Solar energy potential assessment and investment profitability analysis: The case of North Macedonia

Abstract

This study presents a novel integrated framework that combines meteorological data analysis, machine learning forecasting models, clustering techniques, and economic modeling to offer the first data-driven investment roadmap for solar energy in North Macedonia. High-accuracy solar radiation forecasts for 2025 were generated using the Gradient Boosting Regression model (R² = 0.953) using NASA POWER data for the period 2020–2024. Cities were grouped into clusters based on their climatological characteristics; cities such as Štip, Veles, and Kavadarci demonstrated the highest solar potential and economic efficiency. Economic analyses identified payback periods of 3–5 years and average ROI (Return on Investment) values ​​exceeding 24%. It was determined that increasing electricity prices linearly affected the ROI, reaching 33% with an increase in price to $0.12/kWh. The findings highlight the importance of a new integrated decision-support framework merging ML-based solar forecasting, spatial clustering, and dynamic economic modeling tailored for data-limited emerging markets such as North Macedonia

Keywords

• Economic feasibility
• Investment analysis
• Machine learning
• North Macedonia
• Solar energy

References

1. [1] Spasić V. Solar PV panels installed on 108 public buildings in North Macedonia. Balkan Green Energy News 2019. Available from: https://balkangreenenergynews.com/solar-pv-panels-installed-on-108-public-buildings-in-north-macedonia. Accessed: July 15, 2025. [Online].
2. [2] NASA POWER. Prediction of worldwide energy resources. 2024. Available from: https://power.larc.nasa.gov. Accessed: July 16, 2025. [Online].
3. [3] Cristea M, Cristea C, Tîrnovan RA, Șerban FM. Levelized Cost of Energy (LCOE) of Different Photovoltaic Technologies. Applied Sciences 2025;15(12):6710.
4. [4] Emblemsvåg J. Rethinking the ‘Levelized Cost of Energy’: A critical review and evaluation of the concept. Energy Research & Social Science 2025;119:103897.
5. [5] Manzolini G, et al. Limitations of using LCOE as economic indicator for solar power plants. Renewable and Sustainable Energy Reviews 2025;209:115087.
6. [6] Loth E, Qin C, Simpson JG, Dykes K. Why we must move beyond LCOE for renewable energy design. Advances in Applied Energy 2022;8:100112.
7. [7] Pfeifer A, Herc L, Bjelić IB, Duić N. Flexibility index and decreasing the costs in energy systems with high share of renewable energy. Energy Conversion and Management 2021;240:114258.
8. [8] Fraunhofer ISE. Photovoltaics report. Fraunhofer Institute for Solar Energy Systems ISE 2022. Available from: https://www.ise.fraunhofer.de. Accessed: July 16, 2025. [Online].

