Abstract
References
- Blum, C. (2005). Ant colony optimization: Introduction and recent trends. Physics of Life reviews, 2(4), 353-373. Dorigo, M., & Stützle, T. (2019). Ant colony optimization: overview and recent advances. Springer.
- HassanzadehFard, H., Tooryan, F., Collins, E. R., Jin, S., & Ramezani, B. (2020). Design and optimum energy management of a hybrid renewable energy system based on efficient various hydrogen production. International Journal of Hydrogen Energy, 45(55), 30113-30128.
- Hossain, M. S., & Mahmood, H. (2020). Short-term photovoltaic power forecasting using an LSTM neural network and synthetic weather forecast. Ieee Access, 8, 172524-172533.
- Huang, W., Zhang, C., Zhang, X., Meng, J., Liu, X., & Yuan, B. (2019). Photovoltaic power prediction model based on weather forecast. 2019 IEEE Sustainable Power and Energy Conference (iSPEC),
- Irena, R. E. S. (2020). International renewable energy agency. Abu Dhabi, 2020.
- Liang, L., Su, T., Gao, Y., Qin, F., & Pan, M. (2023). FCDT-IWBOA-LSSVR: An innovative hybrid machine learning approach for efficient prediction of short-to-mid-term photovoltaic generation. Journal of Cleaner Production, 385, 135716.
- Mandal, P., Madhira, S. T. S., Meng, J., & Pineda, R. L. (2012). Forecasting power output of solar photovoltaic system using wavelet transform and artificial intelligence techniques. Procedia Computer Science, 12, 332-337.
- Members, R. (2023). Renewables 2023 Global Status Report. In: REN21, Paris, France.
- Nandihal, P., Pareek, P. K., De Albuquerque, V. H. C., RB, M., Khanna, A., & Kumar, V. S. (2022). Ant colony optimization based medical image preservation and segmentation. 2022 Second International Conference on Advanced Technologies in Intelligent Control, Environment, Computing & Communication Engineering (ICATIECE)
- Netsanet, S., Zheng, D., Zhang, W., & Teshager, G. (2022). Short-term PV power forecasting using variational mode decomposition integrated with Ant colony optimization and neural network. Energy Reports, 8.
- Singh, S., Chauhan, P., & Singh, N. (2020). Capacity optimization of grid connected solar/fuel cell energy system using hybrid ABC-PSO algorithm. International Journal of Hydrogen Energy, 45(16), 10070-10088.
- Sultan, H. M., Diab, A. A. Z., Oleg, N. K., & Irina, S. Z. (2018). Design and evaluation of PV-wind hybrid system with hydroelectric pumped storage on the National Power System of Egypt. Global Energy Interconnection, 1(3), 301-311.
- Sultan, H. M., Menesy, A. S., Kamel, S., Korashy, A., Almohaimeed, S., & Abdel-Akher, M. (2021). An improved artificial ecosystem optimization algorithm for optimal configuration of a hybrid PV/WT/FC energy system. Alexandria Engineering Journal, 60(1), 1001-1025.
- Titri, S., Larbes, C., Toumi, K. Y., & Benatchba, K. (2017). A new MPPT controller based on the Ant colony optimization algorithm for Photovoltaic systems under partial shading conditions. Applied Soft Computing, 58, 465-479.
- Visser, L., AlSkaif, T., & Van Sark, W. (2019). Benchmark analysis of day-ahead solar power forecasting techniques using weather predictions. 2019 IEEE 46th photovoltaic specialists conference (PVSC)
- Xia, K., Li, Y., & Zhu, B. (2024). Improved photovoltaic MPPT algorithm based on ant colony optimization and fuzzy logic under conditions of partial shading. Ieee Access.
- Zhang, H., Li, D., Tian, Z., & Guo, L. (2021). A short-term photovoltaic power output prediction for virtual plant peak regulation based on K-means clustering and improved BP neural network. 2021 11th International Conference on Power, Energy and Electrical Engineering (CPEEE)
Forecasting the Power Output of Photovoltaic Systems Using Ant Colony Optimization and Determining the Optimal Time Interval for Agricultural Use
Abstract
The rapid increase in global energy demand, driven by industrialization, population growth, and technological advances, has emphasized the importance of the transition to renewable energy sources. In addition, renewable energy, especially photovoltaic (PV) systems, has become a widespread energy source in agricultural applications to meet the energy needs in agricultural irrigation. However, the efficiency of PV systems varies depending on meteorological and environmental factors such as solar radiation and temperature. Therefore, predictability of energy production is a critical requirement for the effective operation of irrigation systems. In this study, the Ant Colony Optimization (ACO) algorithm is implemented to estimate the power output of PV systems using historical solar radiation, temperature, and actual power production data. Inspired by the foraging behavior of ants, the ACO algorithm optimizes the estimation process by iteratively refining the solutions based on pheromone trails and adaptive learning. The proposed method is evaluated using a case study on a solar power plant located in the Central Anatolian region of Türkiye. The accuracy of the model is evaluated by comparing the predicted values with the actual measurements at various time periods including hourly, daily, weekly, monthly and seasonal forecasts. The results show that the ACO based forecast model provides high accuracy in predicting the PV power output, with the Mean Absolute Percentage Error (MAPE) values improving as the forecast period increases. The findings of this study suggest that ACO is a robust and efficient optimization technique to improve PV power forecasting. The proposed model provides strategic information that can be used in energy planning of agricultural irrigation systems with high accuracy.
