Predicting Battery Storage of Residential PV Using Long Short-Term Memory

Rizky Rakasiwi - Politeknik Negeri Semarang, Indonesia
Kurnianingsih Kurnianingsih - Politeknik Negeri Semarang, Indonesia
Amin Suharjono - Politeknik Negeri Semarang, Indonesia
I Ketut Enriko - PT Telekomunikasi Indonesia, Indonesia
Naoyuki Kubota - Tokyo Metropolitan University, Japan

Citation Format:



Solar power panels, or photovoltaic (PV), have recently grown rapidly as a renewable alternative energy source, especially since the increase in the basic electricity tariff. PV technology can be employed instead of the state electricity company to reduce the electricity used. Indonesia is one of the countries that have great potential in producing electricity from PV technology, considering that most of Indonesia's territory gets sunlight for most of the year and has a large land area. Considering the benefits of PV technology, it is necessary to carry out predictive monitoring and analysis of the energy generated by PV technology to maximize energy utilization in the future. The Internet of Things (IoT) and cloud computing system was developed in this research to monitor and collect data in real-time within 27 days and obtained 7831 data for each parameter that affects PV production. These data include data on the light intensity, temperature, and humidity at the location where the PV system is installed. The feature selection results using Pearson correlation revealed that the light intensity parameter significantly impacted the PV production system. This research used the Long Short-Term Memory (LSTM) method to predict future PV production. By tuning hyperparameters using 3000 epochs, the resulting RMSE value was 171.5720. The results indicated a significant change in the RMSE value compared to 100 epochs of 422.5780. This model can be applied as a forecasting system model at electric vehicle charging stations, given the increasing use of electric vehicles in the future.     

Keywords— Forecasting; energy; Photovoltaic; LSTM; Internet of Thing.



Forecasting; energy; Photovoltaic; LSTM; Internet of Thing

Full Text:



P. Pawar, M. T. Kumar, P. Vittal K, ”An IoT based Intelligent Smart Energy Management System with accurate forecasting and load strategy for renewable generation,” Measurement, vol. 152, p.107187, 2019, doi:

P. M. Fishbane, S. Gasirowicz, and S.T. Thornton: Physics for scientists and engineers, 2nd edition, Prentice-hall, New Jersey, 1996.

R. Hasan, S. Mekhilef, M. Seyedmahmoudian, and B. Horan, “Gridconnected isolated PV microinverters : A review,” Renewable and Sustainable Energy Reviews, Vol. 67, pp.1065-1080, 2017.

A. A. Jamali, N.M. Nor, and T. Ibrahim, “Energy storage systems and their sizing techniques in power system- A review,” IEEE Conference on Energy Conversion (CENCON), pp.4799-8598, 2015, doi: 10.1109/CENCON.2015.7409542

V. Kavitha & V. Malathi, “A Smart Solar PV Monitoring System Using IoT,” University Regional Campus, Madurai, India, 2019. doi: 10.5121/csit.2019.91502.

O. Chieochan, A. Saokaew, E. Boonchieng, “Internet of Things (IOT) for smart solar energy: A case study of the smart farm at Maejo University,” International Conference on Control, Automation and Information Sciences (ICCAIS), 2017, doi: 10.1109/ICCAIS.2017.8217588.

R. Liang, Y. Guo, L. Zhao, and Y. Gao, “Real-time monitoring implementation of PV/T façade system based on IoT,” Journal of Building Engineering, vol. 41, p.102451, 2021, doi:

A. Lopez-Vargas, M. Fuentes, and M. Vivar, “Current challenges for the advanced mass scale monitoring of Solar Home Systems: A review,” Renewable Energy, 163(2):2098-2114, 2020, doi:

K. Lakshmanna et al., “A Review on Deep Learning Techniques for IoT Data,” Electronics 2022, 11, 1604, doi:

S. Yerpude and T. K. Singhal, “Impact of Internet of Things (IoT) Data on Demand Forecasting,” Indian Journal of Science and Technology, Vol 10 (15), 2017, doi: 10.17485/ijst/2017/v10i15/111794.

P. Schober et al, “Correlation Coefficients: Appropriate Use and Interpretation,” Anesthesia & Analgesia, vol. 126(5): p 1763-1768, May 2018, doi: 10.1213/ANE.0000000000002864.

A. G. Asuero, A. Sayago, and A. G. Gonz´alez. “The Correlation Coefficient: An Overview”. Department of Analytical Chemistry, Faculty of Pharmacy, The University of Seville, Seville, Spain, 2006. Doi : 10.1080/10408340500526766.

A. Elamim et al., “Photovoltaic Output Power Forecast Using Artificial Neural Networks,” Journal of Theoretical and Applied Information Technology, vol. 96(15): 5116 – 5126, 2018.

Z. Li, J. Yang, P. A. N. Dezfuli, “Study on the Influence of Light Intensity on the Performance of Solar Cell,” International Journal of Photoenergy, vol. 2021, Article ID: 6648739, doi:

X. Qing, Y. Niu, “Hourly day-ahead solar irradiance prediction using weather forecasts by LSTM,” Key Laboratory of Advanced Control and Optimization for Chemical Processes (East China University of Science and Technology), Ministry of Education, East China University of Science and Technology, Shanghai, China, 2018, doi:

F. Touati et al., “Photo-Voltaic (PV) Monitoring System, Performance Analysis and Power Prediction Models in Doha, Qatar,” Intech Open, 2020, doi: 10.5772/intechopen.92632.

J. Zeng, “Short-term solar power prediction using a support vector machine,” Renewable Energy, vol. 52:118-127, 2013, doi: 10.1016/j.renene.2012.10.009.

A. Graves, “Generating Sequences With Recurrent Neural Networks,” arXiv, :1308.0850, 2014, doi:

R. Zhang and Q. Zou, “Time Series Prediction and Anomaly Detection of Light Curve Using LSTM Neural Network,” Journal of Physics: Conference Series, vol. 1061, Issue 1, article id. 012012, 2018, doi: 10.1088/1742-6596/1061/1/012012.

Kurnianingsih, A. Wirasatriya, L. Lazuardi, N. Kubota, Nawi Ng, “IOD and ENSO-Related Time Series Variability and Forecasting of Dengue and Malaria Incidence in Indonesia,” International Symposium on Community-centric Systems (CcS), 2020, doi: 10.1109/CcS49175.2020.9231358.

P. W. Hardjita, Nurochman, R. Hidayat, “Sentiment Analysis of Tweets on Prakerja Card using Convolutional Neural Network and Naïve Bayes,” International Journal on Informatics for Development (IJID), Vol. 10 No. 2, 2021. doi:

S. Hochreiter and J. Schmidhuber, “Long Short-Term Memory,” MIT Press, Neural Computation , vol. 9, no. 8, p. 1735–1780, 1997, doi: 10.1162/neco.1997.9.8.1735.

L. Tashman, “Out-of-sample tests of forecasting accuracy: an analysis and review,” International Journal of Forecasting, vol. 16 (4), p.437 – 450, 2000, doi: 10.1016/S0169-2070(00)00065-0.

Suwarti, Wahyono, B. Prasetiyo. “Analisis Pengaruh Intensitas Matahari, Suhu Permukaan & Sudut Pengarah Terhadap Kinerja Panel Surya,” Jurnal Teknik Energi Vol 14 No. 3, 2018; 78 – 85, Polines, Semarang, Indonesia, 2018.