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. REFERENCES [1] Rajendra V Kharul, Wind resource assessment in India- a critical review, WISE Publications, Pune, India 34-65 (2006). [2] John Jack, Over view of Wind Energy Forecasting. WISE Publications, Pune, India 191-218 (2006). [3] CWET.2001.Wind Energy Resource Survey in India Vol.6. Chennai : Centre for Wind Energy Technology [4] Endlich R.M 1984, “Wind energy estimates by use of diagnostic model. Boundary layer meteorology 30: 375385 [5] Consolidated energy consultant Ltd., Bhopal www.Windpowerindia.com [6] Kavita Burse, Manish Manoria, Vishnu P. S. Kirar, ”Improved Back Propagation Algorithm to Avoid Local Minima in Multiplicative Neuron Model”, World Academy of Science, Engineering and Technology 72 2010 [7] S. C. Ng, S.H. Leung and A. Luk, “Fast convergent generalized back propagation algorithm with constant learning rate”, Neural Processing Letters, vol. 9, pp. 13-23, 1999. [8] Shikha Singh; T. S. Bhatti; and D. P. Kothari, "Wind Power Estimation Using Artificial Neural Network", DOI: 10.1061/ASCE0733-94022007133:146, Journal of Energy Engineering, Vol. 133, No. 1, March 1, 2007. [9] www.inwea.org [10] Webinar series, MATHWORKS, Wind Resource Assessment – Data Analysis Using MATLAB.