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Forecasting Photovoltaic Power Output Using Long Short-Term Memory and Neural Network Models

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The unpredictable and stochastic nature of solar energy brings forth an array of challenges to the planning, management, and operation of power grid systems as the fluctuation in the output can lead to cost increases, difficulty in grid integration and also cause issues with control and reliability of the system. Hence, forecasting of photovoltaic (PV) output assumes greater significance as it helps operators manage changes in the output and organize optimal schedules for power generation. This paper presents two deep learning models, Long Short- Term Memory and Back Propagation Neural Network, for forecasting PV power output and the comparison of their MSE values for the annual period. The input data was refined initially by performing correlation tests and accordingly wind speed was eliminated from the input dataset. The optimal MSE values for LSTM and BPNN network were 0.000626 and 0.1547 respectively. Both the models preformed effectively and LSTM model performed better than BPNN model due to better generalization capability. These modeling approaches can be employed for forecasting the future solar power output of a PV system to assist in optimal scheduling and planning of power grid system.
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Forecast; LSTM; Neural Network; Power Output; PV Power

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Solar Energy Industries Association. (Accessed 26 April 2021).
Available online:

J. M. Barrera, A. Reina, A. Maté, J.C.Trujillo, Solar Energy Prediction Model Based on Artificial Neural Networks and Open Data. Sustainability, vol. 12, n. 17, 2020, pp. 1-21.

P. Bacher, H. Madsen, H. A. Nielsen. Online Short Term Solar Power Forecasting, Solar Energy, vol. 83,n. 10, 2009, pp. 1772 - 1783.

S. Ramaswamy and P. K. Sadhu, Forecasting PV Power From Solar Irradiance and Temperature using Neural Networks, 2017 International Conference on Infocom Technologies and Unmanned Systems (Trends and Future Directions) (ICTUS), Dubai, United Arab Emirates, 2017, pp. 244-248.

M. Alanazi, M. Mahoor and A. Khodaei, Day-Ahead Solar Forecasting Based on Multi-Level Solar Measurements, 2018 IEEE/PES Transmission and Distribution Conference and Exposition (T&D), Denver, CO, 2018, pp. 1-9.

A. Sfetsos, A.H. Coonick, Univariate and Multivariate Forecasting of Hourly Solar Radiation with Artificial Intelligence Techniques. Solar Energy, 68, n. 2, 2000, pp.169- 178.

H. Ettayyehi, K. El Himdi, Artificial Neural Network for Forecasting One Day Ahead of Global Solar Irradiance (May 29, 2018). The ProceedingS of the International Conference on Smart Applications and Data Analysis for Smart Cities (SADASC'18), 27-28 February 2018.

E. İzgi, A. Öztopal, B. Yerli, M. K. Kaymak, A. D. Şahin, Short-mid-term Solar Power Prediction by using Artificial Neural Networks, Solar Energy, vol. 86, n. 2, 2012, pp. 725-733.

D. Pattanaik, S. Mishra, G.P. Khuntia, R. Dash, S. S. Chandra, An Innovative Learning Approach for Solar Power Forecasting using Genetic Algorithm and Artificial Neural Network, Open Engineering, vol. 10, n. 1, 2020, pp. 630-641.

H. Nazaripouya, B. Wang, Y. Wang, P. Chu, H. R. Pota, R. Gadh, Univariate time series prediction of solar power using a hybrid wavelet-ARMA-NARX prediction method, 2016 IEEE/PES Transmission and Distribution Conference and Exposition (T&D), 2016, pp. 1-5.

Y. Nie, Y. Sun, Y. Chen, R. Orsini, A. Brandt, PV power output prediction from sky images using convolutional neural network: The Comparison of Sky-condition-specific sub-models and an End-to-end Model. Journal of Renewable and Sustainable Energy, vol. 12, n. 4, 2020, 046101.

V. Suresh V, P. Janik , J. Rezmer , Z. Leonowicz, Forecasting Solar PV Output Using Convolutional Neural Networks with a Sliding Window Algorithm, Energies, vol. 13, n. 3, 2020, pp. 723.

A. Saberian, H. Hizam, M.A.M. Radzi, M.Z.A. Ab Kadir, M. Mirzaei, Modelling and Prediction of Photovoltaic Power Output Using Artificial Neural Networks, International Journal of Photoenergy, vol. 2014, 2014, pp.1-10.

I. Qasrawi, M. Awad, Prediction of the Power Output of Solar Cells Using Neural Networks: Solar Cells Energy Sector in Palestine, International Journal of Computer Science and Security, vol.9, n.6, 2015, pp.280-292.

Katruksa, S., Jiriwibhakorn, S., Evaluation of Mid-Term Load Forecasting Case Study Based on Adaptive Neuro-Fuzzy Inference System (ANFIS) and Artificial Neural Networks (ANNs), (2020) International Review of Electrical Engineering (IREE), 15 (4), pp. 283-293.

