Estimation of Electrical Energy Consumption in Irrigated Rice Crops in Southern Brazil

Daniel Lima Lemes, Matheus Mello Jacques, Natalia Bastos Sousa, Daniel Pinheiro Bernardon, Mauricio Sperandio (), Juliano Andrade Silva, Lucas M. Chiara and Martin Wolter
Additional contact information
Daniel Lima Lemes: Headquarters Campus, Federal University of Santa Maria, Santa Maria 97105-900, RS, Brazil
Matheus Mello Jacques: Headquarters Campus, Federal University of Santa Maria, Santa Maria 97105-900, RS, Brazil
Natalia Bastos Sousa: Headquarters Campus, Federal University of Santa Maria, Santa Maria 97105-900, RS, Brazil
Daniel Pinheiro Bernardon: Headquarters Campus, Federal University of Santa Maria, Santa Maria 97105-900, RS, Brazil
Mauricio Sperandio: Headquarters Campus, Federal University of Santa Maria, Santa Maria 97105-900, RS, Brazil
Juliano Andrade Silva: CPFL Energia, Campinas 13088-900, SP, Brazil
Lucas M. Chiara: CPFL Energia, Campinas 13088-900, SP, Brazil
Martin Wolter: Institute for Electrical Energy Systems (IESY), Otto-von-Guericke University Magdeburg (OVGU), 39106 Magdeburg, Sachsen-Anhalt, Germany

Energies, 2023, vol. 16, issue 18, 1-15

Abstract: On average, 70% of the world’s freshwater is used in agriculture, with farmers transitioning to electrical irrigation systems to increase productivity, reduce climate uncertainties, and decrease water consumption. In Brazil, where agriculture is a significant part of the economy, this transition has reached record levels over the last decade, further increasing the impact of energy consumption. This paper presents a methodology that utilizes the U-Net model to detect flooded rice fields using Sentinel-2 satellite images and estimates the electrical energy consumption required to pump water for this irrigation. The proposed approach involves grouping the detected flooded areas using k-means clustering with the electricity customers’ geographical coordinates, provided by the Power Distribution Company. The methodology was evaluated in a dataset of satellite images from southern Brazil, and the results demonstrate the potential of using U-Net models to identify rice fields. Furthermore, comparing the estimated electrical energy consumption required for irrigation in each cluster with the billed energy values provides valuable insights into the sustainable management of rice production systems and the electricity grid, helping to identify non-technical losses and improve irrigation efficiency.

Keywords: neural networks; image processing; irrigated rice crops; energy consumption (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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