LSTM Based Forecasting of PV Power for a Second Order Lever Principle Single Axis Solar Tracker
DOI:
https://doi.org/10.13052/spee1048-5236.4226Keywords:
LSTM method, photovoltaic, PV forecasting, single axis solar tracker, solar energyAbstract
Nowadays solar power generation has significantly improved all over the world. Therefore, the power estimation of photovoltaic (PV) using weather parameters presents the future management of energy utilization in power system planning. This article presents the power forecast of the Second Order Lever Principle Single Axis Solar Tracker (SOLPSAST) system. A deep neural network is developed using Long Short Term Memory (LSTM) and is validated on sunny, cloudy and partially cloudy days. The performance of the proposed LSTM in comparison with Support Vector Machine (SVM) has improved the Mean Absolute Proportion Error (MAPE) forecasts accuracy to 4.29%, 5.16%, and 4.82% for sunny, cloudy and partially cloudy days, respectively. Also, the estimated Mean Relative Error (MRE) value of the LSTM model for sunny, cloudy and partially cloudy days is 3.19%, 4.10%, and 4.02%, respectively. Finally, the forecasted power generation of the SOLPSAST system’s monthly average and annual generation is found to be 2.45 Wh, and 29.44 kWh, respectively.
Downloads
References
Ulyana Koriugina, Daria Illarionova, “Towards Smart Green Cities: Analysis of Integrated Renewable Energy Use in Smart Cities,” Strategic Planning for Energy and the Environment, pp. 75-94, ol. 40, 2021. Available: https://orcid.org/0000-0002-4593-2835
Ninh Nguyen, Thuy Hong Pham, “Promoting Energy Efficiency in an Emerging Market: Insights and Intervention Strategies,” Strategic Planning for Energy and the Environment, pp. 70-80, vol. 38, 2018.
Alkesaiberi, A.; Harrou, F.; Sun, Y. Efficient Wind Power Prediction Using Machine Learning Methods: A Comparative Study. Energies 2022, 15, 2327. Available: https://doi.org/10.3390/en15072327.
Anuj Gupta, Kapil Gupta, Sumit Saroha, “Short Term Solar Irradiation Prediction Framework Based on EEMD-GA-LSTM Method,” Strategic Planning for Energy and the Environment, Vol. 41 Iss. 3, 07–05 2022. Available: https://doi.org/10.13052/spee1048-5236.4132.
Francis M. Lopes, Hugo G. Silva, Rui Salgado, Afonso Cavaco, Paulo Canhoto, Manuel Collares-Pereira, “Short-term forecasts of GHI and DNI for solar energy systems operation: assessment of the ECMWF integrated forecasting system in southern Portugal,” Sol Energy, pp. 14–30, vol. 170, 2018. Available: https://doi.org/10.1016/j.solener.2018.05.039.
J. Alonso-Montesinos, F.J. Batlles, C. Portillo, “Solar irradiance forecasting at one-minute intervals for different sky conditions using sky camera images,” Energy Conversion and Management, pp. 1166–1177, vol. 105, 2015. Available: https://doi.org/10.1016/j.enconman.2015.09.001.
Steven D. Miller, Matthew A. Rogers, John M. Haynes, Manajit Sengupta, Andrew K. Heidinger, “Short-term solar irradiance forecasting via satellite/model coupling,” Solar Energy, pp. 102–117, vol. 168, 2018. Available: https://doi.org/10.1016/j.solener.2017.11.049.
Laith M. Halabi, Saad Mekhilef, Monowar Hossain, “Performance evaluation of hybrid adaptive neuro-fuzzy inference system models for predicting monthly global solar radiation,” Applied Energy, pp. 247–261, vol. 213, 2018. Available: https://doi.org/10.1016/j.apenergy.2018.01.035.
Hou Wei, Xiao Jian, Niu Liyong, “Analysis of power generation capacity of photovoltaic power,” Electr Eng, 2016,17(4):53e8.
Shuwei Miao, Guangtao Ning, Yingzhong Gu, Jiahao Yan, Botao Ma, “Markov Chain model for solar farm generation and its application to generation performance evaluation,” Journal of Cleaner Production, pp. 905–917, vol. 186, 2018. Available: https://doi.org/10.1016/j.jclepro.2018.03.173.
X. G. Agoua, R. Girard and G. Kariniotakis, “Short-Term Spatio-Temporal Forecasting of Photovoltaic Power Production,” IEEE Transactions on Sustainable Energy, pp. 538–546, vol. 9, April, 2018. Available: DOI: 10.1109/TSTE.2017.2747765.
Luca Massidda, Marino Marrocu, “Use of Multilinear Adaptive Regression Splines and numerical weather prediction to forecast the power output of a PV plant in Borkum, Germany,” Solar Energy, pp. 141–149, vol. 146, 2018. Available: https://doi.org/10.1016/j.solener.2017.02.007.
