iJournals: International Journal of Software & Hardware Research in Engineering ISSN-2347-4890 Volume 6 Issue 6 June, 2018
“A comparative study of Forecasting Solar irradiation of Indore City Using Different Machine Learning techniques” Mr. Arshad Sarfuddin1, Mr. Ashish Tiwari2 1
M. Tech Scholar in Power System VITS, Indore (M.P), India. E-mail: (arshad.sarfuddin18@gmail.com) 2
Assistant Professor in CSE Dept., VITS, Indore (M.P), India. (ashishtiwari205@gmail.com)
Abstract — In this work a study is done on understanding the role of ANN in predicting hourly solar radiation value of any location. What make predicting solar radiation important is the fact that solar energy has a great potential along with wind energy to fulfill energy demands of growing India. By looking at past decades record a continuous growth can be seen in new plants installation every year. This is due to the supportive Government policies and interest of investment companies. The other factor which catalyzed this process is the new development in technology which is making it cheap and reliable source of energy to compete with conventional source of energy. India itself is expected to have installed around 10 GW of solar plants in 2017 which is a rise of about 70% than that of 2016 which is a great leap. Also, after this year India is third biggest solar market globally. Artificial Neural Network can help in further improving reliability of solar energy. It is used in this study to predict 1 day ahead solar radiation so that an estimate of amount of energy that can be generated using solar Photovoltaic cell so that further planning can be done. The forecasting accuracy of Artificial Neural Network models is found to be dependent on input parameter combinations, training algorithm and architecture configurations. MAPE, MAE and MSE are considered as performance parameters for evaluating predicted results. Keywords: Solar irradiation, Solar PV generation, Artificial Neural Network, Artificial intelligence, Backpropagation, mean absolute percentage error, MATLAB. I.
INTRODUCTION
The objective of this study is to relate the different backpropagation training algorithms in their capability to figure out a solar radiation forecasting model, and then the choice of the most appropriate training algorithm is made to train the model particularly for multistep 24 Hour ahead solar radiation forecasting. The used datasets
are based on the past weather records for the Indore city of Madhya Pradesh in India from www.meteoblue.com website where data is openly available for all. The data used in this study is dated between 9th July 2017 to 11th Jan 2018. All input and out parameters have per hour readings, so a total of 4512 samples of each parameter are used in study. Samples are for each of the following 6 features; those are Atmospheric Pressure, Humidity, Precipitation, Temperature, wind speed and solar radiation value. First some samples are used as the training dataset and the latter remaining samples are the test dataset to estimate the models. For this purpose, statistical measures such as MSE and MAE are assumed to check the effectiveness of results. The proposed methodological background is applied on this study.
II. LITERATURE REVIEW Many works have been done in past in search for an algorithm or model which can solve the problem of forecasting solar irradiation accurately. Some of the latest work has been discussed in this section below: Ravinesh C. Deoa, Mehmet Şahin in [1] discussed how prior knowledge of solar radiation will be very helpful in may engineering application, especially in predicting energy generation from solar pv system and in site selection for solar power plant. Author concluded that it is due to various fiscal issues and policies which drive this study to find a method which can provide input parameters for the sake of predicting solar radiation using model which is cheap easily available and maintenance free. This study mainly focused on preparing model using ANN which is capable of predicting solar irradiation with the help of MODIS-LST as input parameters. The data utilized are for the location of Queensland. For calculating monthly average, a total of 55 neuron architecture are used while nine neuron architectures are
Mr. Arshad Sarfuddin, Mr. Ashish Tiwari, vol 6 Issue 6, pp 27-32, June 2018
iJournals: International Journal of Software & Hardware Research in Engineering ISSN-2347-4890 Volume 6 Issue 6 June, 2018 used for time-lagged LST. Two type of training work authors have developed a Complex Valued Wavelet algorithm are used SCG and LM. For comparison Network (FCWN) for this purpose. The data for training purpose author has forecasted value using two more and testing this improved wavelet NN is meteorological statically method i.e. ARIMA and MLR. On evaluating data of Tamanrasset city, Algeria. Both daily and hourly results author came to conclusion that ANN results solar irradiation value is forecasted and the results shown outperform Other methods with a far margin. This work to has significant improvement. ascertains that an ANN model coupled with satellitederived LST data can be adopted as a qualified stratagem III. SOLAR ENERGY STATUS for the proliferation of solar energy applications in locations that have an appropriate satellite footprint. It has been clearly seen that Indian economy is continuously growing and is still expected to