Customized IoT Hardware and Artificial Neural Network-Assisted System for Prediction of Solar Radiation and Wind Speed

International Journal of Electrical and Electronics Engineering

© 2023 by SSRG - IJEEE Journal

Volume 10 Issue 5

Year of Publication : 2023

Authors : Pooja Malik, Praveen Kumar Malik, Rajesh Singh, Anita Gehlot, Shaik Vaseem Akram

10.14445/23488379/IJEEE-V10I5P109

How to Cite?

Pooja Malik, Praveen Kumar Malik, Rajesh Singh, Anita Gehlot, Shaik Vaseem Akram, "Customized IoT Hardware and Artificial Neural Network-Assisted System for Prediction of Solar Radiation and Wind Speed," SSRG International Journal of Electrical and Electronics Engineering, vol. 10,  no. 5, pp. 87-101, 2023. Crossref, https://doi.org/10.14445/23488379/IJEEE-V10I5P109

Abstract:

Renewable sources are essential in fulfilling affordable, clean, and sustainable energy requirements. Consistent wind speed forecasting and accurate solar radiation prediction are required to minimize economic losses and enhance the security of power usage. Predicting solar radiation and wind speed is difficult due to their uncertain behaviour. Motivated by these aspects, this study implemented an Internet of Things (IoT) and artificial neural network (ANN) based system for predicting solar radiation and wind speed. Initially, IoT and long-range (LoRa) enabled hardware was developed for obtaining real-time data. In this study, customized hardware was developed with the amalgamation of LoRa, WI-Fi and many sensors. Solar grid and windmill sensor nodes are customized hardware deployed to collect real-time data to create datasets that predict solar radiation and wind speed using wind speed, solar radiation, humidity, temperature, voltage, and current parameters. For the prediction, in this study, we have employed the ANN techniques, i.e., Levenberg Marquardt (LM) and Bayesian regularization (BR) methods. We have found that the proposed ANN framework using the LM algorithm provides accuracy in the prediction compared to the BR algorithm with less root mean square error (RMSE), mean square error (MSE), fast convergence speed, and more significant Correlation coefficient (R).

Keywords:

ANN, IoT, LoRa, Renewable energy, Solar radiation, Windmill.

References:

