Unit Three Kartini
ABSTRACT
The paper proposes based on meteorology data, especially for optimizing the operation of power generating electricity from photovoltaic energy. This paper proposes a novel methodology for very short term forecasting of hourly global solar irradiance (GSI). This methodology is a combination of k-nearest neighbor algorithm (k-NN) modelling and multilayer backpropagation learning neural network (BPLNN) model. The k-NN-multilayer BPLNN model is designed to forecast GSI for 1 hours ahead based on meteorology data for the target PV station which position is surrounded by eight other adjacent PV stations. The forecasting for global solar irradiance using k-NN-multilayer BPLNN modelling is a very powerful technique to determine the behaviour of time series data. The first method implements k-NN as a preprocessing technique prior to backpropagation learning method. The error statistical indicators of k-NN- multilayer BPLNN models used momentum (mc) = 0.8 the root-mean-square error (RMSE) is 176.5 W/m2. The models forecasts are then compared to measured data and validation results indicate that the k-NN-BPLNN based method presented in this study can estimate hourly GSI with satisfactory accuracy.
- Mark J. E, Blake J.Hargis, and J. D. Linton, Augmented Efficient BackProp for Backpropagation Learning in Deep Autoassociative Neural Networks, IEEE, 2010, 978-1-4244-8126-2.Google Scholar
- Guoqi Liu, Zhiheng Zhou, Huiqiang Zhong, Shengli Xie, Gradient descent with adaptive momentum for active contour models, IET Comput. Vis., 2014, Vol. 8, Iss. 4, pp. 287--298 doi: 10.1049/iet-cvi.2013.0089 Google ScholarDigital Library
- ZHANG Laibin, YE Yingchun, LIANG Wei, YU Dongliang, WANG Zhaohui, A Novel BP Algorithm for Pipeline Condition Recognition, 978-0-7695-3507-4/08 2008 IEEE DOI 10.1109/CSIE.2009.681Google Scholar
- Fatih Onur Hocaog, Stochastic approach for daily solar radiation modeling, Solar Energy. 85, 2011, 278--287. Google ScholarCross Ref
- Hugo T.C. Pedro, Carlos F.M. Coimbra, Nearest-neighbor methodology for prediction of intra-hour global horizontal and direct normal irradiances, Renewable Energy. 80, 2015, 770--782. Google ScholarCross Ref
- Sancho Salcedo-Sanz, Carlos Casanova-Mateo, Jordi Munoz-Marí, Gustau Camps-Valls, Prediction of Daily Global Solar Irradiation Using Temporal Gaussian Processes, IEEE GEOSCIENCE AND REMOTE SENSING LETTERS. 11, 2014, 11.Google ScholarCross Ref
- Dazhi Yang, Vishal Sharma a, Zhen Ye, Lihong Idris Lim, Lu Zhao a, Aloysius W. Aryaputera, Forecasting of global horizontal irradiance by exponential smoothing using decompositions, Energy. 81, 2015, 111--119. Google ScholarCross Ref
- Dazhi Yang, Zhen Ye b, Li Hong Idris Lim c, Zibo Dong, Very short term irradiance forecasting using the lasso, Solar Energy. 114, 2015, 314--326. Google ScholarCross Ref
- G. A. Licciardi, R. Dambreville, J. Chanussot, Stephanie Dubost, Spatiotemporal Pattern Recognition and Nonlinear PCA for Global Horizontal Irradiance Forecasting, IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, 12, 2015, 2. Google ScholarCross Ref
- Badia Amrouche, Xavier Le Pivert b, Artificial neural network based daily local forecasting for global solar radiation, Applied Energy. 130, 2014, 333--341. Google ScholarCross Ref
- L.T. Wong, W.K. Chow, Solar radiation model, Applied Energy. 69, 2001, 191--224. Google ScholarCross Ref
- Christophe Paolia, Cyril Voyantab, Marc Musellia, Marie-Laure Niveta, Multi-horizon Irradiation Forecasting For Mediterranean Locations Using Time Series Models, Energy Procedia. 57, 2014, 1354--1363. Google ScholarCross Ref
- Elke Lorenz, Johannes Hurka, Detlev Heinemann, and Hans Georg Beyer, Irradiance Forecasting for the Power Prediction of Grid-Connected Photovoltaic Systems, IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING. 2, 2009, 1. Google ScholarCross Ref
