@article {99348,
	title = {Forecast-integrated techno-economic optimization of off-grid hybrid renewable system in hyper-arid regions: Application to Tamanrasset, Algeria},
	journal = {Energy},
	volume = {334C},
	year = {2025},
	pages = {137468},
	abstract = {This study presents a novel framework for the optimal design of an off-grid residential energy system, applied to the hyper-arid region of Tamanrasset, Algeria. The proposed hybrid renewable energy system (HRES) integrates photovoltaic panels, wind turbines, battery storage, and diesel generators. A key innovation is the integration of a green energy forecasting module within a multi-objective techno-economic optimization process. Various machine learning and deep learning models suited for time series prediction were evaluated, and the best-performing models for each meteorological parameter were selected which enables precise long-term hourly forecasts, improving system design and operational efficiency compared to conventional methods based on historical averages. The optimization targets three objectives: minimizing the Levelized Cost of Energy (LCOE), reducing the Loss of Power Supply Probability (LPSP), and maximizing the Reliability Factor (RF). Using the Multi-Objective Particle Swarm Optimization (MOPSO) algorithm, the system achieves an LCOE of $0.05433/kWh, an LPSP of 3.1\&nbsp;\%, and an RF of 98\&nbsp;\%, indicating a strong balance between cost and reliability. Energy contributions are 47\&nbsp;\% from solar PV, 35\&nbsp;\% from batteries, 12\&nbsp;\% from wind, and 6\&nbsp;\% from diesel. Comparison with HOMER Pro simulations confirms the superior economic performance of the MOPSO-based configuration. Sensitivity analyses underscore the critical role of forecast accuracy in HRES performance, while environmental assessments show an 80\&nbsp;\% reduction in CO2 emissions compared to diesel-only systems. The integrated forecasting module serves as a valuable decision-support tool for rural electrification, particularly in resource-constrained and climate-challenged regions.},
	url = {https://www.sciencedirect.com/science/article/abs/pii/S036054422503110X},
	author = {Hassen Bouzgou and Hadda Zereg}
}
