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A Review on Environmental Parameters Monitoring Systems for Power Generation Estimation from Renewable Energy Systems

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Abstract

The transition towards renewable energy sources necessitates accurate monitoring of environmental parameters to estimate power generation from renewable energy systems. The rapid integration of renewable energy sources into the power grid has necessitated the development of efficient monitoring systems to optimise power generation and enhance overall system performance. This paper provides a comprehensive review of environmental parameters monitoring systems designed for estimating power generation from renewable energy sources. The focus is on the advancements in technology and methodologies employed in monitoring crucial environmental factors that influence the output of renewable energy systems. It explores the significance of environmental parameters, including solar irradiance, wind speed, temperature, and humidity, in determining the efficiency of solar and wind power generation. Various monitoring techniques and sensors used for real-time data acquisition are discussed, highlighting their accuracy and reliability in capturing diverse environmental conditions. It delves into the integration of advanced data analytics and machine learning algorithms for processing the vast amounts of data collected from monitoring systems. These techniques play a pivotal role in predicting power generation patterns, optimising energy output, and facilitating proactive maintenance of renewable energy infrastructure. Furthermore, the review discusses the challenges associated with environmental monitoring, such as data accuracy, sensor calibration, and communication issues. It also explores emerging technologies, such as Internet of Things (IoT) devices and remote sensing, that promise to address these challenges and enhance the robustness of environmental monitoring systems. It focuses on case studies and practical applications of environmental monitoring systems in different renewable energy projects. These case studies provide insights into the successful implementation of monitoring systems, their impact on energy yield, and the economic benefits derived from improved system efficiency. It consolidates the current state of environmental parameters monitoring systems for power generation estimation from renewable energy sources. It highlights the interdisciplinary nature of these systems, incorporating elements of meteorology, data science, and engineering. The synthesis of existing knowledge and identification of research gaps contribute to the ongoing efforts to enhance the reliability and efficiency of renewable energy systems in the face of a dynamic and changing climate. This review paper provides a comprehensive analysis of the existing literature on environmental parameters monitoring systems for estimating power generation from renewable energy sources. The paper explores various renewable energy systems, including solar, wind, and hydroelectric, and highlights the significance of monitoring environmental factors such as solar irradiance, wind speed, temperature, humidity, and water flow rate. More than 50+ papers are taken into consideration; they examine the methodologies, sensors, data acquisition techniques, and modeling approaches employed for power estimation. Additionally, the challenges, advancements, and future directions in this field are discussed to guide further research and development.

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Abbreviations

RES:

Renewable energy systems

PV:

Photovoltaic

EU:

European Union

PR:

Performance ratio

US:

United States

HVAC:

Heating, ventilation, and air conditioning systems

RTDs:

Resistance temperature detectors

RH:

Relative humidity

WSNs:

Wireless sensor networks

LoRaWAN:

Low Power Wide Area Networking

SVMs:

Support vector machines

ANNs:

Artificial neural networks

RMSE:

Root mean square error

EDA:

Exploratory data analysis

RNNs:

Recurrent neural networks

LightGBM:

Light gradient-boosting machine

LSTM:

Long short-term memory

GRU:

Gated recurrent unit

ARIMA:

Autoregressive integrated moving average

MAE:

Mean absolute error

R-squared:

Coefficient of determination

IoT:

Internet of things

WSNs:

Wireless sensor networks

ML:

Machine learning

AI:

Artificial intelligence

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Acknowledgements

SV would like to thank to Department of Biotechnology (DBT), Faculty of Engineering and Technology, Rama University, Kanpur. YLK would like to thank Department of EEE MLR Institute of Technology, and SK would like to thank Department of Electronics and Communication Engineering, K.S.R.M. College of Engineering, Kadapa, Andhra Pradesh, for providing necessary facilities during their study to complete this manuscript.

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Authors and Affiliations

  1. Department of Biotechnology, Faculty of Engineering and Technology, Rama University, GT Road, Kanpur, Uttar Pradesh, 209217, India

    Samakshi Verma

  2. Department of Electrical & Electronics Engineering, MLR Institute of Technology, Hyderabad, Telangana, India

    Yeluripati Lalitha Kameswari

  3. Department of Electronics and Communication Engineering, K.S.R.M. College of Engineering, Kadapa, Andhra Pradesh, 516005, India

    Sonu Kumar

  4. Manipur International University, Imphal, Manipur, 795140, India

    Sonu Kumar

Authors
  1. Samakshi Verma
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  2. Yeluripati Lalitha Kameswari
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  3. Sonu Kumar
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Contributions

 SV, YLK and SK wrote the manuscript. SK thoroughly checked, reviewed and also helped in manuscript writing. SV planned this work, supervised and also wrote the manuscript.

Corresponding author

Correspondence to Sonu Kumar.

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Verma, S., Kameswari, Y.L. & Kumar, S. A Review on Environmental Parameters Monitoring Systems for Power Generation Estimation from Renewable Energy Systems. BioNanoSci. (2024). https://doi.org/10.1007/s12668-024-01358-4

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