N. Premalatha, A.V. Arasu, Prediction of solar radiation for solar systems by using ANN models with different back propagation algorithms, Journal of Applied Research and Technology, Volume 14, n.3, 2016, pp. 206-214.

P.A.G.M.Amarasinghe, N.S.Abeygunawardana, T.N. Jayasekara, E.A. J. P. Edirisinghe, S. K. Abeygunawardane, Ensemble Models for Solar Power Forecasting-A Weather Classification Approach. AIMS Energy, vol. 8, n.2, 2020, pp. 252-271.

S. Kim, J. Jung, M.K.Sim, A Two-Step Approach to Solar Power Generation Prediction Based on Weather Data Using Machine Learning, Sustainability, vol. 11, n. 5, 2019, pp. 1-16.

N. Sharma, P. Sharma, D. Irwin, P. Shenoy, Predicting solar generation from weather forecasts using machine learning, 2011 IEEE International Conference on Smart Grid Communications (SmartGridComm), Brussels, Belgium, 2011, pp. 528-533.

Adam, K., Miyauchi, H., Optimization of a Photovoltaic Hybrid Energy Storage System Using Energy Storage Peak Shaving, (2019) International Review of Electrical Engineering (IREE), 14 (1), pp. 8-18.

Karlov, D., Prokazov, I., Bakshtanin, A., Matveeva, T., Kondratenko, L., Optimizing Neural Network Model Performance for Wind Energy Forecasting, (2021) International Review on Modelling and Simulations (IREMOS), 14 (3), pp. 185-193.

Sawitri, D., Heryanto, M., Suprijono, H., Purnomo, M., Kusumoputro, B., Vibration-Signature-Based Inter-Turn Short Circuit Identification in a Three-Phase Induction Motor Using Multiple Hidden Layer Back Propagation Neural Networks, (2018) International Review of Electrical Engineering (IREE), 13 (2), pp. 98-106.

A. Dairi, F. Harrou, Y. Sun, S. Khadraoui, Short-Term Forecasting of Photovoltaic Solar Power Production Using Variational Auto-Encoder Driven Deep Learning Approach, Journal of Applied Sciences, vol. 10, n 23, 2020, pp. 8400.

B. Brahma, R. Wadhvani, Solar Irradiance Forecasting Based on Deep Learning Methodologies and Multi-Site Data. Symmetry 2020, vol. 12, n. 11, 2020, pp. 1830.

F. Harrou, F. Kadri, Y. Sun, Forecasting of Photovoltaic Solar Power Production Using LSTM Approach, In L. Thomas (Ed.), Advanced Statistical Modeling, Forecasting, and Fault Detection in Renewable Energy Systems, (London: Intech Open, 2020).

V. Sharma, U. Cali, V. Hagenmeyer, R. Mikut, J.A.G. Ordiano, Numerical Weather Prediction Data Free Solar Power Forecasting with Neural Networks, Proceedings of the Ninth International Conference on Future Energy Systems, Association for Computing Machinery , June 2018, pp. 604-609, Karlsruhe, Germany.

C. Hua E. Zhu, L. Kuang, D. Pi, Short-term Power Prediction of Photovoltaic Power Station based on Long Short-term Memory-back-propagation, International Journal of Distributed Sensor Networks, October 2019.

S. Hochreiter, J. Schmidhuber, LSTM can solve hard long time lag problems. Proceedings of the Advances in Neural Information Processing Systems, Denver, CO, USA, 2-5 December 1996, pp. 473-479.

National Renewable Energy Laboratory. (accessed on 20 April 2021).
Available online:

S. Lobo, R.G. Bhavani, Performance and Comparative Analysis of a Medium Sized, Grid Connected Photovoltaic System for Some Locations in the UAE, Proceedings of 2018 IEEE PES Innovative Smart Grid Technologies Asia (ISGT Asia), May 22- 25, 2018, pp. 1215- 1220, Singapore.

Triqui, B., Benyettou, A., Diabetes Prediction Using Feature Selection, (2021) International Journal on Engineering Applications (IREA), 9 (2), pp. 94-103.

Ali, A., Abbas, N., Axial Capacity of Rectangular Concrete-Filled Steel Tube Columns Using Artificial Neural Network, (2021) International Review of Civil Engineering (IRECE), 12 (6), pp. 389-397.

Habib, T., Abouhogail, R., Modelling of Spacecraft Orbit via Neural Networks, (2021) International Review of Aerospace Engineering (IREASE), 14 (5), pp. 285-293.

Ebhota, V., Srivastava, V., Modeling Environmental Effects on Electromagnetic Signal Propagation Using Multi-Layer Perceptron Artificial Neural Network, (2020) International Journal on Communications Antenna and Propagation (IRECAP), 10 (3), pp. 175-182.


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