[ieee 2010 2nd international conference on computational intelligence, communication systems and...

Weather Forecasting Using Photovoltaic System and Neural Network

Iza Sazanita Isa, Saodah Omar, Zuraidi Saad, Norhayati Mohamad Noor, Muhammad Khusairi Osman

Faculty of Electrical Engineering, Universiti Teknologi MARA (UiTM) Malaysia,

Jalan Permatang Pauh 13500 Pmtg. Pauh, P. Pinang, Malaysia. Email: [email protected]

Abstract – This paper presents the applicability of Artificial Neural Network (ANN) for weather forecasting using a Photovoltaic system. The main objective is to predict daily weather conditions based on various measured parameters gained from the PV system. In this work, Multiple Multilayer Perceptron (MMLP) network with majority voting technique was used and trained using Levenberg Marquardt (LM) algorithm. Voting technique is widely used in many applications to solve real world problem. Different techniques of voting are used such as majority rules, decision making, consensus democracy, consensus government and supermajority. The way of the voting technique is different depending on the problem involved. Majority voting technique was applied in the study so that the performance of MMLP can be approved as compared to single MLP network. The proposed work has been used to classify four weather conditions; rain, cloudy, dry day and storm. The system can be used to represent a warning system for likely adverse conditions. Experimental results demonstrate that the applied technique gives better performance than the conventional ANN concept of choosing an MLP with least number of hidden neurons.

Keywords – Forecasting, MMLP Neural Network, photovoltaic system, voting technique.

I. INTRODUCTION

Research on weather forecast using Artificial Neural Network (ANN) has widely discussed among researchers. Ghanbarzadeh [1] utilized the air temperature, length of day, relative humidity and sunshine hour values as input in Feed Forward Neural Network (FFNN) for the prediction of global solar radiation (GSR). Li and Nin [2] proposed Markov Chain model to forecast the power generation of photovoltaic (PV) system but applicable only in the short and medium term capacity forecast. The result showing that Markov theory is feasible as due to the theoretical calculations which are very close to the actual results. Chaouachi et al [3] compared several ANN models such as multilayer perceptron (MLP), radial basis function (RBF), Recurrent Neural Network (RNN) and Neural Network Ensemble (NNE). Neural Network Ensemble to forecast 24 hour ahead of solar power generation for PV system located in Tokyo University of Agriculture and Technology (TUAT). From the experimental results, it is shown that the NNE achieved a higher forecasting accuracy as compared to MLP, RBF and RNN with the fact that NNE improved the generalization and noise tolerance of learning system. Yona et al [4] proposed a RNN combined with Elman Network and compared to FFNN to forecast short-term-ahead power

of PV system. The results obtained showing that forecast errors are greatly minimize by RNN as compared to FFNN. Yona et al [5] have further continued the research on applying RBFNN to be compared with RNN and FFNN. RBFNN is chosen for its structural simplicity and universal approximation property. From the simulation results, it’s shown that RBFNN and RNN outperform the result of FFNN. The validity of the proposed NN is confirmed by 24hours forecasting simulation.

Another approach proposed by Sharma and Manoria [6] using the concept of Adaptive Forecasting Model (AFD) has potential to capture the complex relationships between many factors that contribute to certain weather conditions. Main parameter measured was atmospheric pressure and others as secondary parameters were atmospheric temperature and relative humidity. Experiments were carried out on different locations of different city and the stations with short different from the sea level of altitude having the best performance. Experimental results showed that the neuro-fuzzy prediction model has ability to indicate trend of the future weather of the place based on key features in atmospheric pressure patterns. A research by Oleg et al [7] proposed pattern recognition technique using the meteor code as associative reconstruction of missing data to give the weather forecast. The testing phase of the pattern recognition for true forecast probabilities is implemented using Hamming metric recognition algorithm. This approach was simulated different incomplete sets of meteodata of the decade-mean observations for precipitations, day and night temperatures. The results were achievable up to 91% as significant to probabilities of true forecasting on the different data sets.

II. IMPLEMENTATION

The objective of this study is to forecast daily weather conditions based on various measured parameters gained from the PV system, as shown in Figure 1, which is located at Universiti Teknologi Mara (UiTMPP) Kampus Pulau Pinang. Data recorded by the PV system consist of date information combined with solar radiation, ambient temperature, current, surface temperature, voltage, wind direction and wind speed data from 22 August 2009 to 22 January 2010. Recorded data were used for training and testing phase with backpropagation algorithm in MMLP network.

