Solar Irradiance Forecasting Based on Deep Learning for Sustainable Electrical Energy in Cameroon

Reagan Jean Jacques MOLU, Reagan Jean Jacques MOLU, Wulfran FENDZI MBASSO, Serge Raoul DZONDE NAOUSSI, Serge Raoul DZONDE NAOUSSI, Saatong KENFACK TSOBZE, Saatong KENFACK TSOBZE, Patrice WIRA, Patrice WIRA

Abstract


Solar energy has been considered a clean and renewable form of energy to generate electricity. As a consequence, the use of solar photovoltaic energy has recently received increasing attention. However, the intermittent power generation resulting from the random nature of meteorological parameters leads to various challenges for the security and stability of power grids when this renewable energy is integrated into large-scale grids. Therefore, accurate forecasting of solar irradiance is gradually increasing its importance in reducing fluctuations in solar irradiance in system planning. With the development of artificial intelligence technologies, especially deep learning, an increasing number of models are being considered for forecasting due to their superior ability to deal with complex nonlinear problems. This paper presents a forecast of solar radiation based on Long Short-Term Memory (LSTM). Samples of the meteorological parameters from the city of Douala in Cameroon are used to assess the accuracy of the proposed forecast. The experimental results demonstrate that the LSTM has a better prediction performance with the RMSE=0.47W/m2 and MAE= 5.2813W/m2.


Keywords


forecasting, deep learning, Long Short-Term Memory (LSTM), solar irradiance, renewable energy

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References


Wang J, Zhong H, Lai X, Xia Q, Wang Y, Kang C. Exploring key weather factors from analytical modeling toward improved solar power forecasting. IEEE Trans Smart Grid. 2019; 10:1417–27.

Wan C, Zhao J, Song Y, Xu Z, Lin J, Hu Z. Photovoltaic and solar power forecasting for smart grid energy management. CSEE J Power Energy Syst. 2015; 1:38–46.

Koohi-Kamali S, Rahim N, Mokhlis H, Tyagi V. Photovoltaic electricity generator dynamic modeling methods for smart grid applications: a review. Renew Sustain Energy Rev. 2016; 57:131–72.

Koohi-Kamali S, Rahim N, Mokhlis H. Smart power management algorithm in microgrid consisting of photovoltaic, diesel, and battery storage plants considering variations in sunlight, temperature, and load. Energy Convers Manage. 2014; 84:562–82.

Ferlito S, Adinolfi G, Graditi G. Comparative analysis of data-driven methods online and offline trained to the forecasting of grid-connected photovoltaic plant production. Appl Energy. 2017; 205:116–29.

Graditi G, Ferlito S, Adinolfi G, Tina GM, Ventura C. Energy yield estimation of thin-film photovoltaic plants by using physical approach and artificial neural networks. Sol Energy. 2016; 130:232–43.

Majumder I, Dash PK, Bisoi R. Variational mode decomposition based low-rank robust kernel extreme learning machine for solar irradiation forecasting. Energy Convers Manage. 2018; 171:787–806.

De Giorgi MG, Congedo PM, Malvoni M. Photovoltaic power forecasting using statistical methods: impact of weather data. IET Sci Meas Technol. 2014; 8:90–7.

Yang D, Kleissl J, Gueymard CA, Pedro HTC, and Coimbra CFM, History and trends in solar irradiance and PV power forecasting: a preliminary assessment and review using text mining. Solar Energy. 2018; 168:60–101.

Ji W, Chee KC. Prediction of hourly solar radiation using a novel hybrid model of ARMA and TDNN. Solar Energy. 2011;85,5,808–817.

Voyant C, Muselli M, Paoli C, Nivet ML. Numerical weather prediction (NWP) and hybrid ARMA/ANN model to predict global radiation. Energy. 2012; 39:1341–355.

Wang Y, Wang C, Shi C, Xiao B. Short-term cloud coverage prediction using the ARIMA time series model. Remote Sensing Letters, vol. 9, no. 3, pp. 274–283 (2017).