- Fei Wang, Zengqiang Mi, Shi Su, Hongshan Zhao, Short-Term Solar Irradiance Forecasting Model Based on Artificial Neural Network Using Statistical Feature Parameters, Energies. 5, 2012, 1355--1370.Google ScholarCross Ref
- Adel Mellit, Alessandro Massi Pavan, A 24-h forecast of solar irradiance using artificial neural network: Application for performance prediction of a grid-connected PV plant at Trieste Italy, Solar Energy. 84, 2010, 807--821. Google ScholarCross Ref
- A. Hammer, d. Heinemann, e. Lorenz and b. Lu ckehe, Short-Term Forecasting Of Solar Radiation: A Statistical Approach Using Satellite Data, Solar Energy. 67, 1999, 139--150. Google ScholarCross Ref
- Cao Xiao, Wanpracha Art Chaovalitwongse, Optimization Models for Feature Selection of Decomposed Nearest Neighbor, IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS: SYSTEMS, 2015, 2168--2216Google Scholar
- Richard Perez a, Sergey Kivalov a, James Schlemmer a, Karl Hemker Jr. a, David Renne, Thomas E. Hoff, Validation of short and medium term operational solar radiation forecasts in the US, Solar Energy. 84, 2010, 2161--2172. Google ScholarCross Ref
- Ricardo Marquez, Carlos F.M. Coimbra, Forecasting of global and direct solar irradiance using stochastic learning methods, ground experiments and the NWS database, Solar Energy. 85, 2011, 746--756. Google ScholarCross Ref
- Luis Martıń b, Luis F. Zarzalejo a, Jesus Polo a, Ana Navarro a, Ruth Marchante b, Marco Cony, Prediction of global solar irradiance based on time series analysis: Application to solar thermal power plants energy production planning, Solar Energy. 84, 2010, 1772--1781. Google ScholarCross Ref
- Remus St. Boata, Paul Gravila, Functional fuzzy approach for forecasting daily global solar irradiation, Atmospheric Research. 112, 2012, 79--88. Google ScholarCross Ref
- Romain Dambreville a, Philippe Blanc b, Jocelyn Chanussot a, Didier Boldo, Very short term forecasting of the Global Horizontal Irradiance using a spatio-temporal autoregressive model, Renewable Energy. 72, 2014, 291--300. Google ScholarCross Ref
- Maimouna Diagne, Mathieu David b, Philippe Lauret b, John Boland c, Nicolas Schmutz, Review of solar irradiance forecasting methods and a proposition for small-scale insular grids, Renewable and Sustainable Energy Reviews. 27, 2013, 65--76. Google ScholarCross Ref
- Jianwu Zeng, Wei Qiao, Short-term solar power prediction using a support vector machine, Renewable Energy. 52, 2013, 118--127. Google ScholarCross Ref
- Zibo Dong, Dazhi Yang, Thomas Reindl, Wilfred M. Walsh, Short-term solar irradiance forecasting using exponential smoothing state space model, Energy. 55, 2013, 1104--1113. Google ScholarCross Ref
- Ye Ren, P. N. Suganthan and Narasimalu Srikanth, A Comparative Study of Empirical Mode Decomposition-Based Short-Term Wind Speed Forecasting Methods, IEEE TRANSACTIONS ON SUSTAINABLE ENERGY. 6, 2015, 1.Google ScholarCross Ref
- Mellit. A, Benghanem. M, Bendekhis M, Artificial Neural Network Model for Prediction Solar Radiation Data: Application for Sizing Stand-Alone Photovoltaic Power System, IEEE. 5, 2005, 7803--9156.Google Scholar
- Farhad S.G, S. R. Khaze, I. Malekl, A new Approach in Bloggers Classification with Hybrid of K-Nearest Neighbor and Artificial Neural Network Algorithms, Indian Journal os Science and Technology, 8, 2015, 237--246.Google Scholar
- Kun-Long Ho, Yuan-Yih Hsu, Chien-Chuen Yang, Short Term Load Forecasting Using A Multilayer Neural Network With An Adaptive Learning Algorithm, Transactions on Power Systems, Vol. 7, No. 1, February 1992.Google ScholarCross Ref
Index Terms
- Short Term Forecasting of Global Solar Irradiance by k-Nearest Neighbor Multilayer Backpropagation Learning Neural Network Algorithm
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