Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
2019 | 28 | 3 |
Tytuł artykułu

Elman-based forecaster integrated by Adaboost algorithm in 15 min and 24 h ahead power output prediction using PM 2.5 values, PV module temperature, hours of sunshine, and meteorological data

Warianty tytułu
Języki publikacji
Nowadays, with the depletion of fossil energy and deterioration of environmental quality, solar energy is perceived to be a renewable and clean energy. While developing rapidly all over the world, solar energy is also faced with many challenges resulting from its inherent properties. In order to reduce the impact on the grid and facilitate scheduling, it is a growing problem to build a feasible model to forecast PV power with high precision. Therefore, this paper proposes an Elman-based forecaster integrated by Adaboost algorithm, namely Adaboost + Elman. Before forecasting, input variables containing PM 2.5 values, temperature of the PV module, sunshine hours, and meteorological data are made using correlation, clustering, and discriminate analysis to avoid information redundancy and improve the generalization ability of the model. To verify the developed model’s application to short-term PV forecasting in two different time scales, data of Huangsi in 2016 are used for model construction and verification. An additional 7 models are introduced to make comparison. Experimental results prove that the proposed model is effective and practicable for two different scales of short-term PV power prediction.
Słowa kluczowe
Opis fizyczny
  • Department of Economics and Management, North China Electric Power University, Baoding, China
  • Department of Economics and Management, North China Electric Power University, Baoding, China
  • Department of Economics and Management, North China Electric Power University, Baoding, China
  • Department of Economics and Management, North China Electric Power University, Baoding, China
  • Dezhou Power Supply Company, Dezhou City, Shandong Province, China
  • Spic Ningjin Thermoelectricity Co., Ning Jin County, China
  • 1. CERVONE G., CLEMENTE-HARDING L., ALESSANDRINI S., MONACHE L.D. Short-term photovoltaic power forecasting using Artificial Neutral Networks and an Analog Ensemble. Renewable Energy, 108, 274, 2017.
  • 2. LEWIS N.S., NOCERA D.G. Powering the planet: chemical challenges in solar energy utilization. Proceedings of the National Academy of Sciences of the United States of America, 103 (43), 15729, 2006.
  • 3. SUN W., WANG C.F., ZHANG C.C. Factors analysis and forecasting of CO₂ emissions in Hebei, using extreme learning machine based on particle swarm optimization. Journal of Cleaner Production, 162, 1095, 2017.
  • 4. YI J.W., ZHAO D.Q., HU X.L., CAI G.T. Study on the development of Guangdong’s electricity power under CO₂ emission constraints. Journal of University of Science & Technology of China, 41 (5), 452, 2011.
  • 5. BECKER S., FREW B.A., ANDRESEN G.B., ZEYER T., SCHRAMM S., GREINER M., JACOBSON M.Z. Features of a fully renewable US electricity system: optimized mixes of wind and solar PV and transmission grid extensions. Energy, 72 (7), 443, 2014.
  • 6. ARENT D., PLESS J., MAI T., WISER R., HAND M., BALDWIN S., HEATH G., MACKNICK J., BAZILIAN M., SCHLOSSER A., DENHOLM P. Implications of high renewable electricity penetration in the us for water use, greenhouse gas emissions, land-use, and materials supply. Applied Energy, 123 (3), 368, 2014.
  • 7. LIMA F.J.L., MARTINS F.R., PEREIRA E.B., LORENZ E., HEINEMANN D. Forecast for surface solar irradiance at the Brazilian northeastern region using NWP model and artificial neural networks. Renewable. Energy, 87, 807, 2016.
  • 8. GONG Y.F., LU Z.X., QIAO Y., WANG Q. An Overview of Photovoltaic Energy System Output Forecasting Technology. Automation of Electric Power System, 40 (4), 140, 2016.
  • 9. BAI J.L., MEI H.W. Improved similarity based fuzzy clustering algorithm and its application in the PV array power short-term forecasting. Power System Protection & Control, 42 (6), 84, 2014.
  • 10. LI Z.X., RAHMAN S.M., VEGA R., DONG B. A hierarchical approach using machine learning methods in solar photovoltaic energy production forecasting. Energies, 9 (1), 1, 2016.
  • 11. MASSIDDA L., MARROCU M. Use of Multilinear Adaptive Regression Splines and numerical weather prediction to forecast the power output of a PV plant in Borkum, Germany. Solar Energy, 146, 141, 2017.
  • 12. XIE Y.H., HU X.L., ZHANG H.D. Research on Recognition of Ground-Based Cloud Images Based on Multi-Scale Analysis. Computer Simulation, 31 (11), 212, 2014.
  • 13. ALMONACID F., PEREZ-HIGUERAS P.J., EDUARDO E.F., HONTORIA L. A methodology based on dynamic artificial neural network for short-term forecasting of the power output of a PV generator. Energy Conversion and Management, 85, 389, 2014.