[1] Goal 7: Affordable and Clean Energy, Joint SDG Fund. [Online]. Available: https://www.jointsdgfund.org/sustainable-development-goals/goal-7-affordable-and-clean-energy
[2] Gülçin Büyüközkan, Yağmur Karabulut, and Esin Mukul, “A Novel Renewable Energy Selection Model for United Nations’ Sustainable Development Goals,” Energy, vol. 165, pp. 290–302, 2018.
[CrossRef][Google Scholar][Publisher Link]
[3] Mohamad Bilal, and Erna Andajani, “Factors Affecting the Intention to Use Roof Solar Panel in Households in Indonesia,” ADI Journal on Recent Innovation, vol. 5, no. 1, pp. 25–33, 2023.
[CrossRef][Google Scholar][Publisher Link]
[4] L. Suganthi, and A. Williams, “Renewable Energy in India - A Modelling Study for 2020–2021,” Energy Policy, vol. 28, no. 15, pp. 1095–1109, 2000.
[CrossRef][Google Scholar][Publisher Link]
[5] “Wind and Solar Share in Electricity Production Data Enerdata.” [Online]. Available: https://yearbook.enerdata.net/renewables/wind-solar-share-electricity-production.htmlhttps://yearbook.enerdata.net/renewables/wind-solar-share-electricity-production.html
[6] IRENA, Renewables 2019 Global Status Report, 2019.
[CrossRef][Google Scholar]
[7] India Plans to Produce 175 GW of Renewable Energy by 2022 - United Nations Partnerships for SDGs platform. [Online]. Available: https://sustainabledevelopment.un.org/partnership/?p=34566
[8] Global Installed Wind Energy Capacity 2020 Statista. [Online]. Available: https://www.statista.com/statistics/268363/installed-wind-power-capacity-worldwide/
[9] Renewable Energy - Our World in Data. [Online]. Available: https://ourworldindata.org/renewable-energy
[10] Ministry of New and Renewable Energy Renewable Energy in India, 09 SEP 2022. [Online]. Available: https://pib.gov.in/index.aspx
[11] S. P. Mishra, and P. K. Dash, “Short Term Wind Speed Prediction using Multiple Kernel Pseudo Inverse Neural Network,” International Journal of Automation and Computing, vol. 15, no. 1, pp. 66–83, 2017.
[CrossRef][Google Scholar][Publisher Link]
[12] Chaoli Tang et al., “Analysis and Prediction of Wind Speed Effects in East Asia and the Western Pacific Based on Multi-Source Data,” Sustainability, vol. 14, no. 19, pp. 12089, 2022.
[CrossRef][Google Scholar][Publisher Link]
[13] Sergio Herrería-Alonso et al., “A Solar Altitude Angle Model for Efficient Solar Energy Predictions,” Sensors, vol. 20, no. 5, pp. 1-14, 2020.
[CrossRef][Google Scholar][Publisher Link]
[14] Modise Wiston, and K. M. Mphale, “Weather Forecasting: From the Early Weather Wizards to Modern-Day Weather Predictions,” Journal of Climatology and Weather Forecasting, vol. 6, no. 2, pp. 1-9, 2018.
[CrossRef][Google Scholar][Publisher Link]
[15] Pooja Malik et al., “A Review on ANN Based Model for Solar Radiation and Wind Speed Prediction with Real-Time Data,” Archives of Computational Methods in Engineering, vol. 29, no. 5, pp. 3183–3201, 2022.
[CrossRef][Google Scholar][Publisher Link]
[16] Isam Shahrour, and Xiongyao Xie, “Role of Internet of Things (IoT) and Crowdsourcing in Smart City Projects,” Smart Cities, vol. 4, no. 4, pp. 1276–1292, 2021.
[CrossRef][Google Scholar][Publisher Link]
[17] Nitish Pathak et al., “Trends and Augmentations of Cloud Computing,” 2019 6th International Conference on Computing for Sustainable Global Development (INDIA Com), India, pp. 373-377, 2019.
[Google Scholar][Publisher Link]
[18] A. P. S. Rathod, P. Mittal, and B. Kumar, “Analysis of Factors Affecting the Solar Radiation Received by any Region,” 2016 International Conference on Emerging Trends in Communication Technologies (ETCT), India, pp. 1-4, 2016.
[CrossRef][Google Scholar][Publisher Link]
[19] Chia-Sheng Tu et al., “Short-Term Solar Power Forecasting via General Regression Neural Network with Grey Wolf Optimization,” Energies, vol. 15, no. 18,  2022.
[CrossRef][Google Scholar][Publisher Link]
[20] Ganesh V. Karbhari, and Pragya Nema, “IOT and Machine Learning Paradigm for Next Generation Solar Power Plant Monitoring System,” International Journal of Advanced Science and Technology, vol. 29, no. 3, pp. 7287-7295, 2020.
[Google Scholar][Publisher Link]
