DOI: https://doi.org/10.14710/ijred.2022.43953

Univariate and Multivariate LSTM Models for One Step and Multistep PV Power Forecasting

1Energy and Agro-equipment Department, Hassan II Institute of Agronomy and Veterinary, Rabat 10112, Morocco

2School of Geomatics and Surveying Engineering, Hassan II Institute of Agriculture and Veterinary Medicine, Rabat 10112, Morocco

3LERMA, Higher School of Energy Engineering International University of Rabat Campus de l’UIR, Parc Technopolis Rocade de Rabat-Salé 11100 – Sala Al Jadida, Morocco

Received: 5 Jan 2022; Revised: 10 May 2022; Accepted: 30 May 2022; Available online: 10 Jun 2022; Published: 4 Aug 2022.
Editor(s): H Hadiyanto
Open Access Copyright (c) 2022 The Author(s). Published by Centre of Biomass and Renewable Energy (CBIORE)
Creative Commons License This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

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Abstract

The energy demand is increasing due to population growth and economic development. To satisfy this energy demand, the use of renewable energy is essential to face global warming and the depletion of fossil fuels. Photovoltaic energy is one of the renewable energy sources, widely used by several countries over the world. The integration of PV energy into the grid brings significant benefits to the economy and environment, however, high penetration of this energy also brings some challenges to the stability of the electrical grid, due to the intermittency of solar energy. To overcome this issue, the use of a forecasting system is one of the solutions to guarantee an effective integration of PV plants in the electrical grid. In this paper, a PV power ultra short term forecasting has been done by using univariate and multivariate LSTM models. Different combinations of input variables of the models and different timesteps forecasting were tested and compared. The main aim of this work is to study the influence of the different combinations of variables on the accuracy of the LSTM models for one-step forecasting and multistep forecasting and comparing the univariate and multivariate LSTM models with MLP and CNN models  . The results show that for one step forecasting, the use of a univariate model based on historical data of PV output power is sufficient to get accurate forecasting with 28.98W in MAE compared to multivariate models that can reach 35.39W. Meanwhile, for multistep forecasting, it is mandatory to use a multivariate model that has historical data of meteorological variables and PV output power in the input of LSTM model. Moreover, The LSTM model shows great accuracy compared to MLP and CNN especially in multistep PV power forecasting.

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Keywords: Photovoltaic power forecasting; LSTM model; One step and multistep forecasting; Univariate and Multivariate model; artificial recurrent neural network; Artificial intelligent