Dairi A, Harrou F, Sun Y, Khadraoui S. “Short-Term Forecasting of Photovoltaic Solar Power Production Using Variational Auto-Encoder Driven Deep Learning Approach” In Applied Sciences, 2020, vol. 10(23), 8400. Available: https://doi.org/10.3390/app10238400.
Anuj Gupta, Kapil Gupta, Sumit Saroha, “Solar Irradiation Forecasting Technologies: A Review,” Strategic Planning for Energy and the Environment, vol. 39, Iss. 3–4, 09-07-2021. Available: https://doi.org/10.13052/spee1048-4236.391413.
Mayur Barman, Nalin Behari Dev Choudhury, “Season specific approach for short-term load forecasting based on hybrid FA-SVM and similarity concept,” Energy, pp. 886–896, vol. 174, 2019. Available: https://doi.org/10.1016/j.energy.2019.03.010.
Jie Shi, Wei-Jen Lee, Yongqian Liu, Yongping Yang and Peng Wang, “Forecasting power output of photovoltaic system based on weather classification and support vector machine,” 2011 IEEE Industry Applications Society Annual Meeting, pp. 1–6, 2011. Available: DOI: 10.1109/IAS.2011.6074294.
Geoffrey K.F. Tso, Kelvin K.W. Yau, “Predicting electricity energy consumption: A comparison of regression analysis, decision tree and neural networks,” Energy, pp. 1761–1768, vol. 32, 2007. Available: https://doi.org/10.1016/j.energy.2006.11.010.
Fermín Rodríguez, Alice Fleetwood, Ainhoa Galarza, Luis Fontán, “Predicting solar energy generation through artificial neural networks using weather forecasts for microgrid control,” Renewable Energy, pp. 855–864, vol. 126, 2018. Available: https://doi.org/10.1016/j.renene.2018.03.070.
Gokhan Mert Yagli, Dazhi Yang, Dipti Srinivasan, “Automatic hourly solar forecasting using machine learning models,” Renewable and Sustainable Energy Reviews, pp. 487–498, vol. 105, 2019. Available: https://doi.org/10.1016/j.rser.2019.02.006.
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, pp. 807–821 vol. 84, 2010, Available: https://doi.org/10.1016/j.solener.2010.02.006.
A. Yona, T. Senjyu, A. Y. Saber, T. Funabashi, H. Sekine, C. Kim, “Application of Neural Network to One-Day-Ahead 24 hours Generating Power Forecasting for Photovoltaic System,” 2007 International Conference on Intelligent Systems Applications to Power Systems, pp. 1–6, 2007. Available: DOI: 10.1109/ISAP.2007.4441657.
F. Almonacid, P.J. Pérez-Higueras, Eduardo F. Fernández, L. Hontoria, “A methodology based on dynamic artificial neural network for short-term forecasting of the power output of a PV generator,” Energy Conversion and Management, pp. 389–398, vol. 85, 2014, Available: https://doi.org/10.1016/j.enconman.2014.05.090.
Changsong Chen, Shanxu Duan, Tao Cai, Bangyin Liu, “Online 24-h solar power forecasting based on weather type classification using artificial neural network,” Solar Energy, pp. 2856–2870, vol. 85, Available: https://doi.org/10.1016/j.solener.2011.08.027.
Ercan İzgi, Ahmet Öztopal, Bihter Yerli, Mustafa Kemal Kaymak, Ahmet Duran Şahin, “Short–mid-term solar power prediction by using artificial neural networks,” Solar Energy, pp. 725–733, vol. 86, 2012. Available: https://doi.org/10.1016/j.solener.2011.11.013.
Hugo T.C. Pedro, Carlos F.M. Coimbra, “Assessment of forecasting techniques for solar power production with no exogenous inputs,” Solar Energy, pp. 2017-2028, vol. 86, 2012. Available: https://doi.org/10.1016/j.solener.2012.04.004.
Jie Shi, Wei-Jen Lee, Yongqian Liu, Yongping Yang and Peng Wang, “Forecasting power output of photovoltaic system based on weather classification and support vector machine,” 2011 IEEE Industry Applications Society Annual Meeting, pp. 1–6, 2011. Available: DOI: 10.1109/IAS.2011.6074294.
Monowar Hossain, Saad Mekhilef, Malihe Danesh, Lanre Olatomiwa, Shahaboddin Shamshirband, “Application of extreme learning machine for short term output power forecasting of three grid-connected PV systems,” Journal of Cleaner Production, pp. 395–405, vol. 167, 2017. Available: https://doi.org/10.1016/j.jclepro.2017.08.081.
J. Liu,W. Fang, X. Zhang, and C. Yang, “An improved photovoltaic power forecasting model with the assistance of aerosol index data,” IEEE Trans. Sustain. Energy, pp. 434–442, vol. 6, 2015. Available: DOI: 10.1109/TSTE.2014.2381224.
A. Gensler, J. Henze, B. Sick and N. Raabe, “Deep Learning for solar power forecasting An approach using Auto Encoder and LSTM Neural Networks,” 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2016, pp. 002858–002865, Available: DOI: 10.1109/SMC.2016.7844673.
