PV ENERGY FORECASTING USING DEEP LEARNING ALGORITHM
DOI:
https://doi.org/10.55766/sujst-2024-02-e02972Keywords:
CNNRNN, DL, GACNN, KNNSVM, LSTM, Solar Power ForecastingAbstract
Solar energy has vast potential in India which is a rapidly growing economy with diverse geographical features. Solar energy has intermittent behaviour and depends on geographical and weather conditions. Therefore, the reliability of the solar depends on the seamless operation of solar plants with the latest technologies. The main objective of power operator is to facilitate the renewable power sources intergeration for maintaining an uninterrupted power supply. To achieve this objective, researchers have employed various Deep Learning methods of machine learning, such as RNN, LSTM, CNN and SVM for accurate solar power forecasting with higher relibaility. In this paper, a GA-CNN deep learning algorithm is employed with an optimized hyperparameters technique for PV energy forecasting. This technique outperforms when compared with the other methods such as LSTM, KNN-SVM, and CNN-RNN techniques in terms of RMSE, MAE, MSE and R-Square performance indices. This method provides a better and more robust method of deep learning for solar PV energy forecasting.
References
Abbaskhah, A., Sedighi, H., Akbarzadeh, P., and Salavatipour, A. (2023). Optimization of horizontal axis wind turbine performance with the dimpled blades by using CNN and MLP models. Ocean Engineering, 276:114185. https://doi.org/10.1016/j.oceaneng.2023.114185
Abdel-Nasser, M. and Mahmoud, K. (2017). Accurate photovoltaic power forecasting models using deep LSTM-RNN. Neural Computing and Applications, 31(7):2727-2740. https://doi.org/10.1007/s00521-017-3225-z
Abuella, M. and Chowdhury, B. (2015). Solar power forecasting using artificial neural networks. 2015 North American Power Symposium (NAPS), Charlotte, NC, USA, 2015, pp. 1-5. https://doi.org/10.1109/NAPS.2015.7335176
Ahmad, M.W., Mourshed, M., and Rezgui, Y. (2018). Tree-based ensemble methods for predicting PV power generation and their comparison with support vector regression. Energy, 164:465-474. https://doi.org/10.1016/j.energy.2018.08.207
Ahmed, R., Sreeram, V., Mishra, Y., and Arif, M.D. (2020). A review and evaluation of the state-of-the-art in PV solar power forecasting: Techniques and optimization. Renewable and Sustainable Energy Reviews, 124:109792. https://doi.org/10.1016/j.rser.2020.109792
AlKandari, M. and Ahmad, I. (2019). Solar power generation forecasting using ensemble approach based on deep learning and statistical methods. Applied Computing and Informatics, ahead-of-print. https://doi.org/10.1016/j.aci.2019.11.002
AlSkaif, T., Dev, S., Visser, L., Hossari, M., and Sark, Wilfried van. (2020). A systematic analysis of meteorological variables for PV output power estimation. Renewable Energy, 153:12-22. https://doi.org/10.1016/j.renene.2020.01.150
Chen, T., Lehr, J.M., Lavrova, O., and Martínez-Ramón, M. (2018). Distribution feeder-level day-ahead peak load forecasting methods and comparative study. Iet Generation Transmission and Distribution, 12(13):3270-3278. https://doi.org/10.1049/iet-gtd.2017.1745
Cheng, L., Zang, H., Ding, T., Wei, Z., and Sun, G. (2021). Multi-Meteorological-Factor-Based Graph Modeling for Photovoltaic Power Forecasting. in IEEE Transactions on Sustainable Energy, 12(3):1593-1603. https://doi.org/10.1109/TSTE.2021.3057521