Hassan J. ARIMA and regression models for prediction of daily and monthly clearness index. Renewable Energy. 2014; 68:421–427.

Dong Z, Yang D, Reindl T, Walsh WM. A novel hybrid approach based on self-organizing maps, support vector regression, and particle swarm optimization to forecasting solar irradiance. Energy. 2015; 82:570–577.

Jiang H, Dong Y, Xiao L. A multi-stage intelligent approach based on an ensemble of two-way interaction models for forecasting the global horizontal radiation of India. Energy Conversion and Management. 2017; 137:142–154.

Verbois H, Huva R, Rusydi A, Walsh W. Solar irradiance forecasting in the tropics using numerical weather prediction and statistical learning. Solar Energy. 2018; 162:265–277.

Perez R, Lorenz E, Pelland S. Beauharnois M, Van Knowe G, Hemker K, Heinemann D, Remund J, Muller S, Traunmuller W, Steinmauer G, Pozo D, Ruiz-Arias J, Lara-Fanengo V, Ramirez-Santigosa L, Gaston-Romero M, Luis M, Pomares. Comparison of numerical weather prediction solar irradiance forecasts in the US, Canada, and Europe. Solar Energy. 2013; 94:305–326.

Kamadinata JO, Ken TL, Suwa T. Sky image-based solar irradiance prediction methodologies using artificial neural networks. Renewable Energy. 2019; 134:837–845.

Y. Chu, H. T. C. Pedro, M. Li, and C. F. M. Coimbra. Real-time forecasting of solar irradiance ramps with smart image processing, Solar Energy. 2015; 114:91–104.

F Wang, Z Zhen, C Liu, Z Mi, BM Hodge, M Shafie-khal, JPS Catalao. Image phase shift invariance-based cloud motion displacement vector calculation method for ultra-short-term solar PV power forecasting. Energy Conversion and Management. 2018; 157:123–135.

Voyant C, Notton G, Kalogirou S, Fouilloy A, Nivet ML, Paoli C, Motte F. Machine learning methods for solar radiation forecasting: a review, Renewable Energy. 2017; 105:569–582.

Chu TP, Jhou JH, Leu YG. Image-based solar irradiance forecasting using recurrent neural networks. In: International Conference on System Science and Engineering (ICSSE), IEEE. 2020:1–4 ().

Justin D, Concepcion RS, Calinao HA, Alejandrino J, Dadios EP, Sybingco E. Using stacked long short-term memory with principal component analysis for short term prediction of solar irradiance based on weather patterns. In: 2020 IEEE Region 10 Conference (TENCON), IEEE. 2020:946–951.

Yu Y, Cao J, Zhu J. An LSTM short-term solar irradiance forecasting under complicated weather conditions. IEEE Access. 2019; 7:145651–145666.

Chandola D, Gupta H, Tikkiwal VA, Bohra MK. Multi-step ahead forecasting of global solar radiation for arid zones using deep learning. Procedia Comput. Sci. 2020; 167:626–635.

Srivastava S, Lessmann S. A comparative study of LSTM neural networks in forecasting day-ahead global horizontal irradiance with satellite data. Sol. Energy. 2018; 162:232–247.

S Hochreiter, J Schmidhuder. Long Short-Term Memory. Neural Computation 9(8). 1997: 1735-1780.

Kingma DP, Ba J. Adam: A method for stochastic optimization. ArXiv. 2014; arXiv:1412.6980.

Huang P, Wen C, Fu L, Peng Q, Tang Y. A deep learning approach for multi-attribute data: A study of train delay prediction in railway systems. Inf. Sci. 2020; 516:234–253.

Madondo M, Gibbons T. Learning and Modeling Chaos Using LSTM Recurrent Neural Networks.In Proceedings of the Midwest Instruction and Computing Symposium, Duluth, Minnesota, 2018;6–7.




DOI (PDF): https://doi.org/10.20508/ijsmartgrid.v7i2.279.g320

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