In this paper LM algorithm is adopted for updating each connection weights of units and sigmoid activation function

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applied in the learning process. Sigmoidal activation functions are popular in learning process and commonly used in MLP because there are differentiable. LM algorithm has been used in this study due to the reason that the training process converges quickly as the solution is approached.

Figure 1 Photovoltaic system located at UiTMPP campus.

A. Artificial Neural Network and Training Algorithm

Theoretically, the conventional ANN only selects one MLP, which gives the highest performance after the training phase, to represent a problem [8]. For example, Figure Figure 2 shows the illustration of a MLP network performance after a training stage. The MLPs with hidden neurons 7, 9 and 10 give the same highest performance compared to others. Usually, based on ANN theory, the MLP with the least number of hidden neurons, but with the best performance will be considered to represent the problem. Hence, for this example the MLP with 7 hidden neurons will be chosen to represent or solve the problem. The other two MLPs (i.e. 9 and 10 hidden neurons) will be omitted although they also give the highest performance which means that they may also have the same or even better performance at representing the problem when presented with new sets of data.

Figure 2 Performance of MLP network

The conventional ANN theory may retrain an MLP from obtaining a better performance and hence less accurate results are obtained. For this analysis, all networks that give the highest performance during training phase will be

selected to be a multiple MLP network. The main objective of this project is to prove that multiple MLP network will give a better performance compared to individual MLP network.

B. Feed Forward ANN Arrangement of neurons into layers and the pattern of

connection within and in between layer are generally called as the architecture of the net. The number of layers in an ANN is defined as the number of layers of weighted interconnected links between neurons. Thus, a two layer ANN has two layers of interconnected weights. One architectural structure of an ANN is feed forward, either multilayer or single layer. Figure 3 shows a multilayer feed forward architecture consisting of the input, hidden and output layers. Data enter at the inputs and passes through the network, layer by layer, until it arrives at the outputs. The hidden layers act as an interconnection between the input and output layers. The network only allows input to flow in a forward direction. The hidden layer is the main processing layer which allows the network to solve non-linear problems [10, 11].

Figure 3 Multilayer feed forward ANN

C. Multilayer Perceptron The MLP network is a supervised feed forward ANNs

and it is probably the most considered model of the ANN family [12]. The architecture of the MLP is shown in Figure 3. This type of ANN has an output layer, an input layer and one or more hidden layer. The number of hidden layers can be changed depending on the complexity of a problem. The output nodes also vary depending on the target output types of classification. Research has found that more than two hidden layers may be rarely needed to perform complicated mapping [13].

The layers are interconnected by the pertained weight the input layer receives the data from the outside as the input of ANN. The input layer directly passes the data through the hidden layer. The hidden layer which consists number of hidden neurons receives the input data, processes them and send the output from hidden layer through weight links to the output neurons.

%Correct Classification Vs Hidden neurons

0102030405060708090

100

1 2 3 4 5 6 7 8 9 10 11 12 13Hidden neurons

% C

orre

ct C

lass

ifica

tion

% CC

979797

Figure 4 MLP with one hidden layer

The output from the each hidden node, Uj is given by,

;1

11ik

ijiijj bXWU for kj1 (1)

The output of the thj neuron of the thk hidden layer is given by,

;)(1 1

112^ i ik

j

k

ijiijjkk bXWWty for

nk1 (2)

Where ijW = weights connecting with the input with the

hidden nodes

jkW = weights connecting the hidden with the output nodes.

The MLP weights are updated using,

ijijij WtWtW )1()( (3)

Where )(tWij = Current weight

)1(tWij Previous weight

ijW Weight update

The goal of the network is to train the MLP to achieve a balance between the ability to respond correctly to the input patterns that are used for training and the ability to provide good response to the input that are similar. Back propagation learning is one of the most important types of learning in feed forward network. It is a systematic method for training a multi layer network such as MLP. Back propagation provides a computational efficient method for changing the weight in a feed forward network with differentiable activation function units to learning input output data.

III. PROPOSED SYSTEM

The purpose of using neural network is to be able to classify the data that are too complex for the traditional statistical models. This project has chosen MLP network as the classifier. MLP network are feed forward neural networks with one or more hidden layers, Cybenko and

Funahashi (1989) have proved to be a good function approximation. This means that one hidden layer MLP is almost always sufficient to approximate any continuous function up to certain accuracy [8]. The advantages of MLP are, their abilities to learn and give the better performance especially in the case of classification are proven. In additions the construction of MLP is simple. The ability of an MLP network to classify data efficiently and make decisions based on the classification results is one of the distinguishing features that resemble human intelligence. The MLP network has to train before to perform specific task with less error. Theoretically [9], a single MLP network with the best performance at the fewest number of hidden neurons will be selected as the best ANN to represent a problem. To show that the other MLP networks also have the best performance [14], applications of neural networks for data classification, this project proposes the use of a multiple MLP network with majority voting. Figure5 shows the proposed system.

