Access to uninterrupted power is not a luxury but a basic need. Rural communities living far from the national grid, particularly those in the southern region of Pakistan’s Khyber Pakhtunkhwa province, have limited access to a reliable power supply. In order to provide sustainable electricity, small-scale off-grid renewable energy systems are increasingly used for rural electrification. These systems are commonly known as stand-alone home systems or community micro-grids. This paper proposes an off-grid solar–biogas micro-grid for rural communities in the Lakki Marwat district of Khyber Pakhtunkhwa, Pakistan. The area is mainly dependent upon income from the agricultural and livestock sectors. HOMER was used to simulate the electric power system, while RET-Screen was used to analyze the economics of the system. The optimized system’s results demonstrate that the most economically and technically possible system, which produces 515 kWh and 338.50 m³ biogas daily, is made up of a 30-kW photovoltaic system coupled with a 37-kW biomass hybrid system, a 64-kWh battery storage capacity, and a 20-kW invertor. The system will meet the cooking and power needs of 900 individuals who reside in 100 homes. In addition to household users, the system will provide fixed-priced electricity to productive buildings, and free electricity to community buildings. The system will generate 1300 kg of organic fertilizer each day, which will be sold to local farmers for 50% less than what it would cost on the open market. The proposed approach is techno-economically viable based on the payback period and internal rate of return.

Solar irradiation is the most critical parameter to consider when designing solar energy systems. The high cost and difficulty of measuring solar irradiation makes it impractical in every location. This study’s primary objective was to develop an artificial neural network (ANN) model for global horizontal irradiation (GHI) prediction using satellite data inputs. Three types of ANN, namely, the feed forward neural network (FFNN), cascaded forward neural network (CFNN), and Elman neural network (EMNN), were tested. The findings revealed that altitude, relative humidity, and satellite GHI are the most effective parameters, as they are present in all the best-performing models. The best model for daily GHI prediction was FFNN, which decreased daily MAPE, RMSE, and MBE by 25.4%, 0.11 kWh/${\mathrm{m}}^2/\mathrm{d}$, and 0.01 kWh/${\mathrm{m}}^2/\mathrm{d}$. The FFNN daily MAPE, RMSE, and MBE values were 7.83%, 0.49 kWh/${\mathrm{m}}^2/\mathrm{d}$, and 0.01 kWh/m²/d. The EMNN performed best for monthly and annual prediction, reducing monthly MAPE, RMSE, and MBE by 50.62%, 0.13 kWh/${\mathrm{m}}^2/\mathrm{d}$, and 0.13 kWh/${\mathrm{m}}^2/\mathrm{d}$, while the reduction for yearly was 91.6%, 0.11 kWh/${\mathrm{m}}^2/\mathrm{d}$, 0.2 kWh/${\mathrm{m}}^2/\mathrm{d}$. The EMNN monthly MAPE, RMSE, and MBE values were 3.36%, 0.16 kWh/${\mathrm{m}}^2/\mathrm{d}$, and 0.16 kWh/${\mathrm{m}}^2/\mathrm{d}$, while the yearly values were 0.47%, 0.18 kWh/${\mathrm{m}}^2/\mathrm{d}$, and 0.004 kWh/${\mathrm{m}}^2/\mathrm{d}.$

Performance Analysis of a Novel Photovoltaic Thermal PVT Double Pass Solar Air Heater with Cylindrical PCM Capsules using CFD