[21] Arti Rana et al., “Application of Multilayer (Perceptron) Artificial Neural Network in the Diagnosis System: A Systematic Review,” 2018 International Conference on Research in Intelligent and Computing in Engineering (RICE), El Salvador, pp. 1-6, 2018.
[CrossRef][Google Scholar][Publisher Link]
[22] Vassilis Z. Antonopoulos, “Solar Radiation Estimation Methods using ANN and Empirical Models,” Computers and Electronics in Agriculture, vol. 160, pp. 160-167, 2019.
[CrossRef][Google Scholar][Publisher Link]
[23] Farhad Elyasichamazkoti, and Abolhasan Khajehpoor, “Application of Machine Learning for Wind Energy from Design to Energy-Water Nexus: A Survey,” Energy Nexus, vol. 2, 2021.
[CrossRef][Google Scholar][Publisher Link]
[24] Mehmet Bilgili, and Besir Sahin, “Comparative Analysis of Regression and Artificial Neural Network Models for Wind Speed Prediction,” Meteorology and Atmospheric Physics, vol. 109, no. 1–2, pp. 61–72, 2010.
[CrossRef][Google Scholar][Publisher Link]
[25] Tyler Blanchard, and Biswanath Samanta, “Wind Speed Forecasting using Neural Networks,” Wind Engineering, vol. 44, no. 1, pp. 33–48, 2019.
[CrossRef][Google Scholar][Publisher Link]
[26] Qingguo Zhou, Chen Wang, and Gaofeng Zhang, “Hybrid Forecasting System Based on an Optimal Model Selection Strategy for Different Wind Speed Forecasting Problems,” Applied Energy, vol. 250, pp. 1559–1580, 2019.
[CrossRef][Google Scholar][Publisher Link]
[27] Hamed H. H. Aly, “An Intelligent Hybrid Model of Neuro Wavelet, Time Series and Recurrent Kalman Filter for Wind Speed Forecasting,” Sustainable Energy Technologies and Assessments, vol. 41, 2020.
[CrossRef][Google Scholar][Publisher Link]
[28] Qingyuan Wang et al., “A Hybrid Generative Adversarial Network Model for Ultra Short-Term Wind Speed Prediction,” Sustainability, vol. 14, no. 15, pp. 1-16, 2022.
[CrossRef][Google Scholar][Publisher Link]
[29] Yu Feng et al., “Machine Learning Models to Quantify and Map Daily Global Solar Radiation and Photovoltaic Power,” Renewable and Sustainable Energy Reviews, vol. 118, 2020.
[CrossRef][Google Scholar][Publisher Link]
[30] Faisal Nawab et al., “Evaluation of Artificial Neural Networks with Satellite Data Inputs for Daily, Monthly, and Yearly Solar Irradiation Prediction for Pakistan,” Sustainability, vol. 14, no. 13, pp. 1-20, 2022.
[CrossRef][Google Scholar][Publisher Link]
[31] Mustapha Mukhtar et al., “Development and Comparison of Two Novel Hybrid Neural Network Models for Hourly Solar Radiation Prediction,” Applied Sciences, vol. 12, no. 3, 2022.
[CrossRef][Google Scholar][Publisher Link]
[32] Tingting Zhu et al., “Inter-Hour Forecast of Solar Radiation Based on Long Short-Term Memory with Attention Mechanism and Genetic Algorithm,” Energies, vol. 15, no. 3,  2022.
[CrossRef][Google Scholar][Publisher Link]
[33] Jinjing An et al., “An IoT-Based Traceability Platform for Wind Turbines,” Energies, vol. 14, no. 9, 2021.
[CrossRef][Google Scholar][Publisher Link]
[34] S. Rafeek Ahmed et al., “Smart IOT Based Short Term Forecasting of Power Generation Systems and Quality Improvement using Resilient Back Propagation Neural Network,” Revista Gestão Inovaçãoe Tecnologias, vol. 11, no. 3, pp. 1200–1211, 2021.
[Google Scholar]
[35] Liu Hua, Zhang Junguo, and Lin Fantao, “Internet of Things Technology and its Applications in Smart Grid,” Telkomnika Indonesian Journal of Electrical Engineering, vol. 12, no. 2, 2014.
[CrossRef][Google Scholar][Publisher Link]
[36] Gustavo Costa Gomes de Melo et al., “A Low-Cost IoT System for Real-Time Monitoring of Climatic Variables and Photovoltaic Generation for Smart Grid Application,” Sensors, vol. 21, no. 9, 2021.
[CrossRef][Google Scholar][Publisher Link]
[37] Naser Hossein Motlagh et al., “Internet of Things (IoT) and the Energy Sector,” Energies, vol. 13, no. 2, pp. 494, 2020.
[CrossRef][Google Scholar][Publisher Link]
[38] A. Geetha et al., “Prediction of Hourly Solar Radiation in Tamil Nadu using ANN Model with Different Learning Algorithms,” Energy Reports, vol. 8, pp. 664–671, 2022.
[CrossRef][Google Scholar][Publisher Link]