remain on Maria Malvoni, Paolo Maria Congedo, Maria Grazia de the same path unlike Global economy which is following Giorgi in [2] proposed that the continuous altering nature a low-level Growth. of weather directly affect the PV cell output. In this work author has checked the power of data driven techniques The main reason behind this is consider to be the power in which input data are preprocessed before supplying to sector of India which is rising at fast rate. As on 30-09model. Author has developed model to forecast day 2017 the total installed capacity of India has reached to ahead pv power generation using meteorological data 330 GW. This power cumulative contribution of different used as inputs along with Wavelet analysis and other sources. Thermal is still the prime source of energy with mathematical tools. For the sake of forecasting a day a total installed capacity of 218 GW contributing to ahead PV power generation a time series forecasting around 66% of total power. Then comes share of method as the GLSSVM is used which is the renewable energy sources which contribute to around combination of LS-SVM and GMDH is applied. 60GW which is around 18% of power in India. Hydro power on the other hand contributes to around 45GW of Cyril Voyant, Gilles Notton, en. al. in [3] has power generation which is about 13.5% and remaining propounded that having a prior knowledge of output of power is generated using Nuclear power which is about solar pv system will be very helpful in efficient 2% of total generation. It is evident that the renewable management of power grid. There are many techniques power has secured 2nd position after Thermal and is which are developed for forecasting solar irradiation spreading its wings rapidly in India. [18] which can be roughly classified as physical methods and At present Indian government has made a target to Neural network methods. Author mainly focused on achieve a install capacity of renewables in India to about different intelligent techniques and their comparison is 175 GW by the year 2022, out of which the main made. Author mainly focused his study on hybrid contributor will be solar power which will contribute to model’ s regression tree, random forest, gradient around 65% of total capacity due to high potential of boosting and many others to check their efficiency. solar power in Indian subcontinent. Daniel O’ Leary and Joel Kubby in [4] presented a new approach based on image processing and ANN for solar power prediction for supporting small scale energy markets and individuals. Hence for the aim to reduce losses and expenses of producing solar power without affecting natural elements for both commercial and domestic fields require a good forecasting system. In this study author has utilized image edge detection technique for the acoustic signal classification. This data has improved the results of forecasting significantly over traditional ANN model. The results obtained have an MAE of 5.37% and RMSE of 6.83% which is far better that previous techniques. L. Saad Saoud, F. Rahmoune, V. Tourtchine, K. Baddari in [5] has proposed that due to importance of forecasting solar irradiation in planning pv power plant it is very necessary to develop a good forecasting model. In this
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Figure 1 Source wise Power Installed Capacity as on 31.12.2017.
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iJournals: International Journal of Software & Hardware Research in Engineering ISSN-2347-4890 Volume 6 Issue 6 June, 2018 decision taking methodology and deals with vague and IV. METHODOLOGY: imprecise information unlike classical set where precise Following techniques are utilized in present work for the and clear information is used for decision making. Fuzzy purpose of fault location estimation: unlike Boolean logic where things can be either 0 or 1 i.e. true or false represent value with the degree of truth. Artificial Neural Networks (ANN): This fuzzy logic is applicable to all real-world problems because in none of them the boundary is clear. The value ANN are computing systems or technique that mimic in fuzzy logic comprises of value between 0 & 1 the learning processes of the brain to discover the Including two. relations between the variables of a system. They process input data information to learn and obtain knowledge for forecasting or classifying patterns etc. type of work. ANN consists of number of simple processing elements called neurons. All information processing is done within this neuron only. Levenberg–Marquardt (LM) Algorithm: Reducing error function is the main reason to use this algorithm. Levenberg-Marquardt algorithm [8] [9] is a very efficient technique for minimizing a nonlinear function. The algorithm includes many different variables like in present study we have output data, weight between neurons and error function, that determine efficiency and success rate of model. The ideal values of these variables are very dependent on the test function. Levenberg-Marquardt algorithm is fast [10] and has stable convergence. When the performance function has the form of a sum of squares, which contains first order derivatives of the network errors with respect to the weights and biases, � is a vector of network errors. The Jacobian matrix can be computed through a standard backpropagation technique that is much less complex than computing the Hessian matrix. [11] The following is the relation for LM algorithm computation,
