A Convolution Neural Network (CNN) based Deep Learning Neural Network Forecast Model for Wind Energy Prediction

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A Convolution Neural Network (CNN) based Deep Learning Neural Network Forecast Model for Wind Energy Prediction

K. Gunavardhan¹, Dr. I. Prabhakar Reddy², Dr. P. Sujatha³

1 Ph.D Research Scholar, JNT University, Ananthapuramu, A.P., India

2 Professor, Department of EEE, N.B.K.R.I.S.T, Vidyanagar, Nellore, A.P., India

3 Professor, Department of EEE, JNTU College of Engineering, Ananthapuramu, A.P., India

Abstract:

Renewable Energy resources are susceptible to the whim and vagaries of nature and are a variable random source of power. Predicting and forecasting the power from these variable power sources define and determine the operation of these system. The forecast of wind energy generation using a deep learning neural network called convolution neural network (CNN) is proposed in this paper. Here, the inputs taken are namely the wind energy, wind speed and angle of wind direction relative to the turbine blades, which are obtained from Sotavento Galicia experimental wind energy farm. The results were verified and validated against an actual generation and compared with ANN and ANFIS based forecast.

RMSE, NRMSE and Pearson coefficient are calculated for the same Keywords: CNN, Wind energy, ANN, ANFIS

1. INTRODUCTION

The carbon emissions continue to rise through economic activity, with India being the third largest emitter among individual countries. Therefore, adopting policies that aid in a speedy carbon removal from the energy mix reduce/end deforestation and conserve water resources, have gained national importance.

A draft agreement on climate change was accepted by India at Paris which means India has a daunting task to achieve sustainable development with its huge energy demand. For this, the most challenging step is to increase the renewable energy capacity to 175 Giga watts (GW), by 2022. Since the renewable energy is the key to a sustainable development, problems related to them have to be solved through proper policy and technical approaches. The greatest disadvantage of renewable energy resource is its dependence on the unpredictable nature which makes it a variable power source. However, any large scale power generation using renewable energy source must be able to meet the imperative criteria of being a stable and reliable power source. For this, hybrid power systems are an alternative option which provide feasibility in terms of technicality and are economically viable sources of clean power.

The kinetic energy produced with the movement of wind, is named as wind energy. It is one of the fastest growing renewable energy sectors in the country. India is blessed not only with abundance of sunshine hours; it also has 7517 km of coastline and territorial waters up to 12 nautical miles to sea. India being the 3rd largest in the annual wind power market, provides ample number of business opportunities for both domestic and foreign investors. India represents an annual growth of about 2.1 GW in regards to the new installations and hence is one the prevailing markets of wind industry. The last decade saw an average of 28% increase in the global wind markets, in terms of total installed capacity. IEA project India suggests that India needs a power generation capacity of 327GW by 2020. For this, wind energy should be produced close to 81TWh every year till 2020 and by 2030 it should be 174TWh. Owing to the intermittent nature and stochastic non-stationary of wind energy, the system operations are subjected to significant levels of uncertainty [1]. Therefore, in order to solve operational, planning and economic problems in the ever growing wind power scenario, accurate wind forecasts play an imperative role [1, 2].

Currently, there are two types of wind forecast methods in research, namely: point forecasts (deterministic forecast) [3, 4] and uncertainty forecasts [6,7]. Deterministic forecast emphasize on reducing the forecasting error and deliver specific amount of wind power [8]. However, the uncertainty information provided by the uncertainty forecasting method to the system operators; prove to be of importance for decision making process and electricity market trading strategies [9]. The existing approaches for

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predicting the wind energy can be divided into three categories namely, artificial intelligent model, physical model, and statistical model. Sometimes even hybrid model is chosen which combines the advantages of 2 or more approaches. Different types of wind speed and wind power are predicted by researchers by utilizing these forecasting approaches. The methods that use physical information like temperature, pressure and topography as inputs to predict the wind speed are termed as physical methods [12-14]. These methods prove to be essential for short term or very short term wind speed prediction [15].

Its disadvantage is that the necessary physical information may not be available to all market participants.

A set of wind speed from a particular site and its neighboring sites are used by spatial correlation methods [16-19] to predict the wind speed. Here, the measurement of the sites’ wind speed is difficult and is a major drawback of the same. Conventional statistical methods on the other hand, utilize only historical data to build prediction models. This also has a drawback that it cannot be utilized in forecasting the complicated nonlinear components in a given wind speed series.

