<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Hassen Bouzgou</style></author><author><style face="normal" font="default" size="100%">Hadda Zereg</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Forecast-integrated techno-economic optimization of off-grid hybrid renewable system in hyper-arid regions: Application to Tamanrasset, Algeria</style></title><secondary-title><style face="normal" font="default" size="100%">Energy</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2025</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://www.sciencedirect.com/science/article/abs/pii/S036054422503110X</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">334C</style></volume><pages><style face="normal" font="default" size="100%">137468</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">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&amp;nbsp;%, and an RF of 98&amp;nbsp;%, indicating a strong balance between cost and reliability. Energy contributions are 47&amp;nbsp;% from solar PV, 35&amp;nbsp;% from batteries, 12&amp;nbsp;% from wind, and 6&amp;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&amp;nbsp;% reduction in CO&lt;sub&gt;2&lt;/sub&gt; 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.</style></abstract></record></records></xml>