Remote agricultural regions in desert areas, such as Ghardaïa in southern Algeria, face significant challenges in energy supply due to their isolated locations and harsh climatic conditions. Harnessing solar energy through photovoltaic (PV) systems offers a sustainable solution to these energy needs. This study aims to identify suitable areas for PV power installations in Ghardaïa, utilizing a geographic information system (GIS) combined with the fuzzy analytical hierarchy process (AHP). Various environmental, economic, and technical factors, such as solar radiation, land use, and proximity to infrastructure, are incorporated into the analysis to create a multi-criteria decision-making framework. The integration of fuzzy logic into AHP enables a more flexible evaluation of these factors. The results revealed the presence of ideal locations for installing photovoltaic stations, with 346,673.30 hectares identified as highly suitable, 977,606.84 hectares as very suitable, and 937,385.97 hectares as suitable. These areas are characterized by high levels of solar radiation and suitable infrastructure availability, contributing to reduced implementation costs and facilitating logistical operations. Additionally, the proximity of these locations to agricultural areas enhances the efficiency of electricity delivery to farmers. The study emphasizes the need for well-considered strategic planning to achieve sustainable development in remote rural areas.

Renewable energy resources are increasingly important for sustainable power generation, but accurately forecasting their production remains challenging due to non-stationary data and fluctuating environmental conditions. This study presents a novel hybrid model combining Variational Mode Decomposition with advanced forecasting techniques to improve the precision and reliability of photovoltaic power predictions. By evaluating data from two grid-connected photovoltaic power plants in Algeria, the model demonstrates significant improvements over traditional methods, including Long Short-Term Memory, 1D-Convolutional Neural Network, and Gated Recurrent Unit. Key results show a substantial reduction in Root Mean Square Error by up to 89.39%, highlighting the effectiveness of the proposed approach.

Transient laminar mixed convection of air (Pr = 0.71) in a vertical plane channel is numerically investigated for opposing buoyancy at Re=100. The channel walls include symmetric, discrete, isothermal heated sections applied on the external side of a central wall zone, resulting in a conjugate heat-transfer problem through the solid wall and the fluid domain. The two-dimensional unsteady Navier–Stokes and energy equations are solved using a finite-volume method on a staggered grid with SIMPLE pressure–velocity coupling. The Grashof number is varied over 10 ³ ≤ Gr ≤ 10 ⁵ . Instability onset is identified using a quantitative criterion based on the growth and persistence of velocity/temperature fluctuations and a domain-integrated fluctuation energy. The effects of wall thickness Δ and heated length L _h on the critical Grashof number are reported. Increasing L _h lowers the instability threshold, whereas increasing Δ stabilizes the flow by damping thermal gradients transmitted to the fluid. The results provide a stability map and physical interpretation of the transition from steady to unsteady mixed convection at low Reynolds number.