[39] S. Belaid, and A. Mellit, “Prediction of Daily and Mean Monthly Global Solar Radiation using Support Vector Machine in An Arid Climate,” Energy Conversion and Management, vol. 118, pp. 105–118, 2016.
[CrossRef][Google Scholar][Publisher Link]
[40] Ya Lan Hu, and Liang Chen, “A Nonlinear Hybrid Wind Speed Forecasting Model using LSTM Network, Hysteretic ELM and Differential Evolution Algorithm,” Energy Conversion and Management, vol. 173, pp. 123–142, 2018.
[CrossRef][Google Scholar][Publisher Link]
[41] Kazım Kaba et al., “Estimation of Daily Global Solar Radiation using Deep Learning Model,” Energy, vol. 162, pp. 126–135, 2018.
[CrossRef][Google Scholar][Publisher Link]
[42] M. Madhiarasan, “Accurate Prediction of Different Forecast Horizons Wind Speed using A Recursive Radial Basis Function Neural Network,” Protection and Control of Modern Power Systems, vol. 5, no. 1, 2020.
[CrossRef][Google Scholar][Publisher Link]
[43] E. Cadenas, and W. Rivera, “Wind Speed Forecasting in Three Different Regions of Mexico, using a hybrid ARIMA–ANN Model,” Renewable Energy, vol. 35, no. 12, pp. 2732–2738, 2010.
[CrossRef][Google Scholar][Publisher Link]
[44] Sanju Saini, and Muskaan Ahuja, “Wind Speed Prediction using Wavelet Transform and GA Trained Artificial Neural Network,” Journal of Advanced Research in Dynamical and Control Systems, vol. 11, no. 0009-Special Issue, pp. 198–204, 2019.
[CrossRef][Publisher Link]
[45] Praveen Kumar Malik et al., “Village 4.0: Digitalization of Village with Smart Internet of Things Technologies,” Computers and Industrial Engineering, vol. 165, 2022.
[CrossRef][Google Scholar][Publisher Link]
[46] S. Daksha, and J. Ujjwala, “To Identify Global Solar Radiation Areas for Improving and Predicting the Solar Resources,” SSRG International Journal of Geoinformatics and Geological Science, vol. 1, no. 1, pp. 7-9, 2014.
[CrossRef][Publisher Link]
[47] Palak Kapoor, and Ferdous Ahmed Barbhuiya, “Cloud Based Weather Station using IoT Devices,” TENCON 2019 - 2019 IEEE Region 10 Conference (TENCON), India, pp. 2357-2362, 2019.
[CrossRef][Google Scholar][Publisher Link]
[48] Samy A. Khalil, “The Effect of Atmospheric Transmittance of UVB Solar Irradiance to Broadband Solar Radiation at Different Climate Sites,” SSRG International Journal of Applied Physics, vol. 9, no. 3, pp. 1-18, 2022.
[CrossRef][Publisher Link]
[49] Khushaboo Singh, and Pooran Meena, “Estimation of Changes in the Effectiveness of Parabolic Trough Solar Collector Due to Dust Particles,” SSRG International Journal of Mechanical Engineering, vol. 4, no. 7, pp. 40-47, 2017.
[CrossRef][Google Scholar][Publisher Link]
[50] ATmega2560 Microchip Technology. [Online]. Available: https://www.microchip.com/en-us/product/ATmega2560?gclid=CjwKCAjwuYWSBhByEiwAKd_n_tzCA3m2oZett8u7BbmQmcECoMWD-mNlvGRXODmUy7VaB9myQxO_mhoCpOMQAvD_BwE#
[51] W. Priharti, A. F. K. Rosmawati, I. P. D. Wibawa, “IoT Based Photovoltaic Monitoring System Application," In Journal of Physics: Conference Series, vol. 1367, pp. 012069, 2019.
[CrossRef][Google Scholar][Publisher Link]
[52] R. Dwi Teguh et al., “The Design of an Embedded System for Controlling Humidity and Temperature Room,” Journal of Physics: Conference Series, vol. 776, pp. 012096, 2016.
[CrossRef][Google Scholar][Publisher Link]
[53] Shaik Vaseem Akram et al., “Performance Analysis of IoT and Long-Range Radio-Based Sensor Node and Gateway Architecture for Solid Waste Management,” Sensors, vol. 21, no. 8, pp. 1-22, 2021.
[CrossRef][Google Scholar][Publisher Link]
[54] Swapnil Bagwari et al., “Low-Cost Sensor-Based and LoRa WAN Opportunities for Landslide Monitoring Systems on IoT Platform: A Review,” IEEE Access, vol. 10, pp. 7107–7127, 2022.
[CrossRef][Google Scholar][Publisher Link]
[55] Rajesh Singh et al., “Zigbee and Long-Range Architecture Based Monitoring System for Oil Pipeline Monitoring with the Internet of Things,” Sustainability, vol. 13, no. 18, pp. 1-25, 2021.
[CrossRef][Google Scholar][Publisher Link]
[56] Semtech Corporation, SX1276/77/78/79 - 137 MHz to 1020 MHz Low Power Long Range Transceiver, p. 133, 2016.
[Google Scholar]