Fuzzy logic was proposed in the USA by Prof L. A. Zadeh, in the early 1960s. FL is a superset of conventional (Boolean) logic that has been extended to handle the concept of partial truth-truth values between “ completely trueâ€? and “ completely falseâ€? . Lofti A. Zadeh, who is considered as father of fuzzy logic introduced fuzzy logic in 1965 in his research paperâ€? fuzzy Setsâ€? Fuzzy logic is a way to make machines more intelligent, enabling them to reason in a fuzzy manner like humans. Fuzzy models “ thinkâ€? the way humans do and include verbal expressions instead of numbers. This method mimics the way operation of expert’ s opinion over any decision making. The structure of neural network both biological and artificial is such that each neuron is connected with other neuron through a link. This link holds an information in the form of what we call weights based upon the relationship between input and output data provided to model. Now we will discuss in this section how fuzzy logic can be beneficial to be used in Neural network: • Fuzzy logic is used mainly to optimize weights based on fuzzy set theory. • Fuzzy is used when conventional set theory can’ t be used. • Training validation and learning help neural networks do well in unpredicted circumstances. In those cases, fuzzy values would be more applicable than convention binary values.
(1) where I is the identity matrix, Wk is the current weight, Wk+1 is the next weight, ek+1 is the current total error, and ek is the last total error, Îź is combination coefficient. It tries to combine the advantages of both the methods hence it inherits the speed of the Gauss– Newton algorithm and the stability of the steepest descent method. Fuzzy Logic:The word “ fuzzyâ€? usually used for cases in which there is no clear answer or boundary like there is a vague situation. Fuzzy logic resembles the human
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Figure 2: Working model of an ANN
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iJournals: International Journal of Software & Hardware Research in Engineering ISSN-2347-4890 Volume 6 Issue 6 June, 2018 The Neural Network in present study consist of three which are physically not possible e.g., Solar radiation layers. The first one is Input layer (consist of 4 Neutrons value less than zero. for 4 input element) from where inputs are feed to model VI. RESULTS for further computation. Then comes second layer called Hidden Layer (consist of 10 Neurons), this is where Above data is used to forecast solar radiation for 48 hr activation function is used to limit value of output ahead time-scale using two different back propagation Neuron. At last we have output layer (1 Neuron) form training algorithms separately. Results of each algorithm which we take output result for comparison with actual is discussed below for various hidden layer neurons, and result to calculate error and the feeding it back to model finally various detailed performance and output plots are to vary weight accordingly for improving Performance. shown for the state at which best output is obtained.
V. DATA HANDLING AND PRE-PROCESSING
Results for Levenberg-Marquardt (LM) training function The following study is done using MATLAB Environment. In MATLAB, the command used for training network using Levenberg-Marquardt backpropagation algorithm is “ trainlm” .
To demonstrate the effectiveness of the proposed approach, publicly available hourly data of various weather Parameters from www.meteoblue.com website has been taken to forecast the hourly solar radiation for the Indore city of Madhya Pradesh in India.
The performance factor MAE is noted down for each network for further evaluation. Number of epochs and overall regression is shown in following table as their value also have certain significance and it also changes with change in number of hidden layer. Following figures will describe in detail the various network performance factors at this point.
The data used in this study is dated between 9th July 2017 to 11th Jan 2018. All input and out parameters have per hour readings, so a total of 4512 samples of each parameter are used in study. The below table depicts a sample of used data sheet in the present study. All columns other than one with Solar radiation which is placed at last are used as input data for training model. Table 1: Sample of utilized Data Sheet for the present study. Date
Hour Temp. Humidity Pressure
Wind Speed
Solar Radiation
09-07-17
0
25.61
76
1005.7
23.42
0
09-07-17
1
24.77
82
1005.6
23.4
0
09-07-17
2
24.15
85
1005.1
23.04
0
09-07-17
3
23.64
88
1004.8
22.32
0
09-07-17
4
23.2
89
1005
22.68
0
09-07-17
5
23.14
89
1005.4
24.16
0
09-07-17
6
23.24
90
1005.7
23.93
16.91
09-07-17
7
24.28
86
1005.9
24.15
120.15
09-07-17
8
25.68
81
1006.2
25.42
228.73
The discrepancies observed in the given datasets are removed and the resulting refined time-series for each of the weather parameters is then ready for use. The refining steps of the data for the present work is below: • Find all the slots having missing parameter values (empty slots) and replace them with “ NaN” (Not a Number). Empty slots may be because the measurement instrument was not working for any reason. • Filtering the data by comparing the recorded values with rated values and eliminating out-of-range values i.e.