Figure 5 Schematic diagram of proposed system

When the data come with more attributes, classifications of the problem data become more complex. The neural network will help the process of classification to become more accurate. A voting technique is used, in order to vote for a final output from a multiple MLP networks.

A. Development of Multiple MLP(MMLP) Number of the input layer depends to the number of

features from meteorological data (7 features). The number of hidden neurons decided upon training stage of the MLP networks. Two output neurons are needed to classify the class of the target output. The MLPs networks are train by using meteorological data that has been divided into three proportions (training, validation and testing). The best performance of the MLP network during testing phase then selected to perform individual (MLP) and multiple MLP (MMLP) network. When giving a set of new testing data, the best MLPs network then tested individually the performance of the network. The performance of the best networks with the new data set then recorded. The same data set during testing the network individually is used to perform MMLPs network. All the highest performance of the MLPs network during training the network is selected to perform the MMLP network. Each of the MLP network in the system of MMLPs network will produce individual output. The final output from each data of the MMLPs network will be decided when voting technique take place.

989898

B. Implementation of Voting Technique Majority voting techniques are used to perform the final

output of the data given. The voting technique present by selecting the majority output from the MMLP network. The triangular waveform and transport data classification has three class of the output. The voting techniques become difficult when the MMLPs network produce equal output during majority vote. To overcome the problem, confidence level of the output data are calculated. The way of the confidence level calculated is different depends to the output neuron of the output layer. An example to calculate the confidence level for triangular waveform is shown in Table 1. The outputs of each MLPs network after simulation process are shown below. Given two of the best performance MLPs network is produce during testing phase.

TABLE 1. EXAMPLE OUTPUTS FROM DIFFERENT MLP NETWORKSNo. of MLPs

Network MLP 1 MLP 2

Class 1 2

Simulation Output 0.25 0.83 0.82 0.08 Converted Output 0 1 1 0

The outputs show that the voting process results in equal number of vote for two classes, [0 1] and [1 0]. In this condition the voting technique cannot decide upon the final output for the data. To overcome this problem, confidence level for class 1 and class 2 is calculated using the equations,

21]00[.

BitOutputSimBitOutputSimclasslevelConf (4)

)21(1]10[.


2)11(]01[.


For the above problem, 38.0)85.01(23.0]10[. classConf 27.008.0)81.01(]01[. classConf

To decide upon the final output form the confidence level, Equation 4 is the final output when equal vote occur between class [0 0] and [0 1].

else

classConfclassConfif

OutputFinal

1]10[.]00[.0

(7)

If equal vote occurs between class [0 0] and [0 1]. Equation 5 gives the final output,

Equation 6 gives the final output when equal vote occur between class [0 0] and [1 0].

else


OutputFinal

2]01[.]00[.0

(9)

From the problem above, equal vote occur between class 1 and 2. Referring to the result of confidence level that has been calculated earlier, the confidence of the class 2 is lower than class 1. Therefore the final output of the sample data is class 2.

IV. RESULT AND DISCUSSION

The forecasting performance will be evaluated in terms of forecasting error, defined as the different between the actual and the forecasted values. Table 2 presents inputs for MMLP network based on the meteorological data distribution of solar radiation by PV system from 22 August 2009 to 22 January 2010. Maximum solar radiation recorded was 1350 Wm-2 per day and 0 Wm-2 indicated night day. Others characteristic of the input parameter include the values of daily wind speed, ambient temperature, wind direction, surface temperature, voltage and current. The four different categories defined for the ANN network of weather forecasting are raining, cloudy, dry day and storm.

TABLE 2. MAXIMUM AND MINIMUM VALUES OF PARAMETERS FOR NORMALIZATION

Input Parameters Xmin Xmax

x1Ambient temperature 23.50C 34.90C

x2 Current 0A 27A

x3Solar radiation 0 Wm-2 1350Wm-2

x4Surface temperature 28.20C 37.10C

x5 Voltage 0.2V 26.2V

x6Winddirection 00 3600

x7 Wind speed 0m/s 7 m/s

The best MLP network trained with the LM algorithm has been selected to be integrated in the voting technique. The best MLP networks are those with hidden neurons 4, 7 and 8. A total of 200 data were used to test the individual MLPs and the MMLP.