Cho, K., van Merriënboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., and Bengio, Yo. (2014). Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation. EMNLP 2014 - 2014 Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference, p. 1724-1734. https://doi.org/10.3115/v1/D14-1179
Chung, J., Gulcehre, C., Cho, K., and Bengio, Y. (2014). Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling. Neural and Evolutionary Computing, https://doi.org/10.48550/arXiv.1412.3555
da Silva Fonseca, J.G., Jr., Oozeki, T., Takashima, T., Koshimizu, G., Uchida, Y., and Ogimoto, K. (2012). Use of support vector regression and numerically predicted cloudiness to forecast power output of a photovoltaic power plant in Kitakyushu. Japan. Prog. Photovolt: Res. Appl., 20(7):874-882. https://doi.org/10.1002/pip.1152
de Paiva, G.M., Pimentel, S.P., Marra, E.G., de Alvarenga, B.P., Mussetta, M., and Leva, S. (2019). Intra-day forecasting of building-integrated PV systems for power systems operation using ANN ensemble. 2019 IEEE Milan PowerTech, Milan, Italy, pp. 1-5 https://doi.org/10.1109/PTC.2019.8810480
De, V., Teo, T.T., Woo, W.L., and Logenthiran, T. (2018). Photovoltaic Power Forecasting using LSTM on Limited Dataset. 2018 IEEE Innovative Smart Grid Technologies - Asia (ISGT Asia), Singapore, 2018, pp. 710-715. https://doi.org/10.1109/ISGT-Asia.2018.8467934
El Alani, O., Abraim, M., Ghennioui, H., Ghennioui, A., Ikenbi, I., and Dahr, F.E. (2021). Short term solar irradiance forecasting using sky images based on a hybrid CNN-MLP model. Energy Reports, 7:888-900. https://doi.org/10.1016/j.egyr.2021.07.053
Elsaraiti, M. and Merabet, A. (2022). Solar Power Forecasting Using Deep Learning Techniques. in IEEE Access, 10:31692-31698. https://doi.org/10.1109/ACCESS.2022.3160484
Gao, B., Huang, X., Shi, J., Tai, Y., and Zhang, J. (2020). Hourly forecasting of solar irradiance based on CEEMDAN and multi-strategy CNN-LSTM neural networks. Renewable Energy, 162:1665-1683. https://doi.org/10.1016/j.renene.2020.09.141
Guo, L. (2022). SAR image classification based on multi-feature fusion decision convolutional neural network. IET Image Processing, 16(1):1-10. https://doi.org/10.1049/ipr2.12323
Harrou, F. and Sun, Y. (2020). Advanced Statistical Modeling, Forecasting and Fault Detection in Renewable Energy Systems. IntechOpen, London, 210p.
Heidarpanah, M., Hooshyaripor, F., and Fazeli, M. (2023). Daily electricity price forecasting using artificial intelligence models in the Iranian electricity market. Energy, 263(Part E):126011. https://doi.org/10.1016/j.energy.2022.126011
Hong, Y.-Y., Chan, Y.-H., Cheng, Y.-H., Lee, Y.-D., Jiang, J.-L., Wang, S.-S. (2022a). Week-ahead daily peak load forecasting using genetic algorithm-based hybrid convolutional neural network. IET Generation, Transmission and Distribution, 16(12):2416-2424. https://doi.org/10.1049/gtd2.12460
Hong, Y.-Y., Taylar, J.V., and Fajardo, A.C. (2022b). Locational Marginal Price Forecasting Using Deep Learning Network Optimized by Mapping-Based Genetic Algorithm. in IEEE Access, 8:91975-91988. https://doi.org/10.1109/ACCESS.2020.2994444
Jalali, S.M.J., Ahmadian, S., Kavousi-Fard, A., Khosravi, A., and Nahavandi, S. (2022). Automated Deep CNN-LSTM Architecture Design for Solar Irradiance Forecasting. in IEEE Transactions on Systems, Man, and Cybernetics: Systems, 52(1):54-65. https://doi.org/10.1109/TSMC.2021.3093519
