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A Solar Energy Forecast Model Using Neural Networks: Application for Prediction of Power for Wireless Sensor Networks in Precision Agriculture

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Abstract

Wireless sensor networks employed in field monitoring have severe energy and memory constraints. Energy harvested from the natural resources such as solar energy is highly intermittent. However, its future values can be predicted with reasonable accuracy. Forecasting future values of solar irradiance prolongs the wireless sensor networks lifetime by enabling efficient task scheduling. In this paper, we propose a model for forecasting solar energy for wireless sensor networks using feed forward neural networks and compare it with other models both in terms of accuracy and memory occupancy. Intensity of solar radiations is predicted 24 h ahead based on temperature, pressure, relative humidity, dew point, wind speed, zenith angle, hour of the day and historical values of solar intensity. The dataset of 4 months is used from National Renewable Energy Laboratory. The results indicate that the proposed model is quite efficient with coefficient of correlation (R2) and RMSE values 98.052 and 56.61 respectively.

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References

  1. Zhang, N., Wang, M., & Wang, N. (2002). Precision agriculture: A worldwide overview. Computers and Electronics in Agriculture,36(2-3), 113–132.

    Google Scholar 

  2. Kiani, F., & Seyyedabbasi, A. (2018). Wireless sensor network and internet of things in precision agriculture. Int. J. Adv. Comput. Sci. Appl.,9(8), 220–226.

    Google Scholar 

  3. Baghaee, S., Ulusan, H., Chamanian, S., Zorlu, O., Kulah, H., Uysal-Biyikoglu, E. (2013). Towards a vibration energy harvesting WSN demonstration testbed. In 2013 24th Tyrrhenian international workshop on digital communications-green ICT (TIWDC), pp. 1–6. IEEE.

  4. Lin, F.-T., Kuo, Y.-C., Hsieh, J.-C., Tsai, H.-Y., Liao, Y.-T., & Lee, H.-C. (2015). A self-powering wireless environment monitoring system using soil energy. IEEE Sensors Journal,15(7), 3751–3758.

    Google Scholar 

  5. Zou, T., Lin, S., Feng, Q., & Chen, Y. (2016). Energy-efficient control with harvesting predictions for solar-powered wireless sensor networks. Sensors,16(1), 53.

    Google Scholar 

  6. Alzahrani, A., Kimball, J. W., & Dagli, C. (2014). Predicting solar irradiance using time series neural networks. Procedia Computer Science,36, 623–628.

    Google Scholar 

  7. Zhang, Y., Beaudin, M., Taheri, R., Zareipour, H., & Wood, D. (2015). Day-ahead power output forecasting for small-scale solar photovoltaic electricity generators. IEEE Transactions on Smart Grid,6(5), 2253–2262.

    Google Scholar 

  8. Yan, W. (2012). Toward automatic time-series forecasting using neural networks. IEEE Transactions on Neural Networks and Learning Systems,23(7), 1028–1039.

    Google Scholar 

  9. Bergonzini, C., Brunelli, D., Benini, L. (2009). Algorithms for harvested energy prediction in batteryless wireless sensor networks. In 2009 3rd International workshop on advances in sensors and interfaces, pp. 144–149. IEEE.

  10. Wang, Y., Shen, Y., Mao, S., Cao, G., & Nelms, R. M. (2018). Adaptive learning hybrid model for solar intensity forecasting. IEEE Transactions on Industrial Informatics,14(4), 1635–1645.

    Google Scholar 

  11. 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. IEEE.

  12. Senjyu, T., Takara, H., Uezato, K., & Funabashi, T. (2002). One-hour-ahead load forecasting using neural network. IEEE Transactions on Power Systems,17(1), 113–118.

    Google Scholar 

  13. Yousif, J. H., Kazem, H. A., Alattar, N. N., & Elhassan, I. I. (2019). A comparison study based on artificial neural network for assessing PV/T solar energy production. Case Studies in Thermal Engineering,13, 100407.

    Google Scholar 

  14. https://nsrdb.nrel.gov/

  15. Wang, J., Zhong, H., Lai, X., Xia, Q., Wang, Y., & Kang, C. (2017). Exploring key weather factors from analytical modeling toward improved solar power forecasting. IEEE Transactions on Smart Grid,10(2), 1417–1427.

    Google Scholar 

  16. Yang, J., Rivard, H., & Zmeureanu, R. (2005). On-line building energy prediction using adaptive artificial neural networks. Energy and Buildings,37(12), 1250–1259.

    Google Scholar 

  17. Dumitru, C.-D., Gligor, A., & Enachescu, C. (2016). Solar photovoltaic energy production forecast using neural networks. Procedia Technology,22, 808–815.

    Google Scholar 

  18. Olawoyin, A., & Chen, Y. (2018). Predicting the future with artificial neural network. Procedia Computer Science,140, 383–392.

    Google Scholar 

  19. Bhaskar, K., & Singh, S. N. (2012). AWNN-assisted wind power forecasting using feed-forward neural network. IEEE Transactions on Sustainable Energy,3(2), 306–315.

    Google Scholar 

  20. Saad, E. W., Prokhorov, D. V., & Wunsch, D. C. (1998). Comparative study of stock trend prediction using time delay, recurrent and probabilistic neural networks. IEEE Transactions on Neural Networks,9(6), 1456–1470.

    Google Scholar 

  21. Schmidhuber, J. (2015). Deep learning in neural networks: An overview. Neural Networks,61, 85–117.

    Google Scholar 

  22. Didcock, N., Jakubek, S., & Kögeler, H.-M. (2015). Regularisation methods for neural network model averaging. Engineering Applications of Artificial Intelligence,41, 128–138.