Table 3 shows the results the best performance is given by MLP with hidden neurons 4 with percentage of correct

else


OutputFinal

2]01[.]10[.1

(8)

999999

classification (%CC) is 99.6% and followed by the MLP with 2 hidden neurons and 3. After the voting process, the MMLP network gives the same %CC as the MLP with 8 hidden neurons. This result shows that the performance of the MMLP network could be better than a single MLP network. The result demonstrates that the applied technique gives better performance than the conventional ANN concept of choosing an MLP with least number of hidden neurons.

TABLE 3: COMPARISON PERFORMANCE BETWEEN MLP AND MMLP No hidden neurons %CC MLP %CC MMLP with

voting 2 99.40

99.80 4 99.60

V. CONCLUSION

This paper proposed the weather forecast by using PV system based on several parameters using MMLP network. This approach does not require complicated calculations and the mathematical model but only the meteorological data. Moreover, MMLP is applicable to forecast weather with voting technique that has been developed in order to investigate the proposed concept. This was done by selecting all the best MLP networks which give the highest performance during training phase, to be included a multiple MLP system. Then a new set of unseen data have been used to test the multiple MLP network with voting. The results are then compared to all selected the single MLP networks. The work has successfully developed a multiple MLP system and implemented a voting technique. The results show the feasibility of the applied MMLP system with voting technique. Further development such a proposed system could be tested for different types of classification data, such as for control, chemical and manufacturing processes. The success of the system in other classification tasks could further prove the superiority of the new approach compared to the conventional ANN concept. The proposed multiple MLP system can also be applied for prediction and approximation tasks. For these tasks, new voting techniques need to be formulated.

REFERENCES

[1] A.Ghanbarzadeh, A.R.Noghrehabadi, E.Assareh, and M.A.Behrang, “Solar radiation forecasting based on meteorological data using artificial neural networks”, 7th IEEE International Conference on Industrial Automatics (INDIN 2009), pp.227-231, 2009.

[2] L.Ying-zi and N.Jin-cang, “Forecast of power generation for grid-connected photovoltaic system based on Markov Chain”, Proceedings of IEEE, 2009.

[3] A. Chaouachi, R.M.Kamel, R.Ichikawa, H.Hayashi and K.Nagasaka, “Neural network ensemble-based solar power generation short-term forecasting”, World Academy of Science, Engineering and Technology 54, pp. 54-59, 2009.

[4] A.Yona, T.Senjyu, and T.Funabashi, “Application of Recurrent neural network to short-term-ahead generating power forecasting for photovolataic system”, Proceedings of IEEE, 2007.

[5] A.Yona, T.Senjyu, A.Y.Saber, T.Funabashi, H.Sekine and C.H.Kim, “Application of neural network to 24-hour-ahead generating power forecasting for PV system”, Proceedings of IEEE, 2008.

[6] A.Sharma and M. Manoria, “A weather forecasting system using concept of soft computing: A new approach”, Proceedings of IEEE, pp. 353-356, 2006.

[7] V.D.Oleg, V.A.Lykov and S.A.Terekhoff, “Artificial neural networks in weather forecasting”, Proceedings of IEEE, pp. 829-835, 1992.

[8] M. Y. Mashor, “Performance Comparison Between Back Propagation, RPE And MRPE Algorithms For Training MLP Network.” School of Electrical and Electronic Engineering, Universiti Sains Malaysia, (2002).

[9] S. N Sivanandam, S. Sumathi & S. N. Deepa, “Introduction to Neural Network Using Mathlab”, India: Mc Graw Hill, (1998).

[10] S. Kumar, “Neural Networks: A Classroom Approach”, Singapore: Mc Graw Hill, 2005.

[11] H. Tang, K. C. Tan and Y. Zhang, “Neural Network: Computational Models and Applications”, Berlin: Springer, 2007.

[12] S. Haykin, “Neural Networks: A Comprehensive Foundation. New Jersey”, Prentice Hall, 1999.

[13] Y.Zhang, G.P.Chen, O.P.Malik and G.S. Hope, “A Neural Network Based Adaptive Power System Stabilizer”. IEEE Transaction on Energy Conversion, Vol. 8, No.1, 1993.

[14] S.Omar, “Multiple MLP system with voting technique for improved classification accuracy”, MSc Thesis, Universiti Sains Malaysia, 2008.

[15] S.Omar, Z.Saad, MK Osman, I.S.Isa, and J.M.Salleh, “Improved Classification Performance for Multiple Multilayer Perceptron (MMLP) Network Using Voting Technique”, Asia Modelling Symposium 2010.

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