Jnr, E.O.-N., Ziggah, Y.Y., and Relvas, S. (2021). Hybrid ensemble intelligent model based on wavelet transform, swarm intelligence and artificial neural network for electricity demand forecasting. Sustainable Cities and Society, 66:102679. https://doi.org/10.1016/j.scs.2020.102679
Kim, T.Y. and Cho, S.B. (2019). Particle Swarm Optimization-based CNN-LSTM Networks for Forecasting Energy Consumption. 2019 IEEE Congress on Evolutionary Computation (CEC), Wellington, New Zealand, p. 1510-1516. https://doi.org/10.1109/CEC.2019.8789968
Li, G., Guo, S., and Cheng, C. (2023). Short-term Forecasting Approach Based on bidirectional long short-term memory and convolutional neural network for Regional Photovoltaic Power Plants. Sustainable Energy, Grids and Networks, 34:101019. https://doi.org/10.1016/j.segan.2023.101019
Li, G., Xie, S., Wang, B., Xin, J., Li, Y., and Du, S. (2020). Photovoltaic Power Forecasting With a Hybrid Deep Learning Approach. 2020, 8:175871-175880. https://doi.org/10.1109/ACCESS.2020.3025860
Li, M.-W., Xu, D.-Y., Geng, J., and Hong, W.-C. (2022). A hybrid approach for forecasting ship motion using CNN-GRU-AM and GCWOA. Applied Soft Computing, 114:108084. https://doi.org/10.1016/j.asoc.2021.108084
Li, Z.M., Xu, S. W., Cui, L.G., Li, G.Q., Dong, X.X., and Wu, J.Z. (2012). The Short-Term Forecast Model of Pork Price Based on CNN-GA. Advanced Materials Research, 628:350-358. https://doi.org/10.4028/www.scientific.net/AMR.628.350
Liu, C.H., Gu, J.C., and Yang, M.T. (2021a). A Simplified LSTM Neural Networks for One Day-Ahead Solar Power Forecasting. in IEEE Access, 9:17174-17195. https://doi.org/10.1109/ACCESS.2021.3053638
Liu, J., Shi, Q., Han, R., and Yang, J. (2021b). A Hybrid GA-PSO-CNN Model for Ultra-Short-Term Wind Power Forecasting. Energies, 14(20):6500.https://doi.org/10.3390/en14206500
Liu, Z.-F., Luo, S.-F., Tseng, M.-L., Liu, H.-M., Li, L., and Md Mashud, A.H. (2021c). Short-term photovoltaic power prediction on modal reconstruction: A novel hybrid model approach. Sustainable Energy Technologies and Assessments, 45:101048. https://doi.org/10.1016/j.seta.2021.101048
Mahmud, K., Azam, S., Karim, A., Zobaed, S., Shanmugam, B., and Mathur, D. (2021). Machine Learning Based PV Power Generation Forecasting in Alice Springs. in IEEE Access, 9:46117-46128. https://doi.org/10.1109/ACCESS.2021.3066494
Makridakis, S., Spiliotis, E., and Assimakopoulos, V. (2018). Statistical and Machine Learning forecasting methods: Concerns and ways forward. PLOS ONE, 13(3):e0194889. https://doi.org/10.1371/journal.pone.0194889
Mishra, M., Dash, P.B., Nayak, J., Naik, B., and Kumar Swain, S. (2020). Deep learning and wavelet transform integrated approach for short-term solar PV power prediction. Measurement, 166:108250. https://doi.org/10.1016/j.measurement.2020.108250
Mustaqeem., Ishaq, M., and Kwon, S. (2022). A CNN-Assisted deep echo state network using multiple Time-Scale dynamic learning reservoirs for generating Short-Term solar energy forecasting. Sustainable Energy Technologies and Assessments, 52(Part C):102275. https://doi.org/10.1016/j.seta.2022.102275
Obiora, C.N. and Ali, A. (2021). Hourly Photovoltaic Power Forecasting Using CNN-LSTM Hybrid Model. 2021 62nd International Scientific Conference on Information Technology and Management Science of Riga Technical University (ITMS), Riga, Latvia, 2021, pp. 1-6. https://doi.org/10.1109/ITMS52826.2021.9615335
