In this research study, the design and performance evaluation of grid-tied photovoltaic systems has been carried out through experimentation, HelioScope simulation, and black-box machine learning methods for data-driven artificial intelligence system performance assessment and validation. The proposed systems are based on 15 kWp of monoperk and polyperk, which are separately installed in the industrial sector of Faisalabad, Pakistan. The experimental evaluation of the installed PV modules was performed from November 2020 to August 2021. The performance of the PV modules was evaluated by determining the annual average daily final yield (If), performance ratio (PR), and capacity factor (CF). The study showed that the annual average of daily final yield, performance ratio, and capacity factor for 15 kW polyperk was estimated to be 61.94 kWh, 84.17%, and 19.12, respectively. The annual average of daily final yield, performance ratio, and capacity factor for 15 kW monoperk was estimated to be 58.32 kWh, 81.42%, and 18.13, respectively. A comparison of final yield is obtained from simulation and real-time systems obtained from polyperk PV and monoperk. A significant mean error exists between the experimentation and simulation results which lie within the range of 1250 to 1470 kWh and 1600 to 1950 kWh, respectively. Substantial differences between both aforementioned results were initially tested and highlighted by statistical values; i.e., the standard error lies in-between 5 and 45% in polyperk crystalline and 5 and 25% in monocrystalline PV grid-connected module. Machine learning logistical regression evaluated that monoperk crystalline grid-connected system, experimental work was found to be more reliable with error difference reduces in off-peak months as compared to corresponding simulation study and vice versa for polyperk crystalline grid-connected system. Model accuracy after training and testing produced resulted up to 99.5% accuracy for either grid-connected experimentation or simulation outcomes with validation.

A novel solution for optimized energy management comprising a microgrid system for industries in Pakistan is proposed. The proposed study considered microgrids based on photovoltaics, wind turbines, power storage systems, and dual-fuel (DF) generators as backup. A heuristic methodology with a cuckoo search algorithm (CSA) is presented for efficient power trading by scheduling machines. The study was conducted to prove that CSA is adaptable and flexible for self-governing choices for the efficient management and scheduling of machines and power trade between the microgrid and commercial grid. A mixed integer linear programming algorithm is introduced to optimize the system design problems that control decision making for the ideal operation management. A real-time pricing scheme is utilized for electricity price figures. The simulation results show the efficient performance of the proposed scheme to maximize profitability, reduction in electricity cost, and peak to average ratio. Furthermore, the proposed optimization technique was compared with a highly in-use strawberry algorithm to prove the supremacy of the proposed technique. The proposed efficient and robust energy management system was implemented in Shafi Dyeing Industry, Faisalabad, to validate the simulated model.