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Section: Original Research Article
Language : EN
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  1. Acharya, S.K., Wi, Y.-M., Lee, J.(2020): Day-Ahead Forecasting for Small-Scale Photovoltaic Power Based on Similar Day Detection with Selective Weather Variables. Electronics. 9, 1117 . https://doi.org/10.3390/electronics9071117
  2. Ahmad, M.W., Mourshed, M., 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
  3. Almadhor, A., Matin, M.A., Gao, D. (2019): Recurrent network based planning and management of PV based islanded microgrid. In: Matin, M., Dutta, A.K., and Lange, A.P. (eds.) Wide Bandgap Materials, Devices, and Applications IV. p. 15. SPIE, San Diego, United States. https://doi.org/10.1117/12.2532030
  4. Alzahrani, A., Ferdowsi, M., Shamsi, P., Dagli, C.H. (2017): Modeling and Simulation of Microgrid. Procedia Comput. Sci. 114, 392–400 . https://doi.org/10.1016/j.procs.2017.09.0
  5. Aslam, M., Lee, J.-M., Kim, H.-S., Lee, S.-J., Hong, S. (2019): Deep Learning Models for Long-Term Solar Radiation Forecasting Considering Microgrid Installation: A Comparative Study. Energies. 13, 147. https://doi.org/10.3390/en13010147
  6. Ayompe, L.M., Duffy, A., McCormack, S.J., Conlon, M. (2010): Validated real-time energy models for small-scale grid-connected PV-systems. Energy. 35, 4086–4091. https://doi.org/10.1016/j.energy.2010.06.021
  7. Biswas, P.P., Suganthan, P.N., Amaratunga, G.A.J. (2017): Optimal power flow solutions incorporating stochastic wind and solar power. Energy Convers. Manag. 148, 1194–1207. https://doi.org/10.1016/j.enconman.2017.06.071
  8. Chen, B., Lin, P., Lin, Y., Lai, Y., Cheng, S., Chen, Z., Wu, L. (2020): Hour-ahead photovoltaic power forecast using a hybrid GRA-LSTM model based on multivariate meteorological factors and historical power datasets. IOP Conf. Ser. Earth Environ. Sci. 431, 012059. https://doi.org/10.1088/1755-1315/431/1/012059
  9. Chu, Y., Urquhart, B., Gohari, S.M.I., Pedro, H.T.C., Kleissl, J. (2015), Coimbra, C.F.M.: Short-term reforecasting of power output from a 48 MWe solar PV plant. Sol. Energy. 112, 68–77. https://doi.org/10.1016/j.solener.2014.11.017
  10. Das, U.K., Tey, K.S., Seyedmahmoudian, M., Mekhilef, S., Idris, M.Y.I., Van Deventer, W., Horan, B., Stojcevski, A. (2018): Forecasting of photovoltaic power generation and model optimization: A review. Renew. Sustain. Energy Rev. 81, 912–928 . https://doi.org/10.1016/j.rser.2017.08.017
  11. Desai, M. and Shah, M. (2021) ‘An anatomization on breast cancer detection and diagnosis employing multi-layer perceptron neural network (MLP) and Convolutional neural network (CNN)’, Clinical eHealth, 4, 1–11. doi: 10.1016/j.ceh.2020.11.002
  12. Ding, M., Xu, Z., Wang, W., Wang, X., Song, Y., Chen, D. (2016): A review on China׳s large-scale PV integration: Progress, challenges and recommendations. Renew. Sustain. Energy Rev. 53, 639–652 . https://doi.org/10.1016/j.rser.2015.09.009
  13. Dolara, A., Leva, S., Manzolini, G. (2015): Comparison of different physical models for PV power output prediction. Sol. Energy. 119, 83–99 . https://doi.org/10.1016/j.solener.2015.06.017
  14. Erraissi, N., Raoufi, M., Aarich, N., Akhsassi, M., Bennouna, A. (2018): Implementation of a low-cost data acquisition system for “PROPRE.MA” project. Measurement. 117, 21–40. https://doi.org/10.1016/j.measurement.2017.11.058
  15. Fantidis, J.G., Bandekas, D.V., Potolias, C., Vordos, N. (2013): Cost of PV electricity – Case study of Greece. Sol. Energy. 91, 120–130. https://doi.org/10.1016/j.solener.2013.02.001
  16. Ghafoor, A., Munir, A. (2015): Design and economics analysis of an off-grid PV system for household electrification. Renew. Sustain. Energy Rev. 42, 496–502. https://doi.org/10.1016/j.rser.2014.10.012
  17. Golder, A., Jneid, J., Zhao, J., & Bouffard, F.. (2019) ‘Machine Learning-Based Demand and PV. Power Forecasts’, in 2019 IEEE Electrical Power and Energy Conference (EPEC). 2019 IEEE Electrical Power and Energy Conference (EPEC), Montreal, QC, Canada: IEEE, pp. 1–6. Doi: 10.1109/EPEC47565.2019.9074819
