related work. Keywords: Solar energy prediction; machine learning; support vector machine; Oman. INTRODUCTION. Currently the subject of generating energy ...
International Journal of Applied Engineering Research ISSN 0973-4562 Volume 11, Number 20 (2016) pp. 10166-10172 © Research India Publications. http://www.ripublication.com
Modelling of Daily Solar Energy System Prediction using Support Vector Machine for Oman Hussein A Kazem Associate Professor, Faculty of Engineering, Sohar University PO Box 44, Sohar, PCI 311 Oman. Jabar H. Yousif Assistant Professor, Faculty of Computer & Information Technology, Sohar University PO Box 44, Sohar, PCI 311 Oman. Miqdam T Chaichan Assistant Professor, Department of Mechanical Engineering University of Technology Baghdad, Iraq.
Abstract Solar energy is the most important renewable energy source, which could be utilized through photovoltaic or thermal systems. This paper aims to design and implement a machine learning technique called Support Vector Machine (SVM) for the management of energy generation based on experimental work. The SVM model is consist of two inputs and one output. The inputs are solar radiation, and ambient temperature; the output is the photovoltaic current. The practical implementation of the proposed SVM model is achieved a final MSE of (0.026378744) in training phase and (0.035615759) in cross validation phase. The experiments achieved a value of (R squared) equal to 0.0774 which indicates the predicting model is very close to the regression line and a well data fitting to the statistical model. Besides, the proposed model achieved less MSE in comparison with other related work. Keywords: Solar energy prediction; machine learning; support vector machine; Oman. INTRODUCTION Currently the subject of generating energy from renewable sources is considering as one of the important topics that are growing promptly as a result of its many benefits. The current step of technological development makes the extraction of renewable energy from various sources such as the sun, wind, geothermal energies, and many other sources commercially viable process [1]. Renewable energy started competing with non-renewable energy sources due to many reasons (i.e., fluctuation of oil prices, independency, reduction in renewable energy technology prices and improvement in efficiency, etc.) [2]. Renewable energy generation capacity significantly affected by fluctuations in the atmosphere for example, solar energy generated in the morning for the presence of sunlight during the day and the energy generated from the wind depends on the wind speed and geographic location. These fluctuations in
the generation rates cause instability in the power grid. Thus, to overcome this problem, the energy is generated at the time availability of natural resources is and the excess energy is stored on the need for later use. At this point, the problem of finding an efficient management of these sources of electric power generation is more needed [3]. Solar energy could be utilized through photovoltaic PV or thermal systems. Solar energy technology and especially solar cells and photovoltaic have been experimented for more than 70 years and it shows good success. The PV system prices get down and at the same time, the PV efficiency is improved [4]. Machine learning is a subdivision of artificial intelligence that is developed as a result of studies in pattern classification and recognition for finding mathematical models for various real life problems. Machine learning investigates the construction of algorithms that can learn from the previous data and help in finding a forecast on the data in the current and future time. The machine learning applied iterative and interactive statistical methods in the construction of computational models to obtain the desired results [5]. The factors like efficiency of learning algorithms, the complexity of the problem, methods of representation of data are the most important factors affect the accuracy of the results and future forecasting for data. Several machine learning methods are used for designing and implementing different phases of a renewable energy power systems based on the problem requirements and its characteristics [6, 7 & 8]. Hence, the adaptation of optimal location and structures of renewable power plants is one of the important implementation of learning machine methods. Recently, the Support Vector Machines (SVMs) have been widely implemented into several problems of renewable energy power systems [9, 10, 11, and 12].
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International Journal of Applied Engineering Research ISSN 0973-4562 Volume 11, Number 20 (2016) pp. 10166-10172 © Research India Publications. http://www.ripublication.com There is number of studies in literature related to the use of machine learning techniques to predict PV performance, including Multilayer Perceptron (MLP) network, Probabilistic Neural Networks (PNN), General Regression Neural Networks (GRNN), Radial Basis Function networks (RBF), Cascade Correlation, functional link networks, Kohonen networks, Gram-Charlier networks, learning vector quantization, Hebb networks, Adaline Networks, hetero Associative Networks, Recurrent Networks, and hybrid networks [9]. Reference [10] proposed prediction model for solar power generation based on experimental work. Different machine learning techniques has been used. The authors included that SVM in the multiple regression techniques. In SVM model they tried polynomial, linear, RBF kernels. They claimed that SVM model accuracy increased up to 27%. Furthermore, principal component analysis has been used to improve the model. Ref. [11] proposed hybrid intelligent predictor. The proposed system used regression models namely, RBF, MLP, Linear Regression (LR), SVM, Simple Linear Regression (SLR), Pace Regression (PR), Additive Regression (AR), Median Square (LMS), IBk (an implementation of kNN) and Locally Weighted Learning (LWL). They claimed that LMS, MLP, and SVM are the most accurate models in term of MAE and MAPE. Ref. [12] used SVM model to predict solar energy; he found that SVM accuracy is less than Gaussian Process Regression method. Ref. [5] implements a SVM model to estimate the daily solar radiation using air temperatures. The developed SVM model used a polynomial kernel function which performed better than other SVM models. He obtained a highest NSE of 0.999, and the R-square of 0.969, while the lowest RMSE is 0.833 and RRMSE of 9.00. In this study, SVM model will be proposed for solar PV system in Sohar, the second largest city in Oman, based on experimental data for solar irradiations and installed PV system in the Solar Cells and Photovoltaic Research Lab in Sohar University, Oman. This paper aims to discuss and implement machine learning methods for acceptable management of the energy generation of a PV system. STUDY LOCATION In Oman due to growth in industry and population the electrical demand increased and it will keep increasing in near future as shown in Figure 1. The maximum load increased from 4,634 MW in 2013 to 5,691 MW in 2014. In 2018 the forecasted power demand is 6.8 GW, which mean more power plants need to be installed.