    Google Scholar 

  23. Srivastava, R., Tiwari, A. N., Giri, V. K. (2018). Forecasting of solar radiation in india using various ANN models. In 2018 5th IEEE Uttar Pradesh section international conference on electrical, electronics and computer engineering (UPCON). IEEE.

  24. Praynlin, E., Jensona, J. I. (2017). Solar radiation forecasting using artificial neural network. In 2017 Innovations in power and advanced computing technologies (i-PACT). IEEE.

  25. Dey, S., Pratiher, S., Banerjee, S., Mukherjee, C. K. (2017). Solarisnet: A deep regression network for solar radiation prediction. arXiv preprint arXiv:1711.08413.

  26. Manjili, Y. S., Vega, R., & Jamshidi, M. M. (2017). Data-analytic-based adaptive solar energy forecasting framework. IEEE Systems Journal,12(1), 285–296.

    Google Scholar 

  27. Mellit, A., & Pavan, A. M. (2010). A 24-h forecast of solar irradiance using artificial neural network: Application for performance prediction of a grid-connected PV plant at Trieste, Italy. Solar Energy,84(5), 807–821.

    Google Scholar 

  28. Perveen, G., Rizwan, M., & Goel, N. (2019). Comparison of intelligent modelling techniques for forecasting solar energy and its application in solar PV based energy system. Energy Systems Integration,1(1), 34–51.

    Google Scholar 

  29. Vanderstar, G., Musilek, P., Nassif, A. (2018). Solar forecasting using remote solar monitoring stations and artificial neural networks. In 2018 IEEE Canadian conference on electrical and computer engineering (CCECE). IEEE.

  30. Salima, G., & Chavula, G. M. S. (2012). Determining Angstrom constants for estimating solar radiation in Malawi. International Journal of Geosciences,3(02), 391.

    Google Scholar 

  31. Hasni, A., Sehli, A., Draoui, B., Bassou, A., & Amieur, B. (2012). Estimating global solar radiation using artificial neural network and climate data in the south-western region of Algeria. Energy Procedia,18, 531–537.

    Google Scholar 

  32. Yan, X., Abbes, D., Francois, B. (2014). Solar radiation forecasting using artificial neural network for local power reserve. In 2014 International conference on electrical sciences and technologies in Maghreb (CISTEM), pp. 1–6. IEEE.

  33. Watetakarn, S., Premrudeepreechacharn, S. (2015). Forecasting of solar irradiance for solar power plants by artificial neural network. In 2015 IEEE innovative smart grid technologies-Asia (ISGT ASIA). IEEE.

  34. Alluhaidah, B. M., Shehadeh, S. H., El-Hawary, M. E. (2014). Most influential variables for solar radiation forecasting using artificial neural networks. In 2014 IEEE Electrical power and energy conference. IEEE.

  35. Asl, S. F. Z., Karami, A., Ashari, G., Behrang, A., Assareh, A., & Hedayat, N. (2011). Daily global solar radiation modeling using multi-layer perceptron (MLP) neural networks. World Academy of Science, Engineering and Technology,79, 740–742.

    Google Scholar 

  36. Behrang, M. A., Assareh, E., Ghanbarzadeh, A., & Noghrehabadi, A. R. (2010). The potential of different artificial neural network (ANN) techniques in daily global solar radiation modeling based on meteorological data. Solar Energy,84(8), 1468–1480.

    Google Scholar 

  37. Rehman, S., & Mohandes, M. (2008). Artificial neural network estimation of global solar radiation using air temperature and relative humidity. Energy Policy,36(2), 571–576.

    Google Scholar 

  38. Ghanbarzadeh, A., Noghrehabadi, A. R., Assareh, E., Behrang, M. A. (2009). Solar radiation forecasting based on meteorological data using artificial neural networks. In 2009 7th IEEE international conference on industrial informatics, pp. 227–231. IEEE.

  39. Bouguera, T., Diouris, J.-F., Andrieux, G., Chaillout, J.-J., & Jaouadi, R. (2018). A novel solar energy predictor for communicating sensors. IET Communications,12(17), 2145–2149.

    Google Scholar 

  40. Hyvarinen, A. (1999). Fast and robust fixed-point algorithms for independent component analysis. IEEE Transactions on Neural Networks,10(3), 626–634.

    Google Scholar 

  41. Al Shamisi, M. H., Assi, A. H., Hejase, H. A. N. (2011). Using MATLAB to develop artificial neural network models for predicting global solar radiation in Al Ain City–UAE. In Engineering education and research using MATLAB. IntechOpen.

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Acknowledgements

We thank National Renewable Energy Laboratory for providing us access to National Solar Radiation Database.

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Authors and Affiliations

  1. U.I.E.T, Panjab University, Chandigarh, India

    Sukham Dhillon, Charu Madhu, Daljeet Kaur & Sarvjit Singh

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  1. Sukham Dhillon
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  2. Charu Madhu
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  3. Daljeet Kaur
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  4. Sarvjit Singh
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Correspondence to Charu Madhu.

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Dhillon, S., Madhu, C., Kaur, D. et al. A Solar Energy Forecast Model Using Neural Networks: Application for Prediction of Power for Wireless Sensor Networks in Precision Agriculture. Wireless Pers Commun 112, 2741–2760 (2020). https://doi.org/10.1007/s11277-020-07173-w

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