9. [9] IRENA. Renewable Power Generation Costs in 2022. International Renewable Energy Agency (IRENA) 2021. Available from: https://www.irena.org/publications/2023/Aug/Renewable-Power-Generation-Costs-in-2022. Accessed: July 19, 2025. [Online].
10. [10] Ghadim HV, et al. Are we too pessimistic? Cost projections for solar photovoltaics, wind power, and batteries are over-estimating actual costs globally. Applied Energy 2025;390:125856.
11. [11] Quick J, et al. Surrogate-based modeling and sensitivity analysis of future European electricity spot market prices. Electric Power Systems Research 2024;234:110675.
12. [12] Do HX, Nepal R, Pham SD, Jamasb T. Electricity market crisis in Europe and cross border price effects: A quantile return connectedness analysis. Energy Economics 2024;135:107633.
13. [13] Pavlík M, Bereš M, Kurimský F. Analyzing the Impact of Volatile Electricity Prices on Solar Energy Capture Rates in Central Europe: A Comparative Study. Applied Sciences 2024;14(15):6396.
14. [14] Navia Simon D, Diaz Anadon L. Power price stability and the insurance value of renewable technologies. Nature Energy 2025;10(3):329–341.
15. [15] Falk MT, Hagsten E. The impact of rising electricity prices on demand for photovoltaic solar systems. Energy Economics 2025;147:108583.
16. [16] Honrubia-Escribano A, et al. Influence of solar technology in the economic performance of PV power plants in Europe: A comprehensive analysis. Renewable and Sustainable Energy Reviews 2018;82:488–501.
17. [17] Lugo-Laguna D, Arcos-Vargas A, Nuñez-Hernandez F. A European assessment of the solar energy cost: key factors and optimal technology. Sustainability 2021;13(6):3238.
18. [18] European Commission. EU energy price statistics. 2023. Available from: https://ec.europa.eu/eurostat/web/energy/data/database. Accessed: July 19, 2025. [Online].
19. [19] Khouili O, et al. Evaluating the impact of deep learning approaches on solar and photovoltaic power forecasting: A systematic review. Energy Strategy Reviews 2025;59:101735.
20. [20] Rahimi N, et al. A comprehensive review on ensemble solar power forecasting algorithms. Journal of Electrical Engineering & Technology 2023;18(2):719–733.
21. [21] Gupta AK, Singh RK. A review of the state of the art in solar photovoltaic output power forecasting using data-driven models. Electrical Engineering 2025;107(4):4727–4770.
22. [22] Tsai WC, Tu CS, Hong CM, Lin WM. A review of state-of-the-art and short-term forecasting models for solar PV power generation. Energies 2023;16(14):5436.
23. [23] Vidal Bezerra FD, et al. Machine learning dynamic ensemble methods for solar irradiance and wind speed predictions. Atmosphere 2023;14(11):1635.
24. [24] Sehrawat N, Vashisht S, Singh A. Solar irradiance forecasting models using machine learning techniques and digital twin: A case study with comparison. International Journal of Intelligent Networks 2023;4:90–102.
25. [25] Yan K, et al. Short-term solar irradiance forecasting based on a hybrid deep learning methodology. Information 2020;11(1):32.
26. [26] Wentz VH, Maciel JN, Gimenez Ledesma JJ, Ando Junior OH. Solar irradiance forecasting to short-term PV power: Accuracy comparison of ANN and LSTM models. Energies 2022;15(7):2457.
27. [27] Zameer A, et al. Short-term solar energy forecasting: Integrated computational intelligence of LSTMs and GRU. PLoS One 2023;18(10):e0285410.
28. [28] Sharma G, Chandra S, Yadav AK, Gupta R. Enhancing solar radiation forecasting accuracy with a hybrid SA-Bi-LSTM-Bi-GRU model. Earth Science Informatics 2025;18(3):1–26.
29. [29] Salman D, Direkoglu C, Kusaf M, Fahrioglu M. Hybrid deep learning models for time series forecasting of solar power. Neural Computing and Applications 2024;36(16):9095–9112.
30. [30] Díaz-Bedoya D, González-Rodríguez M, Serrano-Guerrero X, Clairand JM. Solar irradiance forecasting with deep learning and ensemble models: LSTM, Random Forest and Extra Trees with multivariate meteorological data. IET Smart Grid 2025;8(1):e70019.
31. [31] Paletta Q, et al. Advances in solar forecasting: Computer vision with deep learning. Advances in Applied Energy 2023;11:100150.
32. [32] Travieso-González CM, Cabrera-Quintero F, Pinan-Roescher A, Celada-Bernal S. A review and evaluation of the state of art in image-based solar energy forecasting: The methodology and technology used. Applied Sciences 2024;14(13):5605.
33. [33] Canadian Solar. Datasheet: CS6K-280M. 2022. Available from: https://www.canadiansolar.com Accessed: July 18, 2025. [Online].
34. [34] NREL. Best research-cell efficiency chart. National Renewable Energy Laboratory 2023. Available from: https://www.nrel.gov/pv/cell-efficiency.html. Accessed: July 16, 2025. [Online].
35. [35] EIA. Electric Power Monthly. U.S. Energy Information Administration 2023. Available from: https://www.eia.gov/electricity/monthly. Accessed: July 17, 2025. [Online].
36. [36] IEA. World Energy Outlook. IEA 2023. Available from: https://www.iea.org/reports/world-energy-outlook-2023. Accessed: July 18, 2025. [Online].
37. [37] Adjiski V, Serafimovski D. GIS-and AHP-based Decision Systems for Evaluating Optimal Locations of Photovoltaic Power Plants: Case Study of Republic of North Macedonia. Geomatics and environmental engineering 2024;18(1):51.
38. [38] Smederevac S, Stojanović G. Open Science Practice in Western Balkan Countries. Primenjena psihologija 2023;16(4).
39. [39] UNECE. Environmental Performance Reviews: Third Review. Republic of North Macedonia. UN 2011. Available from: https://unece.org/sites/default/files/2021-12/ECE.CEP_.186.Eng_.pdf. Accessed: January 7, 2026. [Online].