Figure 1. Oman peak power demand for 2011-2015 and projection till 2018 Sohar is the second largest city in Oman after the capital, Muscat. It is the primary industrial center which is just 230 km from Muscat, the capital [13]. The solar radiation intensity in Solar (Direct normal (DNI), diffuse horizontal (DHI) and global horizontal (GHI)) was measured and recorded experimentally. The measurements revealed that higher solar irradiance in summer (July) where the maximum irradiation recorded value was 950 W/m2 at 1 PM, and the minimum ones was 202 W/m2 in January at the same hour [14]. As Sohar stranded between the Al-Arab Sea coast and Al Hajar Mountains series, this area subjected to few dust storms [15]. Sohar city climate is harsh, with maximum temperatures that sometimes reach 50°C in the summer season. Also, Sohar has a humid climate as it is a coastal area [16, 17 & 18]. The Authority of Electricity Regulation (AER) in Oman has explored the renewable energy sources in 2008 and they claimed that the priorities are solar than wind [19]. The Public Authority for Electricity and Water (PAEW) in 2010 investigated the best locations to install large scale solar system to generate electricity [20]. In 2011, Sohar University (SU) won a grant from the Omani Research Council to investigate, design and assess PV systems in Oman in term of technical and economic criteria. Refs. [21 & 22] presented feasibility study of PV systems in Oman standalone and grid connected, respectively. Authors of reference [21] proposed optimization of the PV system tilt angle, array size and storage battery capacity using MATLAB numerical method. Load demand and hourly meteorological data has been used. It is found that the PV system sizing ratio for PV array and battery are 1.33 and 1.6, respectively. Also, they claimed that the cost of energy for standalone PV system in Oman is 0.196 USD/kWh. Ref. [22] investigated and assessed the grid connected PV system in Oman. It is found that the Crist Factor and Yield Factor, two technical criteria of the grid connected PV system are 21% and 1875 kWh/kWp/year respectively. Meanwhile, the cost of the energy and the payback period as an economic criteria are 0.045 USD/kWh and 11 years, respectively. As a conclusion they claimed that the energy generated by PV systems is cheaper than energy generated by fossil fuel in Oman.
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International Journal of Applied Engineering Research ISSN 0973-4562 Volume 11, Number 20 (2016) pp. 10166-10172 © Research India Publications. http://www.ripublication.com This study proposed model predicts the output of grid connected PV system in Oman. The proposed model is based on implementation of artificial intelligent techniques. The SVM is used to classify and predict the future amount of production of the PV system. The system experimental data has been measured in Sohar, Oman. EXPERIMENTAL SETUP 1. Practical measurement In the current study, 24 PV modules have been installed in Sohar University in Oman. The rating of PV module is 140W as shown in Table 1. Three PV systems configurations; standalone, grid connected and tracking systems have been designed and evaluated as shown in Figure 2 (a). The meteorological data has been measured and recorded using weather station as shown in Figure 2 (b). The grid connected system data (voltage, current, power and energy) has been recorded and monitored. Also, the measured environmental parameters are linked to the productivity of the PV system. This research is a part of a research on grid connected PV system (see Figure 3) and consequently the production of the PV system here is needed. Table 1: Installed solar system specifications PV array PV panels (24 panels) “Maximum voltage” “Maximum current” “Open circuit voltage” “Short circuit current” “Efficiency” Temperature coefficient of Vo.c
140 Wp (3.36 kWp) 17.7 7.91 22.1 8.68 13.9% -0.36 %/k
Temperature coefficient of Is.c
0.06 %/k
Figure. 2. (a) Grid connected PV system
Figure. 2. (b) Meteorological station
Figure. 3. Solar photovoltaic grid connected system 2. Machine Learning The Machine Learning is one of the applications of artificial intelligence to simulate the thinking capabilities of human [23]. It is concerning with the techniques and methods that help the machine to learn. Machine learning is developed as a result of studies in pattern classification and recognition for finding mathematical models for various problems. Machine learning investigates the construction of algorithms that can learn from the previous data and help in finding a forecast on the data in the current and future time. The machine learning applied iterative and interactive statistical methods in the construction of computational models to obtain the desired results [24]. Several techniques and methodologies are established for machine learning tasks. The new development of machine learning techniques is mainly utilizing Kernelbased methods such as support vector machines (SVM), Gaussian processes, etc. [6]. A SVM is considered as a supervised learning technique which is presented by Refs. [23 & 25] in COLT-92 as presented in Figure 4. It is considering as a classification and regression prediction techniques. SVM is deployed in many applications, such as face analysis and detection, hand writing recognition, pattern classification and regression etc. A classification technique comprises of training and testing data attributes. The main benefits of SVM are minimizing the classification error by implement an iterative training algorithm and maximizing the margin between two hyper-planes. Figure 5 show the classification Hyper-planes (wx+b>1 and wx+b