W. Lee, K. Kim, J. Park, J. Kim and Y. Kim, “Forecasting Solar Power Using Long-Short Term Memory and Convolutional Neural Networks,” IEEE Access, pp. 73068–73080, vol. 6, 2018. Available: DOI: 10.1109/ACCESS.2018.2883330.
Lee D., Kim K, “Recurrent Neural Network-Based Hourly Prediction of Photovoltaic Power Output Using Meteorological Information,” Energies, 12(2):215, 2019. Available: https://doi.org/10.3390/en12020215.
Abdel-Nasser, M., Mahmoud, K, “Accurate photovoltaic power forecasting models using deep LSTM-RNN,” Neural Comput & Applic 31, pp. 2727–2740 2019. Available: https://doi.org/10.1007/s00521-017-3225-z.
Yoonhwa Jung, Jaehoon Jung, Byungil Kim, SangUk Han, “Long short-term memory recurrent neural network for modeling temporal patterns in long-term power forecasting for solar PV facilities: Case study of South Korea,” Journal of Cleaner Production, p. 119476, vol. 250, 2020. Available: https://doi.org/10.1016/j.jclepro.2019.119476.
Mingming Gao, Jianjing Li, Feng Hong, Dongteng Long, “Day-ahead power forecasting in a large-scale photovoltaic plant based on weather classification using LSTM,” Energy, vol. 187, 2019. Available: https://doi.org/10.1016/j.energy.2019.07.168.
Gao M., Li J, Hong F, Long D, “Short-Term Forecasting of Power Production in a Large-Scale Photovoltaic Plant Based on LSTM,” Applied Sciences, 9(15):3192, 2019. Available: https://doi.org/10.3390/app9153192
F. Mei, “Day-Ahead Nonparametric Probabilistic Forecasting of Photovoltaic Power Generation Based on the LSTM-QRA Ensemble Model,” IEEE Access, pp. 166138–166149, vol. 8, 2020. Available: DOI: 10.1109/ACCESS.2020.3021581.
Kejun Wang, Xiaoxia Qi, Hongda Liu, “Photovoltaic power forecasting-based LSTM-Convolutional Network,” Energy, vol. 189, 2019. Available: https://doi.org/10.1016/j.energy.2019.116225.
Wang Fei, Zhiming Xuan, Zhao Zhen, Kangping Li, Tieqiang Wang, Min Shi. “A day-ahead PV power forecasting method based on LSTM-RNN model and time correlation modification under partial daily pattern prediction framework,” Energy Conversion and Management, 212, 2020. Available: https://doi.org/10.1016/j.enconman.2020.112766
Chen B, Lin P, Lai Y, Cheng S, Chen Z, Wu L. “Very-Short-Term Power Prediction for PV Power Plants Using a Simple and Effective RCC-LSTM Model Based on Short Term Multivariate Historical Datasets,” Electronics, 2020. Available: https://doi.org/10.3390/electronics9020289
K. Kumba, S. P. Simon, K. Sundareswaran, P. S. R. Nayak, K. A. Kumar and N. P. Padhy, “Performance Evaluation of a Second-Order Lever Single Axis Solar Tracking System,” IEEE Journal of Photovoltaic, Vol. 12, Issue: 5, pp. 1219–1229, 23/06/2022. Available: https://ieeexplore.ieee.org/document/9828802
Harrou, Fouzi, Kadri, Farid, Sun, Ying. “Forecasting of Photovoltaic Solar Power Production Using LSTM Approach” In Advanced Statistical Modeling, Forecasting, and Fault Detection in Renewable Energy Systems, edited by Fouzi Harrou, Ying Sun. London: Intech Open, 2020. Available: Doi: 10.5772/intechopen.91248.
MathWorks, http://www.mathworks.com (2020).
R. Jiao, T. Zhang, Y. Jiang, H. He, “Short-term non-residential load forecasting based on multiple sequences LSTM recurrent neural network,” IEEE Access, pp. 59438–59448, vol. 6, 2018. Available: DOI: 10.1109/ACCESS.2018.2873712.
G. Chicco, V. Cocina, P. Di Leo, F. Spertino, A. Massi Pavan, “Error assessment of solar irradiance forecasts and AC power from energy conversion model in grid-connected photovoltaic systems,” Energies, vol. 9, no. 1, p. 8, 2015. Available: https://doi.org/10.3390/en9010008
M. A. E. Bhuiyan, F. Yang, N. K. Biswas, S. H. Rahat, T. J. Neelam, “Machine learning-based error modeling to improve GPM IMERG precipitation product over the Brahmaputra river basin,” Forecasting, pp. 248–266, Vol. 2, 2020. Available: https://doi.org/10.3390/forecast2030014
Diebold F.X., Mariano R., “Comparing predictive accuracy,” J. Bus. Econ. Stat., 1995, 13, 253–265.
Diebold F.X., “Element of Forecasting,” 4 ed., Thomson South-western: Cincinnati, OH, USA, 2007, pp. 257–287.
https://aeeeuropeenergy.com/