  18. Hossain, M.S., Mahmood, H. (2020): Short-Term Photovoltaic Power Forecasting Using an LSTM Neural Network. In: 2020 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT). pp. 1–5. IEEE, Washington, DC, USA. https://doi.org/10.1109/ISGT45199.2020.9087786
  19. Huertas Tato, J., Centeno Brito, M. (2018): Using Smart Persistence and Random Forests to Predict Photovoltaic Energy Production. Energies. 12, 100. https://doi.org/10.3390/en12010100
  20. Id Omar, N., Boukhattem, L. (2021), Oudrhiri Hassani, F., Bennouna, A., Oukennou, A.: Data of a PV plant implemented in hot semi-arid climate. Data Brief. 34, 106756 . https://doi.org/10.1016/j.dib.2021.106756
  21. Kabir, E., Kumar, P., Kumar, S., Adelodun, A.A., Kim, K.-H. (2018): Solar energy: Potential and future prospects. Renew. Sustain. Energy Rev. 82, 894–900 . https://doi.org/10.1016/j.rser.2017.09.094
  22. Kushwaha, V., Pindoriya, N.M. (2017): Very short-term solar PV generation forecast using SARIMA model: A case study. In: 2017 7th International Conference on Power Systems (ICPS). pp. 430–435. IEEE, Pune. https://doi.org/10.1109/ICPES.2017.8387332
  23. Li, P., Song, Y., Wang, P., Dai, L. (2018): A Multi-Feature Multi-Classifier System for Speech Emotion Recognition. In: 2018 First Asian Conference on Affective Computing and Intelligent Interaction (ACII Asia). pp. 1–6. IEEE, Beijing. https://doi.org/10.1109/ACIIAsia.2018.8470324
  24. Li, P., Zhou, K., Lu, X., Yang, S. (2020): A hybrid deep learning model for short-term PV power forecasting. Appl. Energy. 259, 114216. https://doi.org/10.1016/j.apenergy.2019.114216
  25. Li, Y., Su, Y., Shu, L. (2014): An ARMAX model for forecasting the power output of a grid connected photovoltaic system. Renew. Energy. 66, 78–89. https://doi.org/10.1016/j.renene.2013.11.067
  26. Lim, Y.S., Tang, J.H. (2014): Experimental study on flicker emissions by photovoltaic systems on highly cloudy region: A case study in Malaysia. Renew. Energy. 64, 61–70 . https://doi.org/10.1016/j.renene.2013.10.043
  27. Lu, H.J., Chang, G.W. (2018): A Hybrid Approach for Day-Ahead Forecast of PV Power Generation. IFAC-Pap. 51, 634–638. https://doi.org/10.1016/j.ifacol.2018.11.774
  28. Luo, X., Zhang, D., Zhu, X. (2021): Deep learning based forecasting of photovoltaic power generation by incorporating domain knowledge. Energy. 225,. https://doi.org/10.1016/j.energy.2021.120240
  29. Massaoudi, M., Refaat, S.S. (2021), Chihi, I., Trabelsi, M., Oueslati, F.S., Abu-Rub, H.: A novel stacked generalization ensemble-based hybrid LGBM-XGB-MLP model for Short-Term Load Forecasting. Energy. 214, 118874 . https://doi.org/10.1016/j.energy.2020.118874
  30. Mellit, A., Pavan, A.M. and Lughi, V. (2021) ‘Deep learning neural networks for short-term photovoltaic power forecasting’, Renewable Energy, 172, 276–288. doi: 10.1016/j.renene.2021.02.166
  31. Nasiri, M., Minaei, B., Sharifi, Z. (2017): Adjusting data sparsity problem using linear algebra and machine learning algorithm. Appl. Soft Comput. 61, 1153–1159. https://doi.org/10.1016/j.asoc.2017.05.042
  32. Nath, P., Saha, P., Middya, A. I., & Roy, S. (2021). Long-term time-series pollution forecast using statistical and deep learning methods. Neural Computing and Applications, 33(19), 12551-12570. Omar Nour-eddine, I., Lahcen, B., Hassani Fahd, O., Amin, B., aziz, O. (2021): Power forecasting of three silicon-based PV technologies using actual field measurements. Sustain. Energy Technol. Assess. 43, 100915. https://doi.org/10.1016/j.seta.2020.100915
  33. Ospina, J., Newaz, A., Faruque, M.O. (2019): Forecasting of PV plant output using hybrid wavelet‐based LSTM‐DNN structure model. IET Renew. Power Gener. 13, 1087–1095 . https://doi.org/10.1049/iet-rpg.2018.5779
  34. Pan, C., Tan, J., Feng, D., Li, Y. (2019): Very Short-Term Solar Generation Forecasting Based on LSTM with Temporal Attention Mechanism. In: 2019 IEEE 5th International Conference on Computer and Communications (ICCC). Pp. 267–271. IEEE, Chengdu, China. https://doi.org/10.1109/ICCC47050.2019.9064298