Artificial intelligence methods [25-31] have gained popularity and have a higher precision in predicting wind speed than other methods for short term forecasting [32]. In this paper, historical data based comprehensive model that predicts the output of a wind energy system, is presented. The scope of prediction method based on convolution neural network (CNN) deep learning neural network for prediction of power output, is explored. Inputs taken are wind energy, wind speed and angle of wind direction relative to the turbine blades.

1.2. Determination on Input Variables for the Power Forecasting Model

For an accurate and reliable output power prediction model, an analysis of the factors that affect the power plant output is necessary. For this, the Pearson product-moment correlation coefficient (r) is used, which measures the direction and strength of the linear relationship between two variables. This coefficient helps in quantifying non-deterministic relationship between two variables. The PMMC value lies between -1and +1, where -1 refers to a total negative correlation, 0 refers to no correlation and +1 refers to total positive correlation. The PMMC between wind speed and power output is depicted in table 1. Here we see that the Pearson correlation of power and wind speed is 0.816, which indicates a high correlation between them.

Table 1. Pearson product-moment correlation coefficient between wind speed and power output.

Sr.No.

Wind Condition

Pearson Product-Moment Correlation Coefficient

a None 0 -0.09

b Low 0.1-0.3

c Medium 0.3-0.5

d High 0.5-1.0

2. CONVOLUTION NEURAL NETWORKS (CNN)

Multiple linear transformations when combined with non linear transformation of input data, results in a deep learning architecture, which yield more abstract and hence more useful representations. Owing to their outstanding performance in different computer vision and pattern recognition tasks [33, 34], they have gained popularity over the years. This is so because these deep layer architectures have been evolved from multilayer neural networks which make them competitive due to the inclusion of different design and training strategies. Spatial invariance, hierarchical feature learning and scalability have been incorporated in these strategies [35]. This approach is also a part of the learning process, which includes layers of deep learning architectures. These architectures assist in the search for an appropriate representation of input images in terms of multi-level visual feature maps that can be learnt [36].

The degree of freedom of models that operate on spatially correlated features can be reduced by the application of CNN. It is a neural network architecture that uses extensive weight-sharing for the same [37]. The three layers that form the CNN network are Convolution, max-pooling, and fully-connected.

One of the classic CNN arrangements includes alternating the Convolution and max-pooling layers,

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followed by fully-connected hidden layers. First, the Convolution layer is used for convolution of the input images with a learned kernel. Every Convolution layer posses same dimensionality as the input, however, each pixel is only activated by a region centered about that pixel (i.e., the kernel). Next, the max pooling layer performs an image down-sampling. The empirical results show that this down sampling yields better performance when the maximum in each sub region is taken rather than averaging, in most of the cases [38]. Apart from “summarizing” each sub-region, the network acquires some degree of translational and rotational invariance through max-pooling. Finally, fully-connected layer is used to encode the position-dependent information and more global patterns.

The weights parameters are updated by the deep CNN are shown in Fig. 1. Here, Stochastic Gradient Descent (SGD) is used. The reason for choosing SGD over ordinary gradient descent algorithm is that the weight parameters are updated by estimating the gradient over the full training set in the latter due to which SGD proceeds comparatively faster than the ordinary gradient descent algorithm. The SGD estimates the gradient by taking few examples or a mini-batch of the training set at a time unlike the ordinary gradient decent where the entire training set is taken to estimate the gradient. However, there may be difficulty in choosing an appropriate learning rate and its related decreasing scheme. Another factor that needs to be considered is the selection of an appropriate size for the mini-batch in SGD.

Fig. 1. Illustration of CNN

Fig. 2 shows two convolution layers on which CNN that has been constructed. Set (P, W, A) denotes wind power, wind speed and wind direction represented by the angle. Forecast at time t, wind power from t −3 to t−1 and wind speed and its relative angle from t−3 to t + 2 are used as raw input data. The raw input data should be reshaped to X under the requirement of matrix input. A 2 × 2 convolution kernel and 1 × 2 convolution kernel are used to extract a feature from the input data in series. [38].

Fig. 2. Diagram of the CNN for wind power forecasts [38].