Sekhar, C. and Dahiya, R. (2023). Robust framework based on hybrid deep learning approach for short term load forecasting of building electricity demand. Energy, 268:126660. https://doi.org/10.1016/j.energy.2023.126660
Singh, V.P., Vijay, V., Siddhartha Bhatt, M., and Chaturvedi, D.K. (2013). Generalized neural network methodology for short term solar power forecasting. 2013 13th International Conference on Environment and Electrical Engineering (EEEIC), Wroclaw, Poland, 2013, pp. 58-62. https://doi.org/10.1109/EEEIC-2.2013.6737883
Tosun, N., Sert, E., Ayaz, E., Yılmaz, E., and Göl, M. (2020). Solar Power Generation Analysis and Forecasting Real-World Data Using LSTM and Autoregressive CNN. 2020 International Conference on Smart Energy Systems and Technologies (SEST), Istanbul, Turkey, 2020, pp. 1-6. https://doi.org/10.1109/SEST48500.2020.9203124
Tovar, M., Robles, M., and Rashid, F. (2020). PV Power Prediction, Using CNN-LSTM Hybrid Neural Network Model. Case of Study: Temixco-Morelos, México. Energies, 13(24):6512. https://doi.org/10.3390/en13246512
Trigo-González, M., Batlles, F.J., Alonso-Montesinos, J., Ferrada, P., del Sagrado, J., Martínez-Durbán, M., Cortés, M., Portillo, C., and Marzo, A. (2019). Hourly PV production estimation by means of an exportable multiple linear regression model. Renewable Energy, 135:303-312. https://doi.org/10.1016/j.renene.2018.12.014
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., and Polosukhin, I. (2017). Attention Is All You Need. Advances in Neural Information Processing Systems, p. 5999-6009. https://doi.org/10.48550/arXiv.1706.03762
Wang, F., Xuan, Z., Zhen, Z., Li, K., Wang, T., and Shi, M. (2020a). 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, 212:112766. https://doi.org/10.1016/j.enconman.2020.112766
Wang, F., Zhang, Z., Chai, H., Yu, Y., Lu, X., Wang, T., and Lin, Y. (2019a). Deep Learning Based Irradiance Mapping Model for Solar PV Power Forecasting Using Sky Image. 2019 IEEE Industry Applications Society Annual Meeting, Baltimore, MD, USA, 2019, pp. 1-9. https://doi.org/10.1109/IAS.2019.8912348
Wang, H., Liu, Y., Zhou, B., and Li, C. (2020b). Taxonomy research of artificial intelligence for deterministic solar power forecasting. Energy Conversion and Management, 214:112909. https://doi.org/10.1016/j.enconman.2020.112909
Wang, K., Qi, X., and Liu, H. (2019b). Photovoltaic power forecasting based LSTM-Convolutional Network. Energy, 189:116225. https://doi.org/10.1016/j.energy.2019.116225
Wang, Y., Liao, W., and Chang, Y. (2018). Gated Recurrent Unit Network-Based Short-Term Photovoltaic Forecasting. Energies, 11(8):2163. https://doi.org/10.3390/en11082163
Yang, D., Kleissl, J., Gueymard, C.A., Pedro, H.T.C., and Coimbra, G.F.M. (2018). History and trends in solar irradiance and PV power forecasting: A preliminary assessment and review using text mining. Solar Energy, 168:60-101. https://doi.org/10.1016/j.solener.2017.11.023
Zha,W., Liu, Y., Wan, Y., Luo, R., Li, D., Yang, S., and Xu, Y. (2022). Forecasting monthly gas field production based on the CNN-LSTM model. Energy, 260:124889. https://doi.org/10.1016/j.energy.2022.124889
Zhang, H., Li, D., Tian, Z., and Guo, L. (2021). A Short-Term Photovoltaic Power Output Prediction for Virtual Plant Peak Regulation Based on K-means Clustering and Improved BP Neural Network. 2021 11th International Conference on Power, Energy and Electrical Engineering (CPEEE), Shiga, Japan, p. 241-244. https://doi.org/10.1109/CPEEE51686.2021.9383350