  35. Pierro, M., Bucci, F., De Felice, M., Maggioni, E., Perotto, A., Spada, F., Moser, D., Cornaro, C. (2017): Deterministic and Stochastic Approaches for Day-Ahead Solar Power Forecasting. J. Sol. Energy Eng. 139, 021010 . https://doi.org/10.1115/1.4034823
  36. Predicting weather using LSTM. Available at: https://www.rsonline.com/designspark/predicting-weather-using-lstm (Accessed: 24 November 2021)
  37. Ramsami, P., Oree, V. (2015): A hybrid method for forecasting the energy output of photovoltaic systems. Energy Convers. Manag. 95, 406–413. https://doi.org/10.1016/j.enconman.2015.02.052
  38. Raza, M.Q., Nadarajah, M., Ekanayake, C. (2016): On recent advances in PV output power forecast. Sol. Energy. 136, 125–144. https://doi.org/10.1016/j.solener.2016.06.073
  39. Sharma, V., Yang, D., Walsh, W., Reindl, T. (2016): Short term solar irradiance forecasting using a mixed wavelet neural network. Renew. Energy. 90, 481–492. https://doi.org/10.1016/j.renene.2016.01.020
  40. Shi, J., Lee, W.-J., Liu, Y., Yang, Y., Wang, P. (2012): Forecasting Power Output of Photovoltaic Systems Based on Weather Classification and Support Vector Machines. IEEE Trans. Ind. Appl. 48, 1064–1069. https://doi.org/10.1109/TIA.2012.2190816
  41. Sivaneasan, B., Yu, C.Y., Goh, K.P. (2017): Solar Forecasting using ANN with Fuzzy Logic Pre-processing. Energy Procedia. 143, 727–732. https://doi.org/10.1016/j.egypro.2017.12.753
  42. Soman, S.S., Zareipour, H., Malik, O., Mandal, P. (2010): A review of wind power and wind speed forecasting methods with different time horizons. In: North American Power Symposium 2010. pp. 1–8. https://doi.org/10.1109/NAPS.2010.5619586
  43. Son, N., Jung, M. (2020): Analysis of Meteorological Factor Multivariate Models for Medium- and Long-Term Photovoltaic Solar Power Forecasting Using Long Short-Term Memory. Appl. Sci. 11, 316. https://doi.org/10.3390/app11010316
  44. Statistics Time Series, https://www.irena.org/Statistics/View-Data-by-Topic/Capacity-and-Generation/Statistics-Time-Series , last accessed 2021/10/05
  45. Stein, G., Letcher, T.M. (2018): 15 - Integration of PV Generated Electricity into National Grids. In: Letcher, T.M. and Fthenakis, V.M. (eds.) A Comprehensive Guide to Solar Energy Systems. pp. 321–332. Academic Press. https://doi.org/10.1016/B978-0-12-811479-7.00015-4
  46. Tsai, Y.-C., Chan, Y.-K., Ko, F.-K., Yang, J.-T. (2018): Integrated operation of renewable energy sources and water resources. Energy Convers. Manag. 160, 439–454. https://doi.org/10.1016/j.enconman.2018.01.062
  47. Voyant, C., Notton, G., Kalogirou, S., Nivet, M.-L., Paoli, C., Motte, F., Fouilloy, A. (2017): Machine learning methods for solar radiation forecasting: A review. Renew. Energy. 105, 569–582. https://doi.org/10.1016/j.renene.2016.12.095
  48. Wang, F., Zhen, Z., Liu, C., Mi, Z., Hodge, B.-M., Shafie-khah, M., Catalão, J.P.S. (2018): Image phase shift invariance based cloud motion displacement vector calculation method for ultra-short-term solar PV power forecasting. Energy Convers. Manag. 157, 123–135. https://doi.org/10.1016/j.enconman.2017.11.080
  49. Wang, F., Zhou, L., Ren, H., Liu, X., Talari, S., Shafie-khah, M., Catalao, J.P.S. (2018): Multi-Objective Optimization Model of Source–Load–Storage Synergetic Dispatch for a Building Energy Management System Based on TOU Price Demand Response. IEEE Trans. Ind. Appl. 54, 1017–1028. https://doi.org/10.1109/TIA.2017.2781639
  50. Yongsheng, D., Fengshun, J., Jie, Z., Zhikeng, L. (2020): A Short-Term Power Output Forecasting Model Based on Correlation Analysis and ELM-LSTM for Distributed PV System. J. Electr. Comput. Eng. 2020, 1–10. https://doi.org/10.1155/2020/2051232
  51. Yu, Y., Cao, J., Zhu, J. (2019): An LSTM Short-Term Solar Irradiance Forecasting Under Complicated Weather Conditions. IEEE Access. 7, 145651–145666. https://doi.org/10.1109/ACCESS.2019.2946057

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