This paper presents a feasibility analysis of the technical, environmental, and economic sustainability of an existing mini-grid technology system in Nigeria. The study investigates the cost and other operational parameters of the Gbamu-Gbamu solar–battery–diesel hybrid mini-grid, specifically the 85 kWp solar PV installation in the Ijebu East Local Government area of Ogun state. Situated within the Owo forest in South-West Nigeria, the mini-grid aims to reduce the effects of global warming and promote sustainable technological development in rural communities by increasing energy access through renewable sources. To assess the system’s viability, this research utilized RETScreen Expert software to validate the techno-economic and environmental sustainability of the installed mini-grid solar–PV–battery–diesel system in the region. Climatic data for the study were obtained from the National Aeronautics and Space Administration (NASA). The results demonstrate that the system is economically feasible and environmentally viable, as indicated by the positive net present value (NPV) and an average monthly irradiance of 4.78 kW/h/m². Furthermore, the system achieved a 92.9% reduction in GHG emissions, provided a reasonable payback period of four years, and enabled a yearly electricity export of 203 MWh. These findings highlight the system’s potential to enhance energy access and mitigate climate change.

Increasing the use of renewable energy, particularly photovoltaic (PV) systems, is essential for mitigating climate change. However, the intermittent nature of PV power generation creates challenges in accurately forecasting and managing electricity supply within grid systems. This study proposes a hybrid deep learning (DL) model combining improved harmony search (IHS) optimization, convolutional neural networks (CNNs), and long short‐term memory (LSTM) networks (IHS–CNN–LSTM) for forecasting the 15‐min power output of grid‐connected monocrystalline and polycrystalline PV systems. The model uses 14 input features obtained from numerical weather prediction (NWP) and local measurement data (LMD), with data sourced from the Public Photovoltaic Output Dataset (PVOD) covering June 2018 to June 2019 in Hebei Province, China. Comparative evaluations against genetic algorithm‐based (GA–CNN–LSTM), differential evolution‐based (DE–CNN–LSTM), and conventional CNN–LSTM models showed that the IHS–CNN–LSTM provided superior forecasting accuracy. Specifically, the proposed model achieved reductions in root mean square error (RMSE) of 3.7% for polycrystalline and 1.8% for monocrystalline PV systems, and reductions in mean absolute error (MAE) of 2.6% and 1.2%, respectively, along with high R ² values of 98% and 99%. The results confirm the effectiveness and accuracy of the proposed hybrid approach for PV power output forecasting.

The main reason for this research is to support the Korean government building retrofitting program initiated to evaluate energy usage trends, propose energy-saving technologies, and focus on reducing the energy demand in residential buildings through energy efficiency improvement. This is achieved by assessing the energy saved after introducing a simple retrofitting measure to an existing residential building. The energy savings in the building were realized after collecting relevant data from the house occupants, analyzing electricity bills, and introducing energy conservation measures (ECMS), leading to an improvement in the energy performance of the building. The building envelope saved 2098 kWh and 6307 kWh of energy via the heating and cooling equipment with an initial incremental cost of USD 500, fuel cost savings of USD 306, and a simple payback of 1.6 years. The analysis takes the occupants 2.6 years to recoup the initial cost of USD 2400 invested with an electricity savings of 2144 kWh. Also, 3.3 tons of CO₂ emissions per year were reduced, equivalent to 3.3 people reducing energy use by 20%. Finally, the actual and simulated data are almost the same for the consumption period, with only a slight difference in October and December, given 0.92 as the Pearson Correlation coefficients.

Image restoration plays a vital role in image processing and pattern recognition in producing better recognition accuracy. It was observed that, most of the traditional restoration algorithms were meant for removing a single type of noise and efforts have been made towards the use of partial differential equation model (PDE), to restore different types of images such as super-resolution, bleeding through and deblurring. This research work deployed hybridized shock filter, shock diffusion of higher order PDE to remove degraded images that have been affected by atmospheric turbulence and water smear images. Degraded images of different types were acquired from various sources. The degraded images were subjected to shock filter algorithm, shock diffusion of higher order PDE. The shock filter was combined with PDE, likewise the shock diffusion was also combined with PDE, and the three algorithms were also combined to test the efficiency of the hybridized method on different degraded images. The result was validated using Mean Square Error, Structural Similarity Index Measure (SSIM), Peak-to-Noise-Signal -Ratio (PNSR) and universal quality index (UQI). From the result, it was observed that the combination of many image restoration algorithms does not give better results whereas, the combination of two of the algorithms has really proved effective.