Predicting Renewable Energy Resources using Machine Learning for Wireless Sensor Networks

Bhatti, Satyam; Khan, Ahsan Raza; Hussain, Sajjad; Ghannam, Rami

doi:10.1109/icecs202256217.2022.9970851

Cited by 3 publications

(1 citation statement)

References 10 publications

(10 reference statements)

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“…While this process may take time and resources, it is an integral part of maintaining the model's accuracy and reliability over time. [ 54 ]…”

Section: Discussionmentioning

confidence: 99%

A Machine Learning Frontier for Predicting LCOE of Photovoltaic System Economics

Bhatti,

Khan,

Zoha

et al. 2024

Adv Energy and Sustain Res

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In this research article, the objective is to determine the return on investment (ROI) of photovoltaic (PV) power plants by employing machine learning (ML) techniques. Special focus is done on the levelized cost of electricity (LCOE) as a pivotal economic parameter crucial for facilitating economic decision‐making and enabling quantitative comparisons among different energy generation technologies. Traditional methods of calculating LCOE often rely on fixed singular input values, which may fall short in addressing uncertainties associated with assessing the financial feasibility of PV projects. In response, a dynamic model that integrates essential demographic, energy, and policy data, is introduced encompassing factors such as interest rates, inflation rates, and energy yield, which are anticipated to undergo changes over the lifetime of a PV system. This dynamic model provides a more accurate estimation of LCOE. The comparative analysis of ML algorithms indicates that the auto‐regression integration moving average (ARIMA) model exhibits a high accuracy of 93.8% in predicting consumer electricity prices. The validation of the model is highlighted through two case studies in the United States and the Philippines underscores the potential impact on LCOE values. For instance, in California, LCOE values could vary by nearly 30% (5.03 cents kWh−1 for singular values vs 7.09 cents kWh−1 using our ML model), influencing the perceived risk or economic feasibility of a PV power plant. Additionally, the ML model estimates the ROI for a grid‐connected PV plant in the Philippines at 5.37 years, in contrast to 4.23 years using traditional methods.

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“…While this process may take time and resources, it is an integral part of maintaining the model's accuracy and reliability over time. [ 54 ]…”

Section: Discussionmentioning

confidence: 99%

A Machine Learning Frontier for Predicting LCOE of Photovoltaic System Economics

Bhatti,

Khan,

Zoha

et al. 2024

Adv Energy and Sustain Res

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Predicting Solar-Harvested Energy for Resource-Constrained IoT Devices Using Machine Learning

Khamitov,

Orel,

Zorbas

2024

2024 20th International Conference on Distributed Computing in Smart Systems and the Internet of Things (DCOSS-IoT)

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Computational Optimization for CdS/CIGS/GaAs Layered Solar Cell Architecture

Bhatti,

Manzoor,

Zoha

et al. 2024

Energies

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Multi-junction solar cells are vital in developing reliable, green, sustainable solar cells. Consequently, the computational optimization of solar cell architecture has the potential to profoundly expedite the process of discovering high-efficiency solar cells. Copper indium gallium selenide (CIGS)-based solar cells exhibit substantial performance compared to those utilizing cadmium sulfide (CdS). Likewise, CIGS-based devices are more efficient according to their device performance, environmentally benign nature, and thus, reduced cost. Therefore, the paper introduces an optimization process of three-layered n-CdS/p-CIGS/p-GaAs (NPP)) solar cell architecture based on thickness and carrier charge density. An in-depth investigation of the numerical analysis for homojunction PPN-junction with the ’GaAs’ layer structure along with n-ZnO front contact was simulated using the Solar Cells Capacitance Simulator (SCAPS-1D) software. Subsequently, various computational optimization techniques for evaluating the effect of the thickness and the carrier density on the performance of the PPN layer on solar cell architecture were examined. The electronic characteristics by adding the GaAs layer on the top of the conventional (PN) junction further led to optimized values of the power conversion efficiency (PCE), open-circuit voltage (VOC), fill factor (FF), and short-circuit current density (JSC) of the solar cell. Lastly, the paper concludes by highlighting the most promising results of our study, showcasing the impact of adding the GaAs layer. Hence, using the optimized values from the analysis, thickness of 5 (μm) and carrier density of 1×1020 (1/cm) resulted in the maximum PCE, VOC, FF, and JSC of 45.7%, 1.16 V, 89.52%, and 43.88 (mA/m2), respectively, for the proposed solar cell architecture. The outcomes of the study aim to pave the path for highly efficient, optimized, and robust multi-junction solar cells.

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Predicting Renewable Energy Resources using Machine Learning for Wireless Sensor Networks

Cited by 3 publications

References 10 publications

A Machine Learning Frontier for Predicting LCOE of Photovoltaic System Economics

A Machine Learning Frontier for Predicting LCOE of Photovoltaic System Economics

Predicting Solar-Harvested Energy for Resource-Constrained IoT Devices Using Machine Learning

Computational Optimization for CdS/CIGS/GaAs Layered Solar Cell Architecture

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