Toward improving water-energy-food nexus through dynamic energy management of solar powered automated irrigation system

Yadav, Neelesh; Pattabiraman, Balasundaram; Tummuru, Narsa Reddy; Soundharajan, B.S.; Kasiviswanathan, K.S.; Adeloye, Adebayo J.; Sen, Subhamoy; Maurya, Mukesh; Vijayalakshmanan, S.

doi:10.1016/j.heliyon.2024.e25359

Cited by 5 publications

(1 citation statement)

References 22 publications

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“…The projections of photovoltaic electricity using the aforementioned models have shown positive results. Neelesh et al 39 proposed a model for optimal onsite solar power generation, and improved the capacity of storage to improve the solar irrigation system. The mechanism was based on several steps such as as data acquisition, soil moisture forecasting, smart irrigation scheduling, and energy management scheme.…”

Section: Introductionmentioning

confidence: 99%

Short-term photovoltaic energy generation for solar powered high efficiency irrigation systems using LSTM with Spatio-temporal attention mechanism

Awais,

Mahum,

Zhang

et al. 2024

Sci Rep

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Solar irrigation systems should become more practical and efficient as technology advances. Automation and AI-based technologies can optimize solar energy use for irrigation while reducing environmental impacts and costs. These innovations have the potential to make agriculture more environmentally friendly and sustainable. Solar irrigation system implementation can be hampered by a lack of technical expertise in installation, operation, and maintenance. It must be technically and economically feasible to be practical and continuous. Due to weather and solar irradiation, photovoltaic power generation is difficult for high-efficiency irrigation systems. As a result, more precise photovoltaic output calculations could improve solar power systems. Customers should benefit from increased power plant versatility and high-quality electricity. As a result, an artificial intelligence-powered automated irrigation power-generation system may improve the existing efficiency. To predict high-efficiency irrigation system power outputs, this study proposed a spatial and temporal attention block-based long-short-term memory (LSTM) model. Using MSE, RMSE, and MAE, the results have been compared to pre-existing ML and a simple LSTM network. Moreover, it has been found that our model outperformed cutting-edge methods. MAPE was improved by 6–7% by increasing Look Back (LB) and Look Forward (LF). Future goals include adapting the technology for wind power production and improving the proposed model to harness customer behavior to improve forecasting accuracy.

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Section: Introductionmentioning

confidence: 99%