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Figure 3: Comparison between the predicted and actual solar radiation employing the proposed model using LM training.
The above plots show the forecasted radiation using proposed model and actual radiation collected from above mentioned website. A plot of errors for all the samples is also plotted in figure 3 describing the difference between actual value and forecasted value. The graph is for solar radiation for a duration of 48 hours. The results have a MAE of 14.40 after 18 epochs. This figure is obtained when number of hidden layers
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iJournals: International Journal of Software & Hardware Research in Engineering ISSN-2347-4890 Volume 6 Issue 6 June, 2018 opted are 30 and 20 and optimization is done using LM VII. CONCLUSION training algorithm. In the present study, ability of Artificial Neural Network in estimating Solar radiation is verified. Solar radiation Results for training with ANFIS algorithm: for a location of Indore City is forecasted using two different ANN training algorithms, which are ANFIS and ANFIS model is used to forecast solar radiation. In this LM. The result of two are compared on the basis of work after training and testing ANFIS model following performance factor i.e. MAE, in order to find the best of results has been observed. The below plots in figure 5 two model for forecasting purpose. shows the forecasted solar radiation using proposed Obtained results are summarized below: model and actual solar radiation value collected from reference weather site. A plot of errors for all the samples is also plotted describing the difference between actual value and calculated value.
• The developed LM model with the configuration of 5-30-20-1 gives the best result with 14.40 after 18 epochs. • The developed ANFIS model with the configuration of 5-(no. of rules)-1 gives the best result with MAE= 11.09 after 3 epochs. Out of the two techniques ANFIS has least error hence it will give the most accurate prediction also it has good convergence speed. After analyzing the results, it has been found that predicted data is very similar to actual measured data with a MAE of 11.09 which is a great improvement and hence ANN can be used to predict Solar radiation and hence solar power.
REFRENCES
Fig 4: FIS editor
[1] Ravinesh C. Deoa, Mehmet Şahin,―Forecasting longterm global solar radiation with an ANN algorithm coupled with satellite-derived (MODIS) land surface temperature (LST) for regional locations in Queensland‖, Renewable and Sustainable Energy Reviews (2017), pp 828–848, Elsevier, 2017. DOI: http://dx.doi.org/10.1016/j.rser.2017.01.114 [2] Maria Malvoni, Paolo Maria Congedo, Maria Grazia de Giorgi,―Forecasting of PV Power Generation using weather input data‐ preprocessing techniques‖, 72nd Conference of the Italian Thermal Machines Engineering Association, ATI2017,Energy Procedia, pp 651-658, 6-8 September 2017,Lecce Italy, Elsevier, 2017. DOI: 10.1016/j.egypro.2017.08.293
Figure 5: Comparison between the predicted and actual Solar Radiation for ANFIS model.
The plot is for solar irradiation for next 48 hours. The results have a MAE of 11.09. The training is completed in 3 epochs.
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[3] Cyril Voyant, Gilles Notton, Soteris Kalogirou, Marie-Laure Nivet, Christophe Paoli, Fabrice Motte, Alexis Fouilloy, “ Machine learning methods for solar radiation forecasting: A review” , Renewable Energy 105, pp 569-582, Elsevier, 2017. DOI: http://dx.doi.org/10.1016/j.renene.2016.12.095 [4] Daniel O’ Leary and Joel Kubby, “ Feature Selection and ANN Solar Power Prediction” , Journal of Renewable Energy, Hindawi, 2017.
