Quality in healthcare service is essential in giving rural dwellers a good standard of living. It has been established that many rural locations in Sub-Saharan Africa away from the grid connection have difficulty accessing electricity. The inaccessibility of reliable energy and essential medical equipment was the leading barrier to improved healthcare delivery in these rural locations. The deficiency of basic medical equipment to power essential services due to limited or unreliable electricity access has reduced rural healthcare workers’ care capabilities, resulting in higher mortality rates. This paper, therefore, reviews the existing energy solutions for rural healthcare facilities, thereby analysing different approaches and the geographical energy mix and ascertaining the effectiveness of various techniques and energy mix as solutions to effective healthcare delivery in healthcare centres. Hybrid Renewable Energy Sources (HRES) microsystems, like microgrids incorporated with solar panels and battery, is identified to ensure higher and more reliable energy access in rural healthcare centres. At the same time, the adoption of Demand Side Management (DSM) in the HRES deployment in countryside healthcare facilities is reported to decrease the initial cost of installation and improve efficiency. Lastly, in improving energy access, rural electrification planning is achieved through modelling tools related to energy access modelling.

This paper takes a cursory look at the problem of inadequate power supply in the rural healthcare centres of a developing country, specifically Nigeria, and proffers strategies to address this issue through the design of hybrid renewable energy systems combined with the existing unreliable grid in order to meet the healthcare load demand, thus ensuring higher reliability of available energy sources. The simulations, analysis and results presented in this paper are based on meteorological data and the load profiles of six selected locations in Nigeria, using which hybrid grid-connected systems integrating diesel, solar and wind energy sources are designed with configurations to give optimum output. The optimised design configurations in the considered case study, Ejioku, Okuru-Ama, Damare-Polo, Agbalaenyi, Kadassaka and Doso, produce very low energy costs of of 0.0791 $/kWh, 0.115 $/kWh, 0.0874 $/kWh, 0.0754 $/kWh, 0.0667 $/kWh and 0.0588 $/kWh, respectively, leveraging solar and wind energy sources which make higher percentage contributions at all sites. The load-following-dispatch strategy is adopted at all sites, ensuring that at every point in time, there is sufficient power to meet the needs of the healthcare centres. Further works on this topic could consider other strategies to optimise general energy usage on the demand side.

Rising energy consumption, driven by industrialisation and urbanisation, contributes significantly to climate change and household economic burdens. In response, this study developed a machine learning model to predict household energy consumption in residential settings. The dataset employed comprises timestamps, temperature, humidity, and weather data. Prior to model training, extensive exploratory data analysis, preprocessing, and feature engineering were conducted to maintain data quality and enhance model performance. After comparing different statistical models, including RandomForestRegressor, ExtraTreesRegressor, SupportVectorRegressor, and XGBRegressor algorithms, ExtraTreesRegressor emerged as the optimal model, with an R² score of 0.7441 and a MAPE of 16.27%. The lower MAPE and the higher R² score indicate the superiority of the ExtraTreeRegressor over other algorithms. While energy consumption is characterised by high variance, our optimised model effectively interprets interactions between input features and predicts the equivalent energy consumed with a lower RMSE of 11.75. This optimised model was integrated into a web application with an interactive user interface. The application programming interface (API) enabled users to make informed decisions about energy consumption, leading to potential energy savings and reduced environmental impact. The feature importance examined for the prediction process of the model revealed the pivotal role of the hour feature in the energy consumption prediction process. Hence, the time of the day defines various occupancy behaviours that could affect energy consumption.

Healthcare facilities depend heavily on regular and sustainable energy for unwavering operation and satisfactory service delivery. However, a significant challenge for healthcare center is the low financial allocation, which affects the provision of sustainable and cost-effective energy systems. To address this issue, this paper establishes the technical viability and economic benefits of combined heat and power (CHP) systems for healthcare facilities. Hybrid renewable resources combined with natural gas are used as energy sources for the proposed system. Several heat recovery ratios (HRR) of 20%, 40%, 60%, and 80% were analyzed using the HOMER Pro simulation software to determine the optimal HRR for economical energy production with minimal climatic impact. The result generated from the simulation identifies the first two optimization cases as having the best net present cost (NPC) and cost of energy (CoE). At 20% HRR, Case 1 (Photovoltaic/Wind Turbine/Natural Gas Generator/Battery) shows an NPC of $233,986 with a CoE of $0.0762, while Case 2 (PV/WT/Batt) has an NPC of $287,799 with a CoE of $0.107. In comparison, the base case system, which is a natural gas plant, shows an NPC of $1,390,000 and a CoE of $0.741. The sensitivity analysis highlights important considerations for designing CHP systems for industrial or domestic use. A low HRR provides the lowest NPC due to reduced generator running costs and less excess energy. This research demonstrates the potential value of hybrid renewable energy resources, combined with a low-carbon generator (natural gas plant), in meeting the electrical and thermal energy demands necessary for the smooth operation of primary healthcare facilities.