a sufficiently high resolution level (both temporally and spatially) to ensure robustness of the
estimates. Published data of 200 and more wind monitoring stations has been used as a source for this work. The data
incorporated following parameters:
(i)
Mean Annual Wind Power Density (MAWPD) is a truer indication of a site’s wind energy potential.
Its value combines the effect of a site’s wind speed distribution and its dependence on air density and
wind speed. WPD is defined as the wind power available per unit area swept by the wind turbine
blades and is given by the following equation:
WPD =
-where
1
2n
n
vi 3
w/m2
(1)
1
air density in kg/m3 is,
vi is
wind speed in m/sec and n is the number of records in the
average interval taken.
(ii)
Longitude and latitude of the topographic site
(iii)
Mean sea Level or altitude in meters.
(iv)
Mast height in meters.
(v)
Air density given by
=
P0
exp
RT
g.z
RT
(kg/m3)
(2)
Po = the standard sea level atmospheric pressure (101,325 Pa), or the actual sea level-adjusted pressure
reading; g = the gravitational constant (9.8 m/s²); and z = the site elevation above sea level (m).
1.2
Data Preparation
There are 231 data patterns available from the Wind Resource Assessment (WRA) data published by Indian
Meteorological Department (IMD) data pertaining. Table 1 shows the sample data records taken from the published data
bank.
Table 1. Sample data used for wind resource assessment & forecasting
Station
SNo
Mast
Height
in m
Latt. N
Long E
Deg Min
Deg Min
Altitude m
Air
Density
in kg/m3
MAWS at
Mast
Height
m/sec
MAWP
D
at
Mast
Height
in w/m2
1
Keating Point
20
9.15
92.46
10
1172
4.9
114
2
Kadavakallu 2
20
14.47
79.21
981
1104
6.47
437
2. ARTIFICIAL NEURAL NETWORKS AS A MODELING TOOL FOR WIND RESOURCE
ASSESSMENT & FORECASTING:
Assessing wind resource across different topographical locations is highly non-linear and the conventional mathematical
models fail to give solutions. Artificial Neural Networks is a real time diagnostic, modeling, control and optimization
tool that has the ability to capture non-linearties of system variables. ANNs extract the required information directly
from the data because of their unique learning capability. They are capable of learning from nonlinear data of a complex
problem and can predict the desired values with high accuracy.
An ANN usually consists of an input layer, some hidden layers, and an output layer. The input layer consists of
all the input factors and information from the input layer is then processed in the course of one hidden layer, and a
following output vector is computed in the output layer. Generally the hidden and the output layers have an activation
function. The Sigmoidal activation function applies a sigmoid transfer function to its input patterns, representing a good
non-linear element to build the hidden layers of the neural network, such a layer is named as sigmoid layer.
Development of Neural networks based forecasting model involves three stages – pre-processing, training,
validation & testing and post-processing. Pre-processing is a stage where all the inputs are arranged in the range of [0,1].
This process is known as normalization. In this work, scale factor based normalization has been adapted for simplicity
sake. All the input and target values were normalized using scale factor based normalization technique so that given
input-output mapping data will fall in the range of [0, 1].
Table 2. Pre-Processing of input-output pairs using scale factor based normalization
Maximum
Minimum
Scale Factor
Max Value/Scale
Factor
Min Value/Scale
Factor
27.7
8.12
40
92.46
68.36
110
1830
1
2000
1174
442
1400
8.82
3.97
11
582
93
625
0.6925
0.8405
0.915
0.839 0.8018
0.9312
0.203
0.6215
0.0005
0.316 0.3609
0.1488
Training is an important stage in which an input is introduced to the network together with the desired outputs,
the weights and bias values are initially chosen randomly and the weights are adjusted, so that the network attempts to
produce the desired output. When a satisfactory level of performance is reached, the training stops, and the network uses
these weights to make decisions. In the supervised learning, a neural network learns to resolve a problem simply by
modifying its internal connections (biases of the Desired Output Layer and weights) by back-propagating the difference
between the current output of the neural network and the desired response. The training algorithm searches for an
optimal combination of network’s biases or weights by moving a virtual point along a multidimensional error surface,
until a good minimum is found. The developed neural network is based on an algorithm that adjusts the layers’ bias and
the synapses’ weight, according to the gradient calculated by the teacher neuron and is back-propagated by the
backward-transportation mechanism. Such an algorithm is known as feed forward back-propagation technique. Many
optimization searching techniques are available based on the method of calculating the gradient. In this work, gradientdescent optimization algorithm has been used for training and testing the data patterns. This algorithm is a suitable
method for training the moderate-sized feed forward neural networks up to several hundred of weights. This algorithm
uses the following parameters to work: learning rate, that represents the ’speed’ of the virtual point along the error
surface and the momentum, that represents the ’inertia’ of that point. Neurons in the input and output layers have no
transfer function and a sigmoid transfer function have been used for the neurons in hidden layers. The number of hidden
layers needs to be increased based on the complexity of the problem and the extent of nonlinear relationship between
inputs and target values. For the current problem of MAWPD assessment, best neural network architecture is found out
to be 5-18-12-1 with 2 hidden layers shown in fig 1. A performance goal of 0.001 was given during the training cycles of
10000 and more. During validation & testing, the neural network is tested with new input data values that have not been
supplied to ANN till now and outputs are evaluated as simulation results.