Keywords
Photovoltaic Systems Power Prediction Ant Colony Optimization Renewable Energy Solar Radiation
References
- Blum, C. (2005). Ant colony optimization: Introduction and recent trends. Physics of Life reviews, 2(4), 353-373. Dorigo, M., & Stützle, T. (2019). Ant colony optimization: overview and recent advances. Springer.
- HassanzadehFard, H., Tooryan, F., Collins, E. R., Jin, S., & Ramezani, B. (2020). Design and optimum energy management of a hybrid renewable energy system based on efficient various hydrogen production. International Journal of Hydrogen Energy, 45(55), 30113-30128.
- Hossain, M. S., & Mahmood, H. (2020). Short-term photovoltaic power forecasting using an LSTM neural network and synthetic weather forecast. Ieee Access, 8, 172524-172533.
- Huang, W., Zhang, C., Zhang, X., Meng, J., Liu, X., & Yuan, B. (2019). Photovoltaic power prediction model based on weather forecast. 2019 IEEE Sustainable Power and Energy Conference (iSPEC),
- Irena, R. E. S. (2020). International renewable energy agency. Abu Dhabi, 2020.
- Liang, L., Su, T., Gao, Y., Qin, F., & Pan, M. (2023). FCDT-IWBOA-LSSVR: An innovative hybrid machine learning approach for efficient prediction of short-to-mid-term photovoltaic generation. Journal of Cleaner Production, 385, 135716.
- Mandal, P., Madhira, S. T. S., Meng, J., & Pineda, R. L. (2012). Forecasting power output of solar photovoltaic system using wavelet transform and artificial intelligence techniques. Procedia Computer Science, 12, 332-337.
- Members, R. (2023). Renewables 2023 Global Status Report. In: REN21, Paris, France.
- Nandihal, P., Pareek, P. K., De Albuquerque, V. H. C., RB, M., Khanna, A., & Kumar, V. S. (2022). Ant colony optimization based medical image preservation and segmentation. 2022 Second International Conference on Advanced Technologies in Intelligent Control, Environment, Computing & Communication Engineering (ICATIECE)
- Netsanet, S., Zheng, D., Zhang, W., & Teshager, G. (2022). Short-term PV power forecasting using variational mode decomposition integrated with Ant colony optimization and neural network. Energy Reports, 8.
- Singh, S., Chauhan, P., & Singh, N. (2020). Capacity optimization of grid connected solar/fuel cell energy system using hybrid ABC-PSO algorithm. International Journal of Hydrogen Energy, 45(16), 10070-10088.
- Sultan, H. M., Diab, A. A. Z., Oleg, N. K., & Irina, S. Z. (2018). Design and evaluation of PV-wind hybrid system with hydroelectric pumped storage on the National Power System of Egypt. Global Energy Interconnection, 1(3), 301-311.
- Sultan, H. M., Menesy, A. S., Kamel, S., Korashy, A., Almohaimeed, S., & Abdel-Akher, M. (2021). An improved artificial ecosystem optimization algorithm for optimal configuration of a hybrid PV/WT/FC energy system. Alexandria Engineering Journal, 60(1), 1001-1025.
- Titri, S., Larbes, C., Toumi, K. Y., & Benatchba, K. (2017). A new MPPT controller based on the Ant colony optimization algorithm for Photovoltaic systems under partial shading conditions. Applied Soft Computing, 58, 465-479.
- Visser, L., AlSkaif, T., & Van Sark, W. (2019). Benchmark analysis of day-ahead solar power forecasting techniques using weather predictions. 2019 IEEE 46th photovoltaic specialists conference (PVSC)
- Xia, K., Li, Y., & Zhu, B. (2024). Improved photovoltaic MPPT algorithm based on ant colony optimization and fuzzy logic under conditions of partial shading. Ieee Access.
- Zhang, H., Li, D., Tian, Z., & Guo, L. (2021). A short-term photovoltaic power output prediction for virtual plant peak regulation based on K-means clustering and improved BP neural network. 2021 11th International Conference on Power, Energy and Electrical Engineering (CPEEE)
Details
| Primary Language | English |
|---|---|
| Subjects | Agricultural Energy Systems |
| Journal Section | Research Article |
| Authors | |
| Publication Date | July 23, 2025 |
| Submission Date | April 24, 2025 |
| Acceptance Date | June 14, 2025 |
| Published in Issue | Year 2025 Volume: 12 Issue: 3 |