For an m× n convolution kernel, the convolution layer formula is given as follows:

ℎ_𝑖 = 𝑠𝑖𝑔𝑚𝑜𝑖𝑑(𝑋_𝑚∗𝑛⊗ 𝑊𝑒𝑖𝑔ℎ𝑡𝑠_𝑚∗𝑛^𝑖 ) (1) 𝑆𝑖𝑔𝑚𝑜𝑖𝑑 (𝑢) = ¹

1+𝑒^−𝑢 (2)

Here ℎ_𝑖 denotes the output of the i^th convolution kernel, X_m*n denotes an m x n sub matrix of the input X, Weights 𝑖_𝑚∗𝑛 denotes the weights variable of the i^th convolution kernel and ⊗ denotes discrete convolution. After convolving layer by layer, a full-connected network is constructed to regress all the decomposed high-dimensional features to Pt. Thus, CNN regression training process can be summarized as the following optimization problem [38].

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√∑^𝑛_𝑡=1[𝑦_𝑡

𝜃=𝑤𝑒𝑖𝑔ℎ𝑡𝑠 ^𝑖

𝑚𝑖𝑛 − 𝐹(𝑋_𝑡,𝑖 , 𝜃)] ² (3)

Where yt denotes the target of the training set and the 2-norm of error vector is chosen as the objective function, Xt,i denotes the i^th kernel of the input at time t. The weights of the convolution kernel are optimized by using gradient descent method to construct the wind power point predictor.

3. DATA SET

In order to train the forecast for the wind energy system, the data is obtained from Sotavento Galicia experimental wind energy farm [39]. Sotavento Galicia, S.A. was established in 1997 with the support of the Government of Galicia. Here, wind speed and angle of wind direction relative to the turbine blades have been considered. The main technical details data are as listed in Table 2.

Table 2. Technical details of Sotavento Galicia experimental wind energy farm

Number of wind turbines 24

Different models 5

Power rating of the wind farm 17.56 MW

Average annual generation 33,364 MWh

Average wind speed at the site 6.41 m/s

total of 4344 data sets pertaining to hourly monitoring for a 181 day period (January 1, 2018 to 30 June 2018) are considered to be trained by the forecast system. Table 3 summarizes the details about the data base used in this study. The plot of average hourly wind speed during and the direction during the study period is illustrated using figure 3 and figure 4. The speed and frequency rise is shown in figure 5.

The plot of actual wind power generated is depicted in figure 6.

Table 3. Dataset utilized in this study

Data Sources Installed Capacity Sampling Data Measurement Item Total number of data points Sotaveno Galicia

experimental wind energy farm

17.56 MW Average values for 60-minute

(i) Wind generation (ii) wind speed (iii) Wind Angle

4344

Fig. 3. Plot of wind speed during the study period

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Fig. 4. Plot of wind direction during the study period

Fig. 5. Plot of speed and frequency wind rise during the study period

Fig. 6. Plot of energy generated during the study period 4. RESULTS

The forecasting model is coded using Matlab R 2016 and the simulations are run in a Pentium i7system with a RAM of 16 GB. In order validate the proposed model data over a 24 hour period was considered and compared with actual generation. The forecast for this period is tabulated using Table 4 and comparative plot between actual power generation and predicted power generation is illustrated using figure 7.

Table 4. Prediction for different wind speeds and direction for a 24 hour period S.No.

Wind speed (m/s)

Wind Direction ( degrees)

Actual Power Generated

Predicted Power ( MW)

% Error

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( MW)

1 7.2 236 12.877 11.93 7.35419741

2 8.36 233 13.803 12.12 12.1930015

3 8.25 234 14.623 13.98 4.39718252

4 8.48 235 15.325 14.72 3.94779772

5 10.63 233 15.802 15.11 4.37919251

6 9.87 236 15.641 15.09 3.52279266

7 9.51 237 15.095 14.45 4.27293806

8 11.67 231 15.259 14.87 2.54931516

9 10.97 231 14.47 13.93 3.73185902

10 8.66 244 15.03 14.22 5.38922156

11 8.85 244 15.227 14,86 2.41019242

12 9.9 254 15.7 15.12 3.69426752

13 11.18 256 15.789 15.16 3.98378618

14 10.97 255 15.521 14.92 3.87217318

15 10.18 255 14.808 14.01 5.38897893

16 10.34 256 14.805 14.03 5.234718

17 10.96 261 14.283 13.67 4.29181544

18 9.47 260 13.465 12.92 4.04753063

19 9.71 259 13.043 12.55 3.77980526

20 8.88 256 13.295 12.87 3.19669049

21 7.08 246 13.404 12.55 6.37123247

22 6.7 244 12.414 11.81 4.86547446

23 6.98 248 13.396 12.85 4.07584354

24 6.7 246 13.315 13.01 2.29064964

The data from the table shows 12.19% to be the highest error in prediction and 2.29% to be the lowest.

