Work

An ANN–GA-Based Predictive Control for Solar–Diesel Hybrid Irrigation Systems in Dryland Agriculture

Year: 2026

Type: article

Abstract: Diesel-powered irrigation pumps are widely used in dryland agriculture but impose high fuel costs and carbon emissions. Solar Photovoltaic (PV) systems offer a cleaner alternative, but their performance is limited under fluctuating irradiance. Hybrid solar–diesel systems can improve stability, yet most existing studies focus on simulations or economic analyses rather than integrated adaptive control with physical implementation. This study develops and evaluates an adaptive solar–diesel hybrid irrigation system integrating PV generation, environmental sensing, an automatic transfer switch, and a predictive control algorithm based on an Artificial Neural Network optimized using a Genetic Algorithm (ANN–GA). The ANN predicts short-term solar irradiance using temperature and humidity inputs, while the GA optimizes model parameters to support real-time energy-source switching. A laboratory-scale prototype (100 Wp PV, 100 Ah battery, 0.5 HP pump) was tested under naturally fluctuating weather conditions over four days. The ANN–GA model achieved high predictive accuracy (coefficient of determination (R²) = 0.9481 for training; R² = 0.9042 for validation), with low prediction errors (Root Mean Square Error (RMSE) = 11.58 W m-2 and Mean Absolute Error (MAE) = 4.70 W m-2), thereby enabling stable, oscillation-free switching decisions. The hybrid system supplied 52% of total energy from PV, reducing diesel runtime by 52%, fuel consumption by 53%, and CO₂ emissions by 53% compared to a diesel-only system. Sensitivity analysis under low irradiance (<200 W m-2) revealed reduced efficiency, emphasizing the need for enhanced storage or complementary renewable sources. These results demonstrate that predictive control integrated with hybrid energy systems can significantly improve irrigation reliability, energy efficiency, and sustainability in tropical dryland environments. This study presents a fully implemented and experimentally validated framework for intelligent, energy-efficient irrigation systems that goes beyond simulation-based approaches.

Source: Engineering Technology & Applied Science Research

DOI: 10.48084/etasr.18683

Authors: Folkes E. Laumal, Sam Herodian, Erniati, Intan K. Wardhani, Annisa Nur Ichniarsyah, Edwin P. Hattu

Institutions: Politeknik Negeri Kupang, IPB University, Bogor Agricultural Development Polytechnic

Language: English

Topic: Photovoltaic System Optimization Techniques

Subfield: Renewable Energy, Sustainability and the Environment

Field: Energy

Domain: Physical Sciences

SDGs: Affordable and clean energy, Climate action, Industry, innovation and infrastructure, Zero hunger

Open Access status: gold

Estimated APC paid: $431