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Rural electrification is a crucial component of meeting sustainable development goals. In Pakistan, load shedding and power outages are frequent. According to the current data, Pakistan is experiencing a shortfall of power between 6000 and 6500 MW. Microgrid technology has the potential to provide a solution to this problem in an efficient and low‐cost manner. This paper proposes the development of a hybrid microgrid system (HMGS) for rural communities. For that purpose, a technological analysis of the HMGS system for rural electrification is performed. Solar photovoltaic (PV) and wind resource‐based renewable energy systems are considered in this work for the electrification of rural areas of Pakistan. A hybrid PV/wind system is designed using MATLAB software. Simulation results show that a 230‐V sinusoidal output voltage has been produced by the proposed model. The advantage of this model is that it minimizes the impact of transients and provides a sinusoidal output waveform. [ABSTRACT FROM AUTHOR]

Accurately predicting fuel blends' lower heating values (LHV) is crucial for optimizing a power plant. In this paper, we employ multiple artificial intelligence (AI) and machine learning‐based models for predicting the LHV of various fuel blends. Coal of two different ranks and two types of biomass is used in this study. One was the South African imported bituminous coal, and the other was lignite thar coal extracted from the Thar Coal Block‐2 mine by Sind Engro Coal Mining Company, Pakistan. Two types of biomass, that is, sugarcane bagasse and rice husk, were obtained locally from a sugar mill and rice mill located in the vicinity of Sahiwal, Punjab. Bituminous coal mixture with other coal types and both types of biomass are used with 10%, 20%, 30%, 40%, and 50% weight fractions, respectively. The calculation and model development procedure resulted in 91 different AI‐based models. The best is the Ridge Regressor, a high‐level end‐to‐end approach for fitting the model. The model can predict the LHV of the bituminous coal with lignite and biomass under a vast share of fuel blends. [ABSTRACT FROM AUTHOR]