Also, the average error percentage is 4.55%. In order to measure the difference between predicted values and the actual values, a parameter called Root Mean Square Error (RMSE) is normally used. RMSE with respect to the estimated variable Xmodel is defined as the square root of the mean squared error

Fig. 7. Comparative plot of Actual and fore cast predictions 10

11 12 13 14 15 16 17

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Actual Predicted

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The individual differences between actual and forecast predictions are called residuals. These RMSE helps in aggregating the residuals into a single quantity known as predictive power. The square root of the mean squared error defines the RMSE of the model prediction with respect to an estimated variable Xmodel and is given as

(4)

Where Xobs is observed values and Xmodel is modeled values at time/place i. For the proposed prediction model for the data tested the RMSE is calculated to be at 0.70 MW. The performance of the proposed forecasting model has also been evaluated using normalized root mean square error (NRMSE). It can provide the comparative analysis for different installed-capacity cases and is defined by

𝑁𝑅𝑀𝑆𝐸 = 100 ∗ √¹

𝑁 ∑ (^𝑃^𝑎^𝑖^−𝑃^𝑓^𝑖

𝑃𝑖𝑛𝑠𝑡𝑎𝑙𝑙)

𝑁𝑖=1 2 % (5)

Here, P install,= installed capacity, Pa,= actual power output , Pf =power forecast value and N = total number of samples. The NRMSE calculated for the proposed predicted model using the data tested equals 4.00%.The correlation coefficient gives an idea about the strength and direction of a linear relationship between two variables like model output and observed values. One of the most used coefficients is the Pearson product-moment correlation coefficient (also called Pearson correlation coefficient or the sample correlation coefficient). It is obtained by dividing the covariance of the two variables by the product of their standard deviations. The Pearson product-moment correlation coefficient for a series of n observations and n model values is used to estimate the correlation between model and observations and can be written mathematically as













 

n

i i

n

i i

n

i i i

y y x

x

y y x x r

1

2 1

) (

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For a perfect increasing linear relationship the correlation is +1. For a perfect decreasing linear relationship, the coefficient is -1. If the values are somewhere in between these values, then it indicates the degree to which the two parameters have a linear relationship. If there is no linear relationship, the coefficient value will be 0. The Pearson coefficient for the proposed forecast model is calculated to be 0.971 indicating a very high degree of correlation between the observed and the predicted value. The performance measures for the proposed model for the test data is tabulated using table 5. From the table it can be observed that the proposed model delivers an acceptable performance as indicated by the metrics.

Table 5: Performance metrics

1 Maximum Error % 12.19

2 Minimum Error % 2.29

3 Mean Error % 4.55

4 RMSE ( MW) 0.70

5 NRMSE (%) 4.00

6 Pearson Correlation (r ) 0.971

The performance of the proposed model is validated for the given data test, by comparing ANN based forecast and ANFIS based forecast. The ANN model-1 used a Feed Forward Back Propagation Neural Network while the ANFIS model was trained using a hybrid approach. On account of their limitation both the models were trained using a reduced number of data points. A truncated data set of 2400 points has been used for training both the models. The results of the forecast in comparison with the proposed approach are tabulated using the table 6. Table 6 clearly shows that the proposed approach has a better performance than ANN and ANFIS based forecast.

Table 6: Comparison of the proposed approach with ANN & ANFIS n

X RMSE X

n

i obsi modeli



 

 ¹

2 ,

, )

(

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S.No. Parameter

Proposed Approach ( CNN)

ANN ANFIS

1 Mean Error % 4.55 7.89 7.21

2 RMSE ( MW) 0.70 1.25 1.09

3 NRMSE (%) 4.00 6.23 5.96

4 Pearson Correlation (r ) 0.971 0.925 0.936 5. CONCLUSIONS

As the search for clean energy continues to grow, wind energy systems will be one of the most important contributors towards delivering the required energy. In this research work CNN based wind energy forecast model has been successfully implemented and presented. It can be inferred from the results that the prediction accuracy is high .This fact is further supplemented through the results of the analysis using, RMSE, NRMSE percentage and Pearson correlation coefficient. CNN based forecast delivers better results when compared to ANN and ANFIS based forecast. The various outputs of the forecast models can be used in different applications like inputs for energy management system used for hybrid PV systems.

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