  • 14. SAINT-DRENAN Y.M., BOFINGER S., FRITZ R., VOGT S., GOOD G.H., DOBSCHINSKI J. An empirical approach to parameterizing photovoltaic plants for power forecasting and simulation. Solar Energy, 120, 479, 2015.
  • 15. WOLFF B., KUHNERT J., LORENZ E., KRAMER O., HEINEMANN D. Comparing support vector regression for PV power forecasting to a physical modeling approach using measurement, numerical weather prediction and cloud motion data. Solar Energy, 135, 197, 2016.
  • 16. CHEN Z.B., DING J., ZHOU H., CHENG X., ZHU X. A model of very short-term photovoltaic power forecasting based on ground-based cloud images and RBF neural network. Proceedings of the CSEE, 35 (3), 561, 2015.
  • 17. MONTEIRO R.V.A., GUIMARAES G.C., MOURA F.A.M., ALBERTINI M.R.M.C., ALBERTINI M.K. Estimating photovoltaic power generation: Performance analysis of artificial neutral networks, Support Vector Machine and Kalman filter. Electric Power System Research, 143, 643, 2017.
  • 18. ANTONANZAS J., OSORIO N., ESCOBAR R., URRACA R., MARTINEZ-DE-PISON F.J., ANTONANZAS-TORRES F. Review of photovoltaic power forecasting. Solar Energy, 136, 78, 2016.
  • 19. DOLARA A., GRIMACCIA F., LEVA S., MUSSETTA M., OGLIARI E., A physical hybrid artificial neural network for short term forecasting of PV plant power output. Energies, 8, 1138, 2015.
  • 20. YU Q., PIAO Z.L., HU B. A Hybrid Model for Short-Term Photovoltaic Power Forecasting Based on EEMD-BP Combined Method. Power System and Clean Energy, 32 (7), 132, 2016.
  • 21. LI Q., ZHOU B.Q., ZHANG J.C., LI J.J. Photovoltaic Power Prediction Based on Adaptive Differential Evolution and BP Neural Network. Shaanxi Electric Power, 42 (2), 23, 2014.
  • 22. GAO X.M., YANG S.F., PAN S.B. A forecasting model for output power of grid-connected photovoltaic generation system based on EMD and ABC-SVM. Power System Protection and Control, 43 (21), 86, 2015.
  • 23. ALESSANDRINI S., MONACHE L.D., SPERATI S., CERVONE G. An analog ensemble for short-term probabilistic solar power forecast. Applied Energy, 157 (1), 95, 2015.
  • 24. PIERRO M., BUCCI F., FELICE M.D., MAGGIONI E., MOSER D., PEROTTO A., SPADA F., CORNARO C. Multi-model ensemble for day ahead prediction of photovoltaic power generation. Solar. Energy, 134, 132, 2016.
  • 25. ANGSTROM A. Solar and terrestrial radiation. Report to the international commission for solar research on actinometric investigations of solar and atmospheric radiation. Quarterly Journal of the Royal Meteorological Society, 50 (210), 121, 1924.
  • 26. QIAN Z., CAI S.B., GU Y.Q., TONG J.J., BAO G.J. Review of PV power generation prediction. Journal of Mechanical - Electrical Engineering, 32 (5), 651, 2015.
  • 27. ZHANG W.Q., FU Y.J., YANG H.Z. Multi-model soft-sensor modeling based on improved clustering and weighted bagging. CIESC Journal, 63 (9), 2697, 2012.
  • 28. BARAK S., ARJMAND A., ORTOBELLI S. Fusion of multiple diverse predictors in stock market. Information Fusion, 36, 90, 2017.
  • 29. NOORI R., KARBASSI A.R., ASHRAFI K., ARDESTANI M., MEHRDADI N. Development and application of reduced-order neural network model based on proper orthogonal decomposition for BOD5 monitoring: Active and online prediction. Environmental Progress & Sustainable Energy, 32 (1), 120, 2013.
  • 30. ZHUANG T., YANG C.J. Silicon content forecasting method for hot metal based on Elman-Adaboost strong predictor. Metallurgical Industry Automation, 41 (4), 1, 2017.
  • 31. Liu H., Tian H.Q., Liang X.F., Li Y.F. Wind speed forecasting approach using secondary decomposition algorithm and Elman neural networks. Applied Energy, 157, 183, 2015.
  • 32. XIAO Y., XIAO J., LU F.B., WANG S.Y. Ensemble ANNs-PSO-GA Approach for Day-ahead Stock E-exchange Prices Forecasting. International Journal of Computational Intelligence Systems, 7, 272, 2014.
  • 33. CHANG W.Y. Short-Term Wind Power Forecasting Using the Enhanced Particle Swarm Optimization Based Hybrid Method. Energies, 6, 4879, 2013.
  • 34. ZHANG P.B., YANG Z.X. A Robust AdaBoost. RT Based Ensemble Extreme Learning Machine. Mathematical Problems in Engineering, 6, 1, 2015.
Typ dokumentu
Identyfikator YADDA
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.