• Rudi Susanto Universitas Duta Bangsa Surakarta
  • Wiji Lestari Universitas Duta Bangsa Surakarta
  • Herliyani Hasanah Universitas Duta Bangsa Surakarta



solar panel, tropical region, energy.


The performance of solar panels affects the utilization of solar energy for daily life. This study aims to carry out the measurement of the performance of solar panels in Surakarta City is located between 110 ° 45 '15" and 110 ° 45" 35" East Longitude and between 7 ° 36" and 7 ° 56" South Latitude. Research used solar panels, current sensors, voltage sensors, temperature sensors, solar irradiance sensors, humidity sensors, Arduino and Labview. The solar panels 20 WP is used in the experiment. The measurement results obtained that the maximum energy value per day produced is 165 Wh and a minimum of 76.8 Wh with an average of 109.1 Wh. Temperature measurements were carried out in the range 37.2 0 C to 41.0 0C which is the normal temperature for PV operations. The average irradiation measurement is 1834.3 W/m2 while the average humidity is 32.5%. The relationship between energy and temperature, energy with solar irradiance and energy with humidity find using Pearson Product Moment Correlation (PPMC). The result show that the effect of temperature and solar irradiance were more significant than humidity.


Asilevi, P.J., Quansah, E., Amekudzi, L.K, Annor, T. and Klutse, N.A.K., 2019, Modeling the spatial distribution of Global Solar Radiation (GSR) over Ghana using the Angström-Prescott sunshine duration model, Scientific African, 4 (2019) e-00094 (Elsevier).

Asri, M.N.M., Hashim, N.H., Desa, W.N.S.M., and Ismail, D., 2016, Pearson Product Moment Correlation (PPMC)and Principal Component Analysis (PCA) for Objective Comparison and Source Determination of Unbranded Black Ballpoint Pen Inks. Australian Journal of forensic sciences.

Budiyanto, M.A., Nasruddin, and Lubis, M.H., 2019, Turbidity factor coefficient on the estimation of hourly solar radiation in Depok City, Indonesia, Energy Reports, 6 (2020) 761–766 (Elsevier).

Dolara, A. et al, (2012). Performance Analysis of a Single-Axis Tracking PV System. IEEE journal of photovoltaics, vol. 2, no. 4

Celik, A. N. and Muneer, T (2013). Neural network based method for conversion of solar radiation data”, Energy Conversion and Management 67 117–124

El-Ghonemy, A.M.K. (2012). Photovoltaic Solar Energy: Review. International Journal of Scientific & Engineering Research, Volume 3, Issue 11

Hasan, MH. et al. (2012). A review on energy scenario and sustainable energy in Indonesia. Renewable and Sustainable Energy Reviews 16 2316–2328.

Huld, T., Gottschalg, R, Beyer, HG. and Topic, M. (2010). Mapping the performance of PV Modules, Effects of Module Type and Data Averaging. Solar Energy 84 324–338.

Kazem, H.A. and Chaichan, M. T. (2015). Effect of Humidity on Photovoltaic Performance Based on Experimental Study,

International Journal of Applied Engineering Research, ISSN 0973-4562 Volume 10, Number 23 pp 43572-43577.

Kymakis, E., Kalykakis, S. and Papazoglou, T.M. (2009). Performance analysis of a grid connected photovoltaic park on the island of Crete,” Energy Convers. Managemend., vol. 50, pp. 433–438.

Lestari, W et al. (2019). Prediction of electrical energy for solar cells based on the weather in the solo city and surrounding areas. Journal of Physics: Conference Series1153 012036.

Nwokolo, S.C. and Ogbulezie, J.C., 2018, A quantitative review and classification of empirical models for predicting global solar radiation in West Africa, Beni-Suef University Journal of Basic and Applied Sciences, 7 (2018) 367–396 (Elsevier).

Rajeshwari B. et al. (2015). Environmental Effect Assessment On Performance Of Solar PV Panel. International Conference on Circuit, Power and Computing Technologies [ICCPCT]

Ratna, I et al. (2015). Maximum power point tracking for photovoltaic using incremental conductance method”, ICSEEA 2014. Energy Procedia 68 22 – 30.

Sudhakar, K. and Srivastava, T. (2014). Energy and exergy analysis of 36 W solar photovoltaic module. International Journal of Ambient Energy, 35:1, 51-57.

Tamizharasi, G., Kathiresan, S., Sreenivasan, K.S. (2014). Energy Forecasting using Artificial Neural Networks., International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, Vol. 3, Issue 3

Tasri, A. and Susilawati, A. (2014). Selection among renewable energy alternatives based on a fuzzy analytic hierarchy process in Indonesia. Sustainable Energy Technologies and Assessments, 7 34–44

Uberti, F. D., Faranda, R., Leva, S. and Ogliari, E. (2010). Performance ratio of a PV power plant: Different panel technologies comparison. Proc. Sol. Energy Tech., Milano, Italy, pp. 13–24

Vieira, R.G. et al. (2016). Comparative performance analysis between static solar panels and single-axis tracking system on a hot climate region near to the equator. Renewable and Sustainable Energy Reviews 64 672–681.

Yadav, A. K. and Chandel, S.S. (2012). Artificial Neural Network based Prediction of Solar Radiation for Indian Stations. International Journal of Computer Applications (0975 – 8887) Volume 50 – No.9

Yousif, J. H., Kaze, H. A. and Boland, J. (2017). Predictive Models for Photovoltaic Electricity Production in Hot Weather Conditions. Energie. 10. 971.