The predictions obtained with several architectures have been analyzed during post-processing.
3. PERFORMANCE EVALUATION OF ANN MODELS:
Post-processing involves the evaluating parameters such as MAPE (Mean Average Percentage Error) and
Coefficient of Determination (CoD) given in the equations (4) and (5). The network architecture shown in fig.1 had
given high regression coefficients during the evaluation process of the neural network. Aim was to deduce the smallest
and simplest neural network that works on faster optimization technique giving rise to minimization of the error within
the least possible epochs. MAPE should be less than 10% and CoD should be nearer to 1 for the neural network
architecture to qualify.
Fig. 1 Developed ANN architecture with Inputs, Outputs and Layers
| t j o j |2
RMSE (Root Mean Square Error) =
1/ 2
..(3) where tj and oj are input and output vector of
respective neural network layer.
tj
oj
100
j
MAPE =
... (4)
j Oj2
| t j o j |2
CoD =
o
j j
2
.. (5)
A curve fitting graph for the prediction of MAWPD for 36 values of testing is shown in fig 2.
4. RESULTS AND DISCUSSIONS:
Artificial neural networks learn on existing data of input-output pairs and perform prediction during the testing phase. A
multilayer ANN is established and the number of neurons used in the hidden layers is determined by experience. Input
response sensitivity has also been carried out so as to decide the most influencing input values for predicting the
MAWPD. Latitude, longitude, mean sea level, air density and mean annual wind speed have been used as input data and
MAWPD being the predicted output value. Various neural network models are evaluated for performance through trial
and error methods are tabulated below. The ANN with 5-18-12-1 has been found be the optimum architecture with tuned
performance parameters. Regression analysis characterizes the goodness of the neural network model adapted as a
whole. This analysis carried out to relate the actual and predicted data has shown that there is high correlation between
Fig 2. Curve Fitting graph of actual and predicted MAWPD values
actual values taken from the published IMD data and predicted values of MAWPD by ANN.
CoD indicates the amount of variation of actual and predicted values for the output - MAWPD.
Table 3. Evaluation of optimum ANN architecture
ANN
Architecture
5-18-1
5-15-9-3-1
5-18-12-1
Training
Cycles
20000
20000
20000
RMSE
0.00306
0.00304
0.00204
Average
of MAPE
14.73
10.65
10.21
CoD
0.7462
0.7867
0.9289
The lower value of CoD (nearer to unity) implies that the model has succeeded in the prediction of MAWPD values.
Noise in the experimental data during improper measurement or excessive interpolation of input-output data is captured
by ANN. Equal to unity values for CoD and lower values of MAPE can further be achieved by taking most of the
topographic factors such as vegetation height level, terrain complexity, temperature distribution across the height of the
mast etc. The separate input factor - Mast height with values of 20m, 25m, and 50m at each location has been found to be
less influencing in order to predict MAWPD through sensitivity analysis and hence opted out from input data.
This study demonstrates the use of artificial neural networks as a modeling tool for wind resource assessment &
forecasting. For assessing the wind energy potential of a site, wind speed vector data near by the site along with various
topographic parameters, atmospheric values and climatological data will be essential. Development of a global resource
assessment & wind energy forecasting model by considering such parameters is the futuristic need of wind energy
industry.
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