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iJournals: International Journal of Software & Hardware Research in Engineering ISSN-2347-4890 Volume 6 Issue 6 June, 2018 DOI: https://doi.org/10.1155/2017/2437387 [13] Amit Kumar Yadav, S.S. Chandel, “ Solar radiation prediction using Artificial Neural Network techniques: A [5] L. Saad Saoud, F. Rahmoune, V. Tourtchine, K. review” , Renewable and Sustainable Energy Reviews Baddari, “ Fully Complex Valued Wavelet Network for (2013), Elsevier,2013. Forecasting the Global Solar Irradiation” , Neural DOI: http://dx.doi.org/10.1016/j.rser.2013.08.055i Process Lett, Springer, 2016. DOI: 10.1007/s11063-016-9537-7 [14] Hong-Tzer Yang, Chao-Ming Huang, Yann-Chang Huang, and Yi-Shiang Pai, “ A Weather-Based Hybrid [6] S.P.Sharma et al., Prediction of Solar Energy Based Method for 1-Day Ahead Hourly Forecasting of PV on Intelligent ANN Modeling , International Journal Of Power Output” , IEEE TRANSACTIONS ON Renewable Energy Research, Vol.6, No.1, 2016 SUSTAINABLE ENERGY, VOL. 5, NO. 3, JULY 2014. DOI: 10.1109/TSTE.2014.2313600 [7] Emre Akarslan, Fatih Onur Hocaoglu, “ A novel adaptive approach for hourly solar radiation [15] Jompob Waewsaka, Chana Chanchama, Marina forecasting” , Renewable Energy 87 (2016) pp 628-633, Mania and Yves Gagnonb, “ Estimation of Monthly Elsevier, 2015. Mean Daily Global Solar Radiation over Bangkok, DOI: http://dx.doi.org/10.1016/j.renene.2015.10.063 Thailand using Artificial Neural Networks” , Energy Procedia 57, pp 1160 – 1168, 2013 ISES Solar World [8] Hakan Kutucu, Ayad Almryad, “ Modeling of Solar Congress.2013. Energy Potential in Libya using an Artificial Neural DOI: 10.1016/j.egypro.2014.10.103 Network Model” , IEEE First International Conference on Data Stream Mining & Processing, 23-27 August [16] Rich H. Inman, Hugo T.C. Pedro, Carlos F.M. 2016, Lviv, Ukraine. IEEE, 2016. Coimbra, “ Review Solar forecasting methods for renewable energy integration” , Progress in Energy and [9] Premalatha Neelamegama, Valan Arasu Amirtham, Combustion Science 39 (2013) 535-576, Elsevier, 2013. “ Prediction of solar radiation for solar systems by using DOI: http://dx.doi.org/10.1016/j.pecs.2013.06.002 ANN models with different back propagation algorithms” , Journal of Applied Research and [17] E. G. Kardakos, M. C. Alexiadis, S. I. Vagropoulos, Technology 14 (2016) pp 206– 214, 2016. C. K. Simoglou, P. N. Biskas, and A. G. Bakirtzis, DOI: http://dx.doi.org/10.1016/j.jart.2016.05.001 “ Application of Time Series and Artificial Neural Network Models in Short-term Forecasting of PV Power [10] John Boland, Mathieu David, Philippe Lauret, Generation” , IEEE 2013. “ Short term solar radiation forecasting: Island versus continental sites” , Energy 113, pp 186-192, Elsevier, [18] Ministry of power, Government of India. 2016. https://powermin.nic.in/en/content/power-sector-glanceDOI: http://dx.doi.org/10.1016/j.energy.2016.06.139 all-india. [11] Masoud Vakilia, Saeed-Reza Sabbagh-Yazdib, Koosha Kalhorb, Soheila Khosrojerdic, “ Using Artificial Neural Networks for Prediction of Global Solar Radiation in Tehran Considering Particulate Matter Air Pollution” , Energy Procedia 74, pp 1205 – 1212, International Conference on Technologies and Materials for Renewable Energy, Environment and Sustainability, TMREES15, 2015. DOI: 10.1016/j.egypro.2015.07.764 [12] João Faceira, Paulo Afonso, and Paulo Salgado, “ Prediction of Solar Radiation Using Artificial Neural Networks” , CONTROLO’ 2014 - Proc. of the 11th Port. Conf. on Autom. Control, Springer 2015 DOI: 10.1007/978-3-319-10380-8_38
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