The major challenge facing renewable energy systems in Nigeria is the lack of appropriate, affordable, and available meteorological stations that can accurately provide present and future trends in weather data and solar PV performance. It is crucial to find a solution to this because information on present and future solar PV performance is important to renewable energy investors so that they can assess the potential of renewable energy systems in various locations across the country. Although Nigerian weather provides favorable weather conditions for clean power generation, there is little penetration of renewable energy systems in the region, since over 95% of the power is fossil-fuel-generated. This is because there has been no detailed report showing the potential of clean power generation systems due to the dysfunctional meteorological stations in the country. This paper sought to fill this knowledge gap by providing a machine-learning-inspired forecasting of environmental weather parameters that can be used by manufacturing companies in evaluating the profitability of siting renewable energy systems in the region. Crucial weather parameters such as daily air temperature, relative humidity, atmospheric pressure, wind speed, and rainfall were obtained from NASA for a period of 19 years (viz. 2004–2022), resulting in the collection of 6664 high-resolution data points. These data were used to build diverse regressive neural networks with varying hyperparameters to find the best network arrangement. In summary, a low mean-squared error of 7 $\times$ 10⁻³ and high regression correlations of 96% were obtained during the training.

In recent times, the significance of renewable energy generation has increased and photovoltaic‐thermoelectric (PV‐TE) technologies have emerged as a promising solution. However, the incorporation of these technologies still faces difficulties in energy storage and optimization. This review paper addresses these challenges by providing a comprehensive overview of the latest advancements in PV‐TE technologies. The paper emphasizes the integration of phase change materials (PCMs) for thermal energy storage, also buttressing the use of encapsulated PCM for thermal storage and efficiency, and the use of hybrid PCM to enhance overall performance. Furthermore, reviews on the use of machine learning techniques for efficient optimization and the integration of thermoelectric modules into tandem perovskite silicon solar cells have been comprehensively analyzed. The advancements in photovoltaic‐thermoelectric systems, as reviewed in this article, signify significant progress in attaining sustainable and effective energy production and storage. This review comprehensively addresses the 4Es, underlining their importance. It not only consolidates recent developments but also charts a path for future research in the field of PV‐TE technologies, offering precise insights to guide upcoming studies and innovations.

This paper explores the feasibility of supplying power to a remote hospital in Vandeikya local government area of Benue state where the facility is strictly run by diesel generator. The average hourly electrical load demand data for the hospital was obtained by measuring electricity consumption from the power house using digital owl meter (CM 199). The annual average insolation for Vandeikya is 4.92 kWh/m²/d obtained from the National Renewable Energy Laboratory database. The simulation was carried out using HOMER Pro (Hybrid Optimization Model for Multiple Energy Resources version 3.13.8). The average hourly electricity load demand data; cost of components and sizes; average annual insolation data, serve as input into the software. The results gave a net present cost (NPC) of ₦718,308,000.00 (Note:$ 1 equals ₦ 386.32 Naira as at 6^th July, 2020) as compared to diesel only with a cost of ₦9,060,974,000.00; the lifetime of the project considered is 25 years and an assumed real interest rate of 10% per annum, while the system offers a payback period of 1 year. The optimal system configuration is the system with diesel genset (300 kW), generic 500 kW biogas genset, generic flat plate PV (300 kW), generic 1kWh lead acid, and system converter (300 kW). The system has a yearly excess electricity of 411,771 kWh/yr which can be sold out and has no unmet load. It greatly reduced greenhouse gas (GHG) emission. The hybrid PV/diesel/biomass gasifier system makes economic sense and is technically viable for Mbaakon hospital and those with similar load and weather conditions.

In the relentless pursuit of sustainable energy solutions, this study pioneers an innovative approach to integrating thermoelectric generators (TEGs) and photovoltaic (PV) modules within hybrid systems. Uniquely, it employs neural networks for an exhaustive analysis of a plethora of parameters, including a diverse spectrum of semiconductor materials, cooling film coefficients, TE leg dimensions, ambient temperature, wind speed, and PV emissivity. Leveraging a rich dataset, the neural network is meticulously trained, revealing intricate interdependencies among parameters and their consequential impact on power generation and the efficiencies of TEG, PV, and integrated PV-TE systems. Notably, the hybrid system witnesses a striking 23.1% augmentation in power output, escalating from 0.26 W to 0.32 W, and a 20% ascent in efficiency, from 14.68% to 17.62%. This groundbreaking research illuminates the transformative potential of integrating TEGs and PV modules and the paramountcy of multifaceted parameter optimization. Moreover, it exemplifies the deployment of machine learning as a powerful tool for enhancing hybrid energy systems. This study, thus, stands as a beacon, heralding a new chapter in sustainable energy research and propelling further innovations in hybrid system design and optimization. Through its novel approach, it contributes indispensably